Regulation of Disintermediated Financial Ecosystems

Research Collection

Doctoral Thesis

Author(s): Elsner, Erasmus

Publication Date: 2020

Permanent Link: https://doi.org/10.3929/ethz-b-000492901

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ETH Library DISS. ETH NO. 26989

REGULATION OF DISINTERMEDIATED FINANCIAL ECOSYSTEMS

A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich)

presented by ERASMUS ELSNER

M.A, University of Zurich

born on 13.08.1985

citizen of Zurich

accepted on the recommendation of

Prof. Stefan Bechtold Prof. Ryan Bubb Prof. Christoph Stadtfeld

2020 Abstract

The overarching theme of this PhD thesis is the analysis of the interplay between regulation and financial intermediation. The research focuses on the analysis of the role played by security laws and industry-specific financial regulation in shaping the allocation through either the firm or the market. Through a law and economics perspective, the thesis tries to answer two related questions in the realm of both equity and credit markets. Firstly, it tries to establish how different regulations impose (implicit or explicit) prices on transactions and thereby either promote a market-based or a firm-based allocation mechanism. Secondly, for the competing allocative regimes, the thesis questions how the presence of both positive and negative externalities of firms and markets may favor a particular allocation mechanism. The PhD project is divided into five separate chapters, with each one examining different aspects of the law and economics of financial intermediation. The first chapter establishes a general law and economics theory, which is applied to concrete asset classes in two chapter pairs, with the first pair (chapters 2 and 3) being dedicated to the analysis of equity intermediation and the second pair (chapters 4 and 5) being dedicated to credit intermediation. Methodologically, this PhD thesis engages both in traditional legal analysis, as well as statistical and economic concepts and theories from the fields of law & economics and network analysis. Chapter 1 (Anatomy of Securities Regulation) develops the ‘Coase Theorem of Securities Regulation’ (the ‘Theorem’), a novel law and economics theory of securities regulation. Under the first part of the theory, the chapter analyses how differential regulatory costs are placed on economic transactions by security laws and firm-specific regulation and how such costs can determine the mode of allocation. In contrast, the core contribution of the second part of the theory is that it re-conceptualizes the costs of the market – and more specifically the costs of securities regulation – as externalities. This allows for an analysis of security laws under the original Coase Theorem and the realization that the role of securities regulation will gradually decrease with a secular fall of transaction costs. Chapter 2 (The Problem of Startup Disclosure Cost) investigates the central role played by regulation – both securities regulation and the alternative exemption regime – in driving and shaping the secular trend of startups staying private longer. This analysis is guided by the Theorem, which is applied in the context of startups. In particular, the chapter explores how (i) securities regulation and the competing exemption regime may price early-stage technology startups out of the financial markets and how (ii) the regulatory costs of securities regulation could be minimized and re-allocated among stakeholders to foster a market allocation. Chapter 3 (Play-to-pay in Silicon Valley venture networks) empirically analyzes the intermediation microstructure of early stage investments in technology startup firms in the Silicon Valley ecosystems. Using a novel dataset compiled for the post-Dotcom era between 2004 and 2014, this chapter conducts a network analysis on Silicon Valley venture network dynamics. The chapter identifies two disparate macro-trends, which have shaped the venture industry at different ends of the funding spectrum, (i) the rise of ‘mega funds’ at the late stage and (ii) the emergence of ‘founder-funder’ VCs at the early stage. Chapter 4 (The Nature of the Banking firm) applies the Theorem in the context of credit and explores how (i) competing securities and banking regulations may price some credit assets out of financial markets and into banking firms and how (ii) the regulatory costs of securities regulation could be re-allocated among stakeholders to foster a market-based credit system. Chapter 5 (Too-distributed-to-Fail Credit Systems) introduces a network-based stress test model for assessing competing theoretical perspectives on banking firms and credit markets through the lens of systemic risk and institutional loss absorption. The introduced network model allows for a simulation of a stylized, too-distributed-to-fail credit market regime, where credit is originated and held in a fully distributed financial architecture with no single-point-of- failure. Systemic resilience levels are compared to a bank-based economy, both in the presence and absence of government intervention. In summary, the thesis provides a balanced perspective on the regulation of disintermediation, highlighting both the benefits and costs of transitioning from a firm-based allocation to a market- based allocation in the context of different asset classes. As a whole, the PhD project draws a colorful picture of diverse equity and credit transactions – placed in competing regulatory regimes – and giving rise to a range of pressing policy considerations. Zusammenfassung Diese Dissertation ist im Spannungsfeld zwischen Regulierung und Finanzintermediation an- gesiedelt. Dabei wird der Forschungsfrage nachgegangen, welche Rolle Wertschriftenregulierung gegenüber alternativer Regulatorien bei der Selektion des dominanten Allokationsmechanismus durch die Firma oder den Markt einnimmt. Anhand einer Law & Economics Perspektive wird im Rahmen dieser Dissertation versucht eine Antwort auf zwei zusammenhängende Fragen zu Aktien- und Kreditmärkten zu finden. Zunächst wird untersucht inwiefern Regulatorien (im- plizite und explizite) Transaktionskosten darstellen und dadurch einen Markt-basierten oder Firmen-basierten Allokationsmechanismus fördern. Für die unterschiedlichen allokativen Regime wird sodann versucht zu ergründen, inwiefern positive und negative Externalitäten einen gewissen Allokationsmechanismus begünstigen. Die Dissertation ist in fünf Kapitel unterteilt, wobei jedes einzelne Kapital einen interdisziplinären Aspekt im Spannungsfeld von Law & Economics und Finanzregulierung beleuchtet. Im ersten Kapitel wird eine allgemeine Law & Economics Theorie entwickelt, welche in den Folgekapiteln auf verschiedene Anlageklassen angewendet wird. Das erste Kapitelpaar (Kapitel 2 und 3) widmet sich Aktienanlagen, während das zweite Kapitelpaar (Kapitel 4 und 5) Kreditanlagen betrifft. Methodisch kommen sowohl traditionelle juristische Methoden, wie auch statistische und ökonomische Konzepte aus den Bereichen Law & Economics und der Netzwerkanalyse zur Anwendung. Kapitel 1 (Anatomy of Securities Regulation) entwickelt das “Coase Theorem of Securities Regulation”, eine neue Law & Economics Theorie zur Wertschriftenregulierung. Der erste Teil der Theorie analysiert inwiefern Wertpapiergesetze und alternative industriespezifische Regu- latorien unterschiedliche regulatorische Kosten auf ökonomisch gleichartige Transaktionen ap- plizieren und dadurch den dominanten Allokationsmodus beeinflussen können. Demgegenüber besteht der Beitrag des zweiten Teils der Theorie darin, dass Marktkosten und insbesondere die Kosten der Wertschriftenregulierung als Externalitäten re-konzeptualisiert werden, wodurch Wertschriftengesetze unter dem etablierten Coase Theorem analysiert werden können. Kapitel 2 (The Problem of Startup Disclosure Cost) untersucht den Beitrag von Regulatorien, sowohl der Wertpapierregulierung und der alternativen Ausnahmen, bezüglich dem säkularen Trend von Wachstumsunternehmen mit einem Börsengang immer länger zuzuwarten. Die Anal- yse basiert dabei auf dem im ersten Kapitel entwickelten Theorem, welches hier auf Startups angewendet wird. Dabei wird analysiert (i) wie Wertschriftenregulierung und alternative Aus- nahmen Startups in der Frühphase aus den öffentlichen Märkten verdrängen können und (ii) wie die regulatorischen Kosten der Wertpapierregulierung minimisiert und zwischen den Stakeholdern re-alloziert werden können um eine Markt-basierte Allokation zu fördern. Kapital 3 (Play-to-pay in Silicon Valley venture networks) führt auf Basis eines neuen Daten- satzes, welcher Risikokapitalinvestitionen im Zeitraum nach dem Platzen der DotCom-Blase zwischen 2004 und 2014 betrifft, eine empirische Netzwerk-Analyse durch. Im Rahmen dieser Analyse werden neuerliche Dynamiken im Silicon Valley analysiert und zwei unterschiedliche Makro- Trends identifiziert, welche Wagniskapitalgeber an unterschiedlichen Enden des Finanzierungs- Spektrums betreffen insb. (i) das Aufkommen von "Mega Fonds" in den späten Finanzierungsrun- den und (ii) das Aufkommen von “Gründer-Finanzierer VC Fonds" im Frühstadium. Kapitel 4 (The Nature of the Banking firm) wendet das “Theorem" auf Kreditmärkte an analysiert den regulatorischen Wettbewerb von Wertschriften- und Bankengesetzen und die Kosten-Reallokation zwischen Stakeholdern zur Förderung eines Markt-basiertes Kreditsystems. Kapitel 5 (Too-distributed-to-Fail Credit Systems) entwickelt ein Netzwerk-basiertes Stresstest- Modell als Basis für eine Vergleichsanalyse zwischen Banken und Kreditmärkten aus der Sys- temrisiko Perspektive. Das Modell ermöglicht die Simulation eines stilisierten “too-distributed- to-fail" Kreditmarktregimes in welchem Kredite in einer vollständig verteilten, Markt-basierten Finanzarchitektur herausgegeben werden ohne konzentrierte systemische Schwachstellen. Dabei wird die Systemresilienz verglichen mit einer Bank-basierten Ökonomie in Szenarien mit und ohne Staatshilfe. Zusammengefasst liefert die Dissertation eine ausgewogene Perspektive auf die Regulierung von Disintermediationsprozessen. Dabei werden sowohl die Vorteile wie auch die Kosten eines Über- gangs von einer Firmen-basierten zu einer Markt-basierten Allokation im Kontext verschiedenere Anlageklassen hervorgehoben. In der Gesamtbetrachtung zeichnet diese Dissertation ein farben- reiches Bild von unterschiedlichen Eigenkapital- und Kredittransaktionen, welche durch konkur- renzierende Regulatorien erfasst werden und Anstoss zu einer Reihe von dringenden finanzpoli- tischen Erwägungen geben. This dissertation is dedicated to the love of my life. Regulation of Disintermediated Financial Ecosystems Essays on the Law and Economics of Financial Intermediation and Innovation

PhD Thesis

Author: Supervisor:

Erasmus Elsner Prof. Stefan Bechtold ETH Zurich ETH Zurich

Co-supervisors:

Prof. Ryan Bubb NYU School of Law

Prof. Christoph Stadtfeld ETH Zurich

July 4, 2021 Contents (abbreviated)

Introduction 9

1 Anatomy of Securities Regulation 13 1.1 Introduction ...... 14 1.2 Disintermediation ...... 15 1.2.1 Levels of disintermediation ...... 16 1.3 Scope of securities regulation ...... 17 1.3.1 Transactions within the scope of securities regulation ...... 17 1.3.2 Transactions outside the scope of security laws ...... 21 1.4 e three functional layers of securities regulation ...... 23 1.4.1 Disclosure and information layer ...... 24 1.4.2 Investment and liquidity layer ...... 25 1.4.3 Diversication layer ...... 27 1.4.4 Summary overview and limitations ...... 28 1.5 e Coase eorem of Securities Regulation ...... 29 1.5.1 Doctrinal Classication ...... 29 1.5.2 Substantive Provisions ...... 29 1.6 First Part of the eorem ...... 29 1.6.1 Substantive provisions ...... 29 1.6.2 Application to the disclosure and information layer ...... 33 1.6.3 Application to the investment and liquidity layer ...... 39 1.6.4 Application to the diversication layer ...... 45 1.7 Second Part of the eorem ...... 52 1.7.1 Doctrinal Classication ...... 52 1.7.2 Substantive provisions ...... 52 1.7.3 Application to the disclosure and information layer ...... 59 1.7.4 Application to the investment and liquidity layer ...... 66 1.7.5 Application to the diversication layer ...... 70 1.8 Application across asset classes ...... 74 1.8.1 Asset classes analyzed within the scope of this PhD thesis ...... 75 1.8.2 Application to other asset classes ...... 75 1.9 Conclusion ...... 76

2 e Problem of Startup Disclosure Cost 77 2.1 Introduction ...... 78 2.1.1 Research process ...... 79 2.2 Allocation mechanism ...... 79 2.2.1 Market allocation ...... 80 2.2.2 Venture rm allocation ...... 81 2.3 Silicon Valley technology startup nancing over time ...... 82 2.3.1 Startup nancing during the Dotcom era ...... 82 2.3.2 Modern startup nancing landscape ...... 84 2.3.3 Dominance of the venture rm allocation and ‘death of the IPO’ ...... 85 2.4 Coase eorem of Securities Regulation ...... 86 2.4.1 e three functional layers ...... 86 2.4.2 First part of the eorem ...... 87 2.4.3 Second part of the eorem ...... 88 2.5 First part of the eorem ...... 89 2.5.1 Disclosure and information layer ...... 89

1 2.5.2 Investment and liquidity layer ...... 100 2.5.3 Diversication layer ...... 108 2.6 Second part of the eorem ...... 113 2.6.1 Disclosure and information layer ...... 114 2.6.2 Investment and liquidity layer ...... 121 2.6.3 Diversication layer ...... 126 2.7 Conclusion ...... 130

3 Play-to-pay in Silicon Valley venture networks 132 3.1 Introduction ...... 133 3.2 Related Literature ...... 133 3.3 Networked venture capital rms ...... 134 3.3.1 Venture capital as capital and information brokers ...... 134 3.3.2 Network dynamics in Silicon Valley’s inner venture circle ...... 134 3.4 Venture capital over time ...... 135 3.4.1 Venture capital in the Dotcom era ...... 136 3.4.2 Modern venture industry ...... 136 3.4.3 Rise of the mega funds ...... 137 3.4.4 Innovation recycling ...... 139 3.5 Network analysis ...... 141 3.5.1 Network analysis methodology ...... 141 3.5.2 Data Collection ...... 141 3.5.3 Graphical Network Representation ...... 142 3.5.4 VC network topology measures ...... 144 3.6 Social capital hypothesis ...... 145 3.6.1 Results ...... 145 3.6.2 Limitations ...... 146 3.7 Dynamic inner circle hypothesis ...... 147 3.7.1 Seed stage networks ...... 148 3.7.2 Venture stage networks ...... 149 3.7.3 Summary of results and limitations ...... 150 3.8 Mega fund hypothesis ...... 150 3.8.1 Results ...... 150 3.9 Play-to-pay networks ...... 151 3.9.1 Small-scale ‘PayPal maa’ network ...... 151 3.9.2 Innovation recycling hypothesis ...... 155 3.10 Conclusion ...... 157

4 e Nature of the Banking rm 159 4.1 Introduction ...... 160 4.2 Bank-based vs. market-based systems ...... 160 4.2.1 Bank-based systems ...... 160 4.2.2 Market-based systems ...... 161 4.2.3 Contribution of this chapter ...... 162 4.3 Coase eorem of Securities Regulation ...... 162 4.3.1 e three functional layers ...... 162 4.3.2 First part of the eorem ...... 163 4.3.3 Second part of the eorem ...... 164 4.4 First part of the eorem ...... 164 4.4.1 Disclosure and information layer ...... 164 4.4.2 Investment and liquidity layer ...... 170 4.4.3 Diversication layer ...... 181 4.5 Second part of the eorem ...... 186 4.5.1 Disclosure and information layer ...... 187 4.5.2 Investment and liquidity layer ...... 196 4.5.3 Diversication layer ...... 203 4.6 Conclusion ...... 208

2 5 Too-distributed-to-fail Credit Systems 211 5.1 Introduction ...... 212 5.2 Bank-based vs. market-based systems ...... 213 5.2.1 Bank-based systems ...... 213 5.2.2 Market-based systems ...... 214 5.3 Systemic risk ...... 214 5.3.1 Bank failures ...... 214 5.3.2 Credit market failures ...... 215 5.4 Regulatory responses ...... 216 5.4.1 Bank regulation ...... 216 5.4.2 Credit market regulation ...... 218 5.5 A distributed systems perspective ...... 219 5.5.1 Distributed systems in computer sciences ...... 219 5.5.2 Distributed systems in the eld of biology ...... 220 5.5.3 Distributed systems perspective in economics and nance ...... 221 5.5.4 Distributed systems perspective in this chapter ...... 221 5.6 Stress test model ...... 223 5.6.1 Related Literature ...... 223 5.6.2 Construction of the nancial system ...... 223 5.6.3 Economic shocks ...... 225 5.6.4 Loss absorption mechanisms ...... 225 5.7 Simulation Parameters ...... 227 5.8 Results ...... 229 5.8.1 Banking-based systems ...... 229 5.8.2 Market-based system ...... 241 5.8.3 Discussion ...... 251 5.8.4 Limitations ...... 252 5.9 Stylized too-distributed-to-fail credit markets ...... 253 5.10 Conclusion ...... 254

Epilogue 255

Bibliography 257

3 Contents

Introduction 9

1 Anatomy of Securities Regulation 13 1.1 Introduction ...... 14 1.2 Disintermediation ...... 15 1.2.1 Levels of disintermediation ...... 16 1.2.1.1 Contractual disintermediation ...... 16 1.2.1.2 Institutional disintermediation ...... 16 1.3 Scope of securities regulation ...... 17 1.3.1 Transactions within the scope of securities regulation ...... 17 1.3.1.1 Scope of securities regulation under U.S. law ...... 17 1.3.1.2 Optimal scope ...... 18 1.3.2 Transactions outside the scope of security laws ...... 21 1.3.2.1 Transactions and parties exempted from security laws ...... 21 1.3.2.2 Transactions governed through industry-specic regulation ...... 22 1.4 e three functional layers of securities regulation ...... 23 1.4.1 Disclosure and information layer ...... 24 1.4.1.1 Denition ...... 24 1.4.1.2 Disclosure and information layer under U.S. security laws ...... 24 1.4.1.3 Market-enabling rms at disclosure and information layer ...... 25 1.4.2 Investment and liquidity layer ...... 25 1.4.2.1 Denition ...... 25 1.4.2.2 Investment and liquidity layer under U.S. security laws ...... 27 1.4.2.3 Market-enabling rms at the investment and liquidity layer ...... 27 1.4.3 Diversication layer ...... 27 1.4.3.1 Denition ...... 27 1.4.3.2 Diversication layer under U.S. security laws ...... 28 1.4.3.3 Market-enabling rms at diversication layer ...... 28 1.4.4 Summary overview and limitations ...... 28 1.5 e Coase eorem of Securities Regulation ...... 29 1.5.1 Doctrinal Classication ...... 29 1.5.2 Substantive Provisions ...... 29 1.6 First Part of the eorem ...... 29 1.6.1 Substantive provisions ...... 29 1.6.1.1 eoretical foundation ...... 29 1.6.1.2 Limitations of the rst part of the eorem ...... 32 1.6.2 Application to the disclosure and information layer ...... 33 1.6.2.1 Costs of the market ...... 33 1.6.2.2 Costs of the rm ...... 37 1.6.2.3 Comparative pricing ...... 38 1.6.3 Application to the investment and liquidity layer ...... 39 1.6.3.1 Costs of the market ...... 39 1.6.3.2 Costs of the rm ...... 42 1.6.3.3 Comparative pricing ...... 44 1.6.4 Application to the diversication layer ...... 45 1.6.4.1 Costs of the market ...... 45 1.6.4.2 Costs of the rm ...... 50 1.6.4.3 Comparative pricing ...... 51 1.7 Second Part of the eorem ...... 52

4 1.7.1 Doctrinal Classication ...... 52 1.7.2 Substantive provisions ...... 52 1.7.2.1 eoretical foundation ...... 52 1.7.2.2 Of positive and negative externalities ...... 53 1.7.2.3 e Coase eorem in the securities regulation seing ...... 54 1.7.2.4 Application and results ...... 57 1.7.2.5 Securities regulation as catalyst and residual cost of markets ...... 58 1.7.3 Application to the disclosure and information layer ...... 59 1.7.3.1 Misinformation externality ...... 59 1.7.3.2 Optimal regime ...... 59 1.7.3.3 Least cost avoider ...... 64 1.7.4 Application to the investment and liquidity layer ...... 66 1.7.4.1 Illiquidity externality ...... 66 1.7.4.2 Optimal regime ...... 67 1.7.4.3 Least cost avoider ...... 69 1.7.5 Application to the diversication layer ...... 70 1.7.5.1 Misallocation externality ...... 70 1.7.5.2 Optimal regime ...... 71 1.7.5.3 Least cost avoider ...... 72 1.8 Application across asset classes ...... 74 1.8.1 Asset classes analyzed within the scope of this PhD thesis ...... 75 1.8.2 Application to other asset classes ...... 75 1.9 Conclusion ...... 76

2 e Problem of Startup Disclosure Cost 77 2.1 Introduction ...... 78 2.1.1 Research process ...... 79 2.2 Allocation mechanism ...... 79 2.2.1 Market allocation ...... 80 2.2.1.1 Market regulation ...... 80 2.2.2 Venture rm allocation ...... 81 2.2.2.1 Venture rm regulation ...... 81 2.3 Silicon Valley technology startup nancing over time ...... 82 2.3.1 Startup nancing during the Dotcom era ...... 82 2.3.2 Modern startup nancing landscape ...... 84 2.3.3 Dominance of the venture rm allocation and ‘death of the IPO’ ...... 85 2.4 Coase eorem of Securities Regulation ...... 86 2.4.1 e three functional layers ...... 86 2.4.1.1 Disclosure and information layer ...... 87 2.4.1.2 Investment and liquidity layer ...... 87 2.4.1.3 Diversication layer ...... 87 2.4.2 First part of the eorem ...... 87 2.4.3 Second part of the eorem ...... 88 2.5 First part of the eorem ...... 89 2.5.1 Disclosure and information layer ...... 89 2.5.1.1 Costs of the market ...... 89 2.5.1.2 Costs of the rm ...... 94 2.5.1.3 Comparative pricing ...... 99 2.5.2 Investment and liquidity layer ...... 100 2.5.2.1 Costs of the market ...... 100 2.5.2.2 Costs of the rm ...... 104 2.5.2.3 Comparative pricing ...... 108 2.5.3 Diversication layer ...... 108 2.5.3.1 Costs of the market ...... 109 2.5.3.2 Costs of the rm allocation ...... 111 2.5.3.3 Comparative pricing ...... 113 2.6 Second part of the eorem ...... 113 2.6.1 Disclosure and information layer ...... 114 2.6.1.1 Misinformation externality ...... 114 2.6.1.2 Optimal regime ...... 114 2.6.1.3 Optimal regime: ground truth data layer ...... 115

5 2.6.1.4 Optimal regime: data processing and analysis layer ...... 119 2.6.1.5 Least cost avoider ...... 120 2.6.2 Investment and liquidity layer ...... 121 2.6.2.1 Illiquidity externality ...... 121 2.6.2.2 Optimal regime ...... 121 2.6.2.3 Least cost avoider ...... 125 2.6.3 Diversication layer ...... 126 2.6.3.1 Misallocation externality ...... 126 2.6.3.2 Optimal regime ...... 126 2.6.3.3 Least cost avoider ...... 129 2.7 Conclusion ...... 130

3 Play-to-pay in Silicon Valley venture networks 132 3.1 Introduction ...... 133 3.2 Related Literature ...... 133 3.3 Networked venture capital rms ...... 134 3.3.1 Venture capital as capital and information brokers ...... 134 3.3.2 Network dynamics in Silicon Valley’s inner venture circle ...... 134 3.4 Venture capital over time ...... 135 3.4.1 Venture capital in the Dotcom era ...... 136 3.4.2 Modern venture industry ...... 136 3.4.3 Rise of the mega funds ...... 137 3.4.3.1 Startups staying private longer ...... 138 3.4.3.2 Rich-get-richer network dynamics ...... 138 3.4.4 Innovation recycling ...... 139 3.4.4.1 Emergence of the founder-funder class ...... 139 3.4.4.2 Founder-friendly environment ...... 139 3.4.4.3 Play-to-pay in founder-led VC rms ...... 140 3.5 Network analysis ...... 141 3.5.1 Network analysis methodology ...... 141 3.5.2 Data Collection ...... 141 3.5.2.1 VC network construction ...... 141 3.5.3 Graphical Network Representation ...... 142 3.5.4 VC network topology measures ...... 144 3.5.4.1 Degree centrality ...... 144 3.5.4.2 Betweenness centrality ...... 144 3.6 Social capital hypothesis ...... 145 3.6.1 Results ...... 145 3.6.2 Limitations ...... 146 3.7 Dynamic inner circle hypothesis ...... 147 3.7.1 Seed stage networks ...... 148 3.7.2 Venture stage networks ...... 149 3.7.3 Summary of results and limitations ...... 150 3.8 Mega fund hypothesis ...... 150 3.8.1 Results ...... 150 3.9 Play-to-pay networks ...... 151 3.9.1 Small-scale ‘PayPal maa’ network ...... 151 3.9.1.1 Outsized returns in the PayPal maa founder-funder network ...... 152 3.9.1.2 Network-level dynamics ...... 154 3.9.2 Innovation recycling hypothesis ...... 155 3.10 Conclusion ...... 157

6 4.3.1.2 Investment and liquidity layer ...... 163 4.3.1.3 Diversication layer ...... 163 4.3.2 First part of the eorem ...... 163 4.3.3 Second part of the eorem ...... 164 4.4 First part of the eorem ...... 164 4.4.1 Disclosure and information layer ...... 164 4.4.1.1 Costs of the market ...... 165 4.4.1.2 Costs of the banking rm ...... 167 4.4.2 Investment and liquidity layer ...... 170 4.4.2.1 Costs of the market ...... 170 4.4.2.2 Costs of the market: primary market structure ...... 170 4.4.2.3 Costs of the market: primary market regulation ...... 173 4.4.2.4 Costs of the market: secondary market ...... 176 4.4.2.5 Costs of the banking rm ...... 177 4.4.3 Diversication layer ...... 181 4.4.3.1 Costs of the market ...... 181 4.4.3.2 Costs of the banking rm ...... 184 4.4.3.3 Comparative pricing ...... 186 4.5 Second part of the eorem ...... 186 4.5.1 Disclosure and information layer ...... 187 4.5.1.1 Misinformation externality ...... 187 4.5.1.2 Optimal regime ...... 187 4.5.1.3 Optimal regime: ground truth data layer ...... 188 4.5.1.4 Optimal regime: data processing and analysis layer ...... 192 4.5.1.5 Least cost avoider ...... 194 4.5.1.6 Ground truth data ...... 194 4.5.1.7 Data processing and analysis ...... 195 4.5.2 Investment and liquidity layer ...... 196 4.5.2.1 Illiquidity externality ...... 196 4.5.2.2 Optimal regime ...... 197 4.5.2.3 Optimal regime: Primary markets ...... 197 4.5.2.4 Optimal regime: Secondary markets ...... 201 4.5.2.5 Least cost avoider ...... 202 4.5.3 Diversication layer ...... 203 4.5.3.1 Misallocation externality ...... 203 4.5.3.2 Optimal regime ...... 204 4.5.3.3 Least cost avoider ...... 207 4.6 Conclusion ...... 208

5 Too-distributed-to-fail Credit Systems 211 5.1 Introduction ...... 212 5.2 Bank-based vs. market-based systems ...... 213 5.2.1 Bank-based systems ...... 213 5.2.2 Market-based systems ...... 214 5.3 Systemic risk ...... 214 5.3.1 Bank failures ...... 214 5.3.2 Credit market failures ...... 215 5.4 Regulatory responses ...... 216 5.4.1 Bank regulation ...... 216 5.4.1.1 Ex-ante regulatory tools ...... 217 5.4.1.2 Ex-post regulatory tools ...... 217 5.4.2 Credit market regulation ...... 218 5.5 A distributed systems perspective ...... 219 5.5.1 Distributed systems in computer sciences ...... 219 5.5.2 Distributed systems in the eld of biology ...... 220 5.5.3 Distributed systems perspective in economics and nance ...... 221 5.5.4 Distributed systems perspective in this chapter ...... 221 5.5.4.1 Load balancing through the banking rm ...... 222 5.5.4.2 Load balancing through the credit markets ...... 222 5.5.4.3 Load balancing through the state ...... 222 5.6 Stress test model ...... 223

7 5.6.1 Related Literature ...... 223 5.6.2 Construction of the nancial system ...... 223 5.6.2.1 Bank-based system ...... 224 5.6.2.2 Market-based credit systems ...... 224 5.6.2.3 Disintermedation level ...... 225 5.6.3 Economic shocks ...... 225 5.6.3.1 Negative shocks: credit defaults ...... 225 5.6.3.2 Positive shocks: no credit default scenario ...... 225 5.6.4 Loss absorption mechanisms ...... 225 5.6.4.1 Private loss absorption mechanism ...... 226 5.6.4.2 Public loss absorption mechanism ...... 226 5.7 Simulation Parameters ...... 227 5.8 Results ...... 229 5.8.1 Banking-based systems ...... 229 5.8.1.1 Positive shock: economic upswing scenario ...... 229 5.8.1.2 Negative shock with private loss absorption ...... 232 5.8.1.3 Negative shock with public loss absorption ...... 234 5.8.1.4 Capital Adequacy Regulation ...... 236 5.8.1.5 Bail-in Regulation ...... 239 5.8.2 Market-based system ...... 241 5.8.2.1 Disintermediated market-based credit system: positive shock ...... 241 5.8.2.2 Disintermediated market-based credit system: negative shock ...... 243 5.8.2.3 Distributed market-based credit system: positive shock ...... 245 5.8.2.4 Distributed market-based credit system: negative shock with private loss absorption . 247 5.8.2.5 Distributed market-based credit system: market bailouts ...... 249 5.8.3 Discussion ...... 251 5.8.3.1 Economic upswing simulations ...... 251 5.8.3.2 Private loss absorption simulations ...... 251 5.8.3.3 Government bailouts simulations ...... 252 5.8.4 Limitations ...... 252 5.9 Stylized too-distributed-to-fail credit markets ...... 253 5.10 Conclusion ...... 254

Epilogue 255

Bibliography 257

8 Introduction

e overarching theme of this PhD thesis is the analysis of the interplay between regulation and nancial intermediation. e research focuses on the analysis of the role played by security laws and industry-specic nancial regulation in shaping the allocation through either the rm or the market. From a law and economics perspective, the thesis tries to answer two related questions in the realm of both equity and credit markets. Firstly, it tries to establish how dierent regulations impose (implicit or explicit) prices on transactions and thereby either promote a market-based or a rm- based allocation mechanism. Secondly, for the competing allocative regimes, the thesis questions how the presence of both positive and negative externalities of rms and markets may favor a particular allocation mechanism. e PhD project is divided into ve separate chapters, with each one examining dierent aspects of the law and economics of nancial intermediation. e rst chapter establishes a general law and economics theory, which is applied to concrete market segments in chapters 2 and 4 of the thesis. e remaining chapters are split into two pairs. e rst pair is dedicated to the analysis of equity intermediation. More specically, it focuses on investments in technology startups by either venture capital rms or the public markets. e second pair is dedicated to credit intermediation. In particular, it looks at the allocation of credit through either credit markets or the banking rm. Of the two pairs, the respective rst chapter can be seen as an application of the law and economics theory developed in the rst chapter to the respective asset class. In contrast, the focus in the second chapter of the respective pair lies on assessing positive and negative externalities and internalities of the respective rm allocation through either empirical analysis or computational simulation. Methodologically, this PhD thesis engages both in traditional legal analysis, as well as statistical and economic concepts and theories from the elds of law and economics and network analysis. is methodological plurality allows it to account for a wider range of paerns of interactions, which can be of interest for legal scholars and nancial economists alike. While the legal analysis focuses largely on the U.S. legal system, in particular on securities and banking regulation, it deals with the law at a conceptual level, thereby extending its potential reach.

eory Chapter

e rst chapter establishes a general theory of securities regulation, which is applicable across multiple asset classes. e aim of the theory is to sharpen our understanding of the role that securities regulation plays in fostering (or deterring) nancial markets. In so doing, nancial market regulation is contrasted with the rm allocation that subjects economic transactions to a competing legal regime. is law and economics perspective on legal rules as competing cost and incentive structures provides a novel framework for assessing both the explicit and implicit costs of securities regulation.

Chapter 1: Anatomy of Securities Regulation

is chapter develops the ‘Coase eorem of Securities Regulation’, a novel law and economics theory of securities regulation. Based on the foundational work of Ronald Coase and Guido Calabresi, it oers a novel perspective on securities regulation, which allows us to have a beer understanding of the role of security laws on the formation of rms and markets. Under the rst part of the theory, the chapter analyses how dierential regulatory costs are placed on economic transactions by security laws and rm-specic regulation and how such costs can determine the mode of allocation. Based on Coase’s paper ‘e Nature of the Firm’, the theory draws aention to the fact that security

9 laws can price certain transactions out of the market and into a rm allocation. Under the rst part of the theory, regulatory costs are viewed as endogenous transaction cots. In contrast, the core contribution of the second part of the theory is that it re-conceptualizes the costs of the market – and more specically the costs of securities regulation – as externalities. is allows for an analysis of security laws under the original Coase eorem and the realization that the role of securities regulation will gradually decrease with a secular fall of transaction costs. In the absence of a frictionless environment, the theory posits that – following the normative work of Calabresi – security laws should minimize the costs of the market and assign the costs to the least cost avoider. While the theory is developed in the context of U.S. federal securities regulation, it is not specic to a particular legal system and applicable across a wide range of asset classes: in chapter 2 of this PhD thesis, the theory is applied in the sphere of technology startups and venture capital rms, whereas chapter 4 applies the theory to the allocation of debt through banking rms and credit markets. e overall aim of the theory is to sharpen our recognition of the implicit and explicit costs of security laws and provide a policy tool which can inform a value neutral cost-benet analysis of security laws as it relates to its role in determining the mode of allocation.

Equity Chapter Pair

e rst chapter pair is dedicated to the intermediation of shareholder rights through either the rm or the market. In particular, it looks at equity investments in Silicon Valley technology startups by either venture capital rms or the public markets. e motivation for analyzing this particular segment of the equity market was (i) the gradual transition of technology companies from a market-based to a rm-based allocation over the past decades and (ii) the outsized innovation output of rms emerging from this technology cluster – with unparalleled macroeconomic dimensions, both in the United States and globally.

Chapter 2: e Problem of Startup Disclosure Cost

Prior to the burst of the Dotcom bubble, the dominant allocation of technology startups was through the public markets. In the past decades, technology rms have increasingly opted to stay private for longer stretches of time and evolve and grow in the shadows of securities regulation – nanced predominantly by venture capital rms. is chapter investigates the central role played by regulation – both securities regulation and the alternative exemption regime – in driving and shaping this secular trend. e analysis is guided by a law and economics theory developed in the rst chapter of this PhD thesis, the Coase eorem of Securities Regulation. By applying the theory in the context of startups, the chapter explores (i) how securities regulation and the competing exemption regime may price early-stage technology startups out of the nancial markets and (ii) how the regulatory costs of securities regulation could be minimized and re- allocated among stakeholders to foster a market allocation. In a rst step, the eorem is used to compare and contrast the allocation through the venture capital rm with the public market allocation along the regulatory cost dimension. For certain functional layers, such as the disclosure and information layer, the chapter nds that existing security laws place excessive costs on the market allocation, thereby implicitly promoting an allocation through the venture rm or fund structure. In a second step, the eorem re-conceptualizes the costs of the market as an externality in the Coasian sense. is allows us to identify specic policy levers that could foster and revive capital formation of startup equity through open markets. e chapter proposes a number of practical policy solutions for adjusting security laws in a way that would ‘re-price’ the public markets and thus make public security oerings a more viable alternative to funding provided by the venture rm.

Chapter 3: Play-to-pay in Silicon Valley venture networks

is chapter analyzes the intermediation microstructure of early stage investments in technology startup rms in the Silicon Valley ecosystems. For decades, investments in these technology rms have been dominated by a closely- knit regional and relational network of venture capital (VC) rms. While this network structure has been analyzed across many dimensions at an aggregate level, lile research has so far gone into the network dynamics over time

10 and the underlying factors driving it. Using a novel dataset compiled for the post-Dotcom era between 2004 and 2014, this chapter conducts an empirical network analysis on Silicon Valley venture network dynamics. e main results are consistent with prior studies, which have shown that the network position of VCs signicantly predicts investment success. e chapter further shows that these venture networks are not static over time, as Silicon Valley’s inner circle network adjusts with new venture rms entering and strategically positioning themselves across dierent nancing stages and industry verticals. In particular, the chapter identies two macro-trends over the observation period. Firstly, the rise of ‘mega funds’, venerable venture rms with oen decade-long track records, which have managed to raise multi-billion venture and growth fund oerings in the past decade and typically deploy most capital in the later stages a startup’s life cycle (growth capital stage). Secondly, the emergence of a new generation of venture rms, which are started and led by a new class of ‘founder-funder’ venture capitalists, which has emerged in the post-Dotcom era through ‘innovation recycling’. ese later venture rms typically invest at the earlier Seed to Series A stages and have eectively positioned themselves as founder-friendly rms over the past decades. In summary, this chapter provides a novel perspective on the network structure and dynamics of venture capital networks and its underlying drivers.

Credit Chapter Pair

e second chapter pair is dedicated to the intermediation of creditor rights. In particular, it looks at the allocation of credit through either credit markets or the banking rm. e allocation of structured credit (such as ABS, RMBS or CDOs) on a market basis is widely believed to have been a major cause of the global nancial crisis of 2008. e credit chapter pair unpacks and challenges this widely held notion. In analyzing the dichotomy of rms and markets for this asset class, the chapter pair aims to establish a beer understanding of the mechanics of market-based credit and its role in fostering economic growth, while potentially also contributing to systemic risk.

Chapter 4: e Nature of the Banking rm

Credit allocation in modern nancial systems is dominated by large banking rms that take deposits and originate loans through the banking rm’s balance sheet. In contrast, the allocation of credit through nancial markets is still nascent and underdeveloped. is chapter investigates the central role played by regulation in maintaining the dominance of banking rms in credit and asks how regulatory reforms could foster scalable credit markets that actively compete with banks. e analysis is guided by a law and economics theory developed in the rst chapter of this thesis, the Coase eorem of Securities Regulation (the ‘eorem’). By applying the theory in the context of credit, the chapter explores how (i) competing securities and banking regulations may price some credit assets out of nancial markets and into banking rms and how (ii) the regulatory costs of securities regulation could be re-allocated among stakeholders to foster a market-based credit system. In the rst part of the eorem, the chapter sheds light on the costs of competing regulatory regimes that can govern credit transactions, depending on whether credit is allocated through the rm or the market. In particular, the chapter identies the costs of arranging credit through banking institutions, where transactions are subject to industry-specic banking regulation. is is then compared and contrasted to the costs of transacting through nancial markets, where transactions are governed by securities regulation. e second layer of the eorem dives deeper into the architecture of credit markets. eoretically rooted in the tradition of the original Coase theorem, it re-conceptualizes the costs of the market as an externality that has to be borne by either borrower or creditor. us, in a positive transaction cost environment it tries to imagine an optimal regime where the costs of the market are structurally and legally optimized and assigned to the least cost avoider.

Chapter 5: Too-distributed-to-Fail Credit Systems

A longstanding policy debate revolves around the question of whether we should promote bank-based or market- based nancial systems. is chapter introduces a network-based stress test model for assessing competing theoretical perspectives through the lens of systemic risk and institutional loss absorption. Past nancial crises have brought to the fore the fragility of the existing nancial architecture, in particular of too-big-to-fail banking rms. rough government

11 bailouts of systemically relevant nancial institutions, losses are eectively absorbed by society at large. Recent regulatory responses, such as counter-cyclical risk buers, enhanced capital requirements and bail-in regulations, have been introduced to enhance resilience and discourage excessive risk-taking of banking rms. In a rst instance, the developed network-based model provides an analytical tool for assessing the wealth transfer eects of government bailouts. Given the parsimony of the framework, it further allows to sharpen our understanding of the regulatory measures that were taken in the aermath of the crisis. rough dierent simulations in a stylized one bank economy, the model rstly shows how government bailouts can exacerbate wealth dierentials among agents. Furthermore, it demonstrates how recent regulatory measures remain ‘point solutions’ only, which do not address the core architectural challenges associated with bank-based economies. On the other hand, the allocation of credit through markets is generally believed to be more resilient. Losses are absorbed by creditors instead of banking rms’ balance sheets. e network model allows us to simulate a stylized, too-distributed-to-fail credit market seing, where credit is originated and held in a fully distributed nancial architecture with no single-point-of-failure. Under this regime, in contrast to bank-based economies, nancial institutions only facilitate the origination of loans, without taking any principal risk. e model shows how such a stylized distributed credit market system can promote nancial stability under economic stress scenarios by leaving systemically relevant market operators functional, while spreading credit losses evenly among creditors. However, the model also reveals that once the government intervenes in the credit markets, the positive eects of too-distributed- to-fail credit systems vanish and the economic situation looks very similar to the bank-based allocation.

Summary

e two chapter pairs, on equity and credit, provide an overall balanced perspective on the regulation of disintermediation, highlighting both the benets and costs of transitioning from a rm-based allocation to a market-based allocation in the context of dierent asset classes. On the one hand, through the law and economics theory developed in chapter 1 and its concrete applications in chapters 2 and 4, the thesis shows how securities regulation can implicitly price certain transactions out of the market and into a rm-based allocation. On the other hand, chapter 3 empirically analyzes the potential positive internalities that arise in a rm-based allocation in the sphere of venture capital. In contrast, chapter 5 takes a more discerning look at negative externalities in bank-based and market-based systems and nds that under certain stylized assumptions, both allocation forms may induce similar levels of systemic risks. As a whole, the PhD project draws a colorful picture of diverse equity and credit transactions – placed in competing regulatory regimes – and shedding light on a range of pressing policy topics.

12 Chapter 1

Anatomy of Securities Regulation

13 1.1 Introduction

Securities regulation is a notoriously complex eld of the law with wide ranging nancial implications. Placed at the intersection of administrative and contract law, it determines how investors and issuers of securities can contract through the (public) markets and at the same time exposes market actors to the scrutiny of the securities regulator. Scholarly work in the eld needs to sidestep the ‘widespread, yet misguided assumption that securities regulation aims at protecting investors’1 and instead be based on the core premise that the purpose of securities regulation is to foster open and transparent capital markets.2 Goshen and Parchomovsky(2006) have drawn aention to the fact that surprisingly lile scholarly aention has been devoted to the question of how securities regulation achieves this policy aim.3 is chapter develops a novel framework for analyzing securities regulation, the Coase eorem of Securities Regu- lation (hereinaer the “eorem”), which challenges the regulation of securities at a more fundamental level. Based on established principles of law and economics, in particular Coase’s ‘twin tower’ papers,4 it provides a new perspective on the eects of securities regulation on the formation of rms and markets. As such, it draws into question whether securities regulation does achieve its policy goal, or whether it may actually, as a whole body of regulation, have the opposite eect on transactions and markets. In other words, the chapter asks us to consider, whether the regulatory costs that security laws places on certain economic transactions may price these transactions out of the market and into the rm structure. Doctrinally, the theory developed here is a functional theory of law and economics and as such aims to oer value neutral principles of lawmaking in the area of securities regulation. However, given that it draws heavily on the foundational positive work of Ronald Coase and the normative work of Guido Calabresi,5 it also encapsulates elements of both the Chicago-school and the Yale-school of law and economics. In a rst step, before seing out the theory itself, the chapter introduces three functional layers of securities regulation, which act as the main units of the regulatory analysis. ese three layers follow a hierarchical and sequential logic for a set of activities and functions. At the base layer, referred to as the ‘disclosure and information layer’, the issuer of a security, either required by securities regulation or voluntarily, discloses information to investors. At a second layer, referred to as the ‘investment and liquidity layer’, the newly issued securities are traditionally placed in the public markets by an underwriter and actively traded on a regulated securities exchange. Lastly, at the ‘diversication layer’, investors limit their idiosyncratic risk exposure to a single issuer by diversifying across a wide range of issuers through pooling vehicles, such as mutual funds and pension plans. e theory itself is divided into two separate parts, which try to answer two dierent, yet closely related questions:

• First part of the eorem: e rst part tries to explain why certain transactions are carried out through the rm or the market as a result of regulatory costs, in particular securities regulation and alternative rm-specic regulations.6 In this rst part, securities regulation is treated as an endogenous transaction cost under the market allocation. e rst part requires a positive legal analysis, which assumes that economic actors rationally choose

1See Huber(2016) (‘Contrary to popular belief, protecting investors’ interests is not the primary purpose of the securities laws, and protecting investors’ interests is merely derivative of the primary purpose – protecting the market interest—and protecting the public interest is non-existent.’); Goshen and Parchomovsky(2006) (citing Schlesinger Inv. P’ship v. Fluor Corp., 671 F.2d 739, 743 (2d Cir. 1982) (‘e Williams Act was meant to protect the ordinary investor.’); Feit v. Leasco Data Processing Equip. Corp., 332 F. Supp. 544, 565 (E.D.N.Y. 1971) (‘[P]rospectuses should be intelligible to the average small investor.’). H.R. REP. NO. 73–85, pt. 1 (1933) (legislative history of Securities Acts) (‘e purpose of the legislation […] is to protect the public with the least possible interference to honest business.’); H.R. REP. NO. 73-1383, pt. 2, at 5 (1934) (‘As a complex society so diuses […] the nancial interests of the ordinary citizen that he […] cannot personally watch the managers of all his interests […] it becomes a condition of the very stability of that society that its rules of law […] protect that ordinary citizen’s dependent position.’).). 2See Goshen and Parchomovsky(2006) (‘the essential role of securities regulation is to create a competitive market for sophisticated investors’ ) and J. Gordon and Kornhauser(1985) (‘[T]he law should select rules promoting the eciency of nancial markets relative to the optimal information set.’). 3See Goshen and Parchomovsky(2006) (‘Surprisingly, this pivotal question has never been fully answered’) citing Bushman, Piotroski, and Smith (2004) (‘lile research considers how and why information systems, per se, vary around the world’). 4Coase(1937) [hereinaer ‘e Nature of the Firm’]; Coase(1960) [hereinaer ‘e Problem of Social Cost’]; Schwab(1993) (referring to e Nature of the Firm and e Problem of Social Costs as the ‘Coase’s twin towers’). 5See Calabresi(1970). 6At rst glance, this question may appear very similar to the question raised in Coase’s ‘e Nature of the Firm’. However, while Coase is formulating it as a broader question of rms and markets, the eorem introduced here is narrower in scope and focuses specically on identifying the role of regulation.

14 to allocate a transaction through either the rm or the market, depending on the competing legal regimes and the respective prices. is descriptive analysis is used to identify regulatory ineciencies in particular areas of security laws, which may price economic transactions out of the market allocation.

• Second part of the eorem: e second part goes a step further by making the costs of the market the main unit of analysis and reconceptualizing the costs of the market, including security laws, as externalities. is exogenization allows us to analyze securities regulation under the traditional Coasean seing of ‘e Problem of Social Costs’. Much like Calabresi’s most important contribution can be seen in his restatement of tort law as accident law and his identication of the costs of accidents as the most important normative category, the eorem’s main contribution lies in re-shaping our understanding of securities regulation around its costs. e Coasean lens allows for the key insight that the role of securities regulation will gradually decrease with a secular fall of transaction costs. It further establishes an objective function of optimal securities regulation. In particular, following the normative work of Calabresi, it sets out to minimize the costs of the market and assign the costs to the least cost avoider, either the issuer or the investor.

As a general theory, the eorem is abstract in nature and applicable across many asset classes. But the theory does not lack specicity and can thus be readily applied as a policy tool as part of a broader economic cost-benet analysis. In particular, it provides the theoretical foundation for chapters 2 and 4 of this PhD thesis. In chapter 2, the theory is applied in the sphere of technology startups and venture capital rms, whereas chapter 4 applies the theory to the allocation of debt through banking rms and credit markets. Structurally, this chapter proceeds as follows: section 1.2 of this chapter discusses the phenomenon of disintermediation, the transition of transactions from a rm allocation to a market allocation. Section 1.3 outlines in broad strokes, which types of economic transactions discussed within this chapter are covered by securities laws and which ones fall outside of its scope, either ex ante, as they do not qualify as securities, or ex post, as they are either exempted transactions or are covered by rm or transaction-specic regulation. In section 1.4, the three functional layers are introduced. In sections 1.6 and 1.7, the Coase eorem of Securities Regulation, as the core theoretical contribution of this thesis, is introduced and discussed in detail. While the eorem is introduced at a high level of abstraction, references to the core ndings of chapters 2 and 4 of the PhD thesis are made for illustrative purposes. Finally, section 1.8 sets out to further narrow this divide between the theoretical and the practical, by providing the reader with some outlook on potential applications of the eorem to other asset classes.

1.2 Disintermediation

Over the last decades, both in the realm of nancial markets and commerce in general, the costs of identifying suitable counterparties, agreeing on terms and executing an agreement have eroded signicantly. is development has been driven, in large part, by a secular fall in transaction costs. With the click of a buon, modern technology allows individuals to communicate and transact with others around the world. is begs the question why most shareholder and creditor relationships, as purely nancial transactions, are still largely allocated through nancial rms, rather than on an open market basis. Instead of nancing their neighbour’s mortgage directly, depositors transact with their peers through the banking rm structure. e bank accepts retail deposits and uses deposits to originate loans to the next door neighbour at a higher interest rate. Similarly, rather than making a direct investment in a college student startup, a Berkeley professor may invests his or her retirement savings in this very same startup through a multi-layered rm system, which can involve funneling the savings through the California Public Employees’ Retirement System (CalPERS) pension fund, a major venture fund LP and a Sand Hill Road venture capital rm, before ultimately reaching the startup next door. ese complex and multi-layered rm-based governance structures provide the impetus for the present research. is chapter looks to securities regulation and competing rm-specic regulation for an answer to the question of why many nancial assets are still allocated and intermediated through the rm, rather than a market structure.

15 Within the scope of this chapter, the transition from a rm allocation to a market allocation is referred to as disintermediation. While the process of disintermediation is not the direct object of analysis of this chapter, it can be seen as (i) either a natural consequence of leveling the playing eld between the rm and the market, for which this chapter provides a theoretical foundation, or (ii) as an explicit policy goal of legislators or regulators, who may apply the theory in the future.

1.2.1 Levels of disintermediation

Further precision is required with respect to the level at which this transition takes place. In this chapter, the object of analysis is the economic transaction between:7

• a surplus agent, an investor or creditor; and

• a decit agent, an issuer or a borrower.

Disintermediation is therefore regarded as a transition of such economic transactions from a within rm allocation structure to a direct allocation through the market. is direct allocation requires what can be separated into contractual and institutional disintermediation.

1.2.1.1 Contractual disintermediation

Under the rm regime, there typically exist two separate contracts. On the one hand, there exists a contract between the rm and a surplus agent, an investor or a depositor. On the other hand, there exists a contract between the rm and a decit agent, an issuer or a borrower. For example, where credit is allocated through the banking rm, there exist separate contracts between (i) the bank and its depositors and (ii) the bank and its borrowers. Similarly, in the case of an investment rm, such as a private equity or venture fund, there exist separate contracts between (i) the rm and the rm’s limited partners, its investors, and (ii) the rm and its portfolio companies. Contractual disintermediation is referred to as the transitioning of this multi-sided, rm-based contractual system to a market-based contractual system where surplus agent and decit agent are in a direct contractual relationship. In other words, where the economic risks and returns of the transaction are segregated from the rm and legally placed directly and exclusively between the ultimate economic beneciaries of the transaction.

1.2.1.2 Institutional disintermediation

As outlined above, under the rm allocation, the rm is contractually embedded between the surplus and the decit agent. In a completely frictionless regime, which is used as a theoretical concept in the foundational work of Coase, the rm can be completely replaced by the market. In such an environment, the rm becomes a redundant cost layer that can be fully avoided through the market. Like Coase, the frictionless environment is used as an analytical tool for identifying an objective function for the regulator. At the same time, one must be aware that in a real-world market regime, there exist positive – albeit sharply decreasing – transaction costs. As a result, the economic reality of public markets, which this chapter is aempting to model, is not one without any rms. Instead, as we can observe in the real world, there exist multiple rms enabling nancial markets, which Hertig, Kraakman and Rock refer to as ‘market gatekeepers’.8 is may appear as a contradiction at rst, since a market allocation requires rms to exist and operate. However, while these market-enabling rms are indeed rms, their purpose is to encourage the direct contractual exchange between surplus and decit agents. Judge Richard Posner has summarized the role of these market-enabling rms, as it is understood by Coase, Oliver Williamson, and other institutional economists as follows:9 7See Boot and akor(1997) (using a similar terminology ‘A primary function of the nancial system is to facilitate the transfer of resources from savers (”surplus units”) to those who need funds (”decit units”)’). 8See R. Kraakman et al.(2017); R. H. Kraakman(1986) (‘Gatekeepers, then, might be divided into market gatekeepers, who (like accountants and underwriters) face powerful private incentives to prevent misconduct, and public gatekeepers, who do not.’) 9See Posner(2010); Williamson(1986) (‘[A] guiding principle of comparative institutional study [is] the hypothesis that transactions are assigned to and organized within governance structures in a discriminating (transaction-cost economizing) way.’ ).

16 “[... ] the primary function of the institutions that support the market is to reduce transaction costs.”

Of course, the somewhat paradoxical outcome of this is that while these rms may reduce higher transactions costs that would be encountered in their absence, they also eectively become the residual transaction costs that exist in a market allocation.10 us, institutional disintermediation is dened as a transition from a within rm allocation to a market allocation that is facilitated by market-enabling rms.

1.3 Scope of securities regulation

While securities regulation is oen said to exhibit signs of regulatory overreach, it must be noted that most economic transactions are in fact not governed by securities regulation. is section tries to give an overview of the legal scope of securities regulation and thus, by extension, the scope of the theory developed within this chapter. e legal scope is established in the context of U.S. securities regulation. In addition to outlining which nancial transactions are presently covered under securities regulation, transactions which explicitly fall outside of securities regulation, but are still covered by the regulatory analysis, are also considered.

1.3.1 Transactions within the scope of securities regulation

In this section, the legal scope of securities regulation under U.S. law is established and the notion of the optimal scope of securities regulation is introduced, both of which give rise to much scholarly debate. It should be noted that the theory developed within this chapter fully accepts the current scope of securities regulation. Rather than the scope, the eorem challenges the regulatory design and the costs imposed by the provisions that cover the transactions. While it does not challenge the legal scope of transactions covered by securities regulation, which varies greatly between jurisdictions, it is important to establish the larger context in which the regulatory analysis takes place.

1.3.1.1 Scope of securities regulation under U.S. law

Like all areas of the law, securities law operates through connecting factors, which link specic real-world actions and objects to the legal system. In the case of U.S. securities law, economic transactions are linked to the legal system through the classication of the transaction as a ‘security’. It is noteworthy that, at the basic level, U.S. securities law puts the emphasis on the transaction rather than the rm, which expands the potential scope of the regulation signicantly. When establishing the federal securities regulation, Congress deliberately dened the term security very broadly and in non-economic terms11 in both the Securities Act of 1933 (“1933 Act”)12 and the Securities Exchange Act of 1934 (“1934 Act”).13 Both statutes provide an extensive list of examples and categories of securities in an eort to include the many types of transactions that Congress predicted would or should fall within the legal scope of securities regulation.14 While there are minor dierences between the denitions under the statutes, the U.S. Supreme Court has made it clear that the two statutory denitions are to be treated as the same.15 When determining whether a novel or unique economic transaction should fall under the scope of federal securities laws, such as most recently in the case of digital assets,16 both the Securities and Exchange Commission and the federal

10See Judge(2019) (‘[…] many transaction costs now take the form of fees paid to specialized intermediaries.’). 11is becomes apparent from both the legislative history of Securities Acts and later judicial interpretation thereof. See, for example United Hous. Found. v. Forman, 421 U.S. 837, 847-48 (1975) (quoting H.R. REP. NO. 73-85, at 11 (1933)). (“In providing this denition Congress did not aempt to articulate the relevant economic criteria for distinguishing ”securities” from ”non-securities.” Rather, it sought to dene ”the term ‘security’ in suciently broad and general terms so as to include within that denition the many types of instruments that in our commercial world fall within the ordinary concept of a security.”). Reves v. Ernst & Young, 494 U.S. 56, 61 (1990). (“Congress therefore did not aempt precisely to cabin the scope of the Securities Acts. Rather, it enacted a denition of ‘security’ suciently broad to encompass virtually any instrument that might be sold as an investment.”). 1215 U.S.C. §77b(a)(1). 1315 U.S.C. § 78c(10). 1415 U.S.C. §77b(a)(1) (“e term “security means any note, stock, treasury stock, security future, security-based swap, bond, debenture, evidence of indebtedness, certicate of interest or participation in any prot-sharing agreement [... ] investment contract [... ]”). 15United Hous. Found. v. Forman, 421 U.S. 837, at 847 n.12 (1975) (“[t]he denition of a security in § 3(a)(10) of the 1934 Act … is virtually identical [to the denition in the Securities Act of 1933] and, for present purposes, the coverage of the two Acts may be considered the same.”). 16SEC, Framework for “Investment Contract” Analysis of Digital Assets (Apr. 3, 2019), hps://www.sec.gov/les/dlt-framework.pdf

17 courts most prominently focus on the ‘investment contract’ security. While the investment contract is just one particular form of security under the broad laundry list oered in both statutes, it has developed into the de facto acid test of whether an economic transaction is classied as a security. Given its crucial role in dening the edge cases, the denition of the investment contract requires some closer aention. While both the 1933 Act and the 1934 Act include within their respective denitions the connecting factor investment contract to link the economic transaction to securities law, neither statute denes it. us, when regulators and judges try to assess whether a transaction falls under the scope of the securities regulation, they have to look to judicial decisions. In this respect, a landmark decision rendered by the Supreme Court in 1946, SEC v. WJ Howey Co.,17 involving a Florida citrus grove investments, has emerged as the dominant precedent for craing the denition of investment contracts. In particular, the elements laid out in this decision, which is now commonly known as the Howey test, deem a transaction to constitute an investment contract where the following criteria are met:

• there exists an investment of money;

• there exists an expectation of prots from the investment;

• the investment of money is in a common enterprise;

• any prot comes from the eorts of a promoter or third party.

It is beyond the scope of this chapter to take a more granular look at the individual elements, but it can be noted from the above that these are rather broad categories that can lead to an expansive scope of securities regulation in practice.

1.3.1.2 Optimal scope

e elements of the Howey test and its application to concrete transactions has given much rise to both scholarly and judicial debate. From the standpoint of economic actors, whose economic transactions are being either exempted from or subjected to costly federal securities regulation, the arguably rather obscure test developed in the context of citrus groves more than half a century ago seems far from satisfactory. e sale of most goods and services is not generally considered an investment contract by ordinary citizens and should thus rightly be exempted under the Securities Acts and the Howey test. However, both scholars and practitioners have pointed out edge cases, such as education,18 where the ordinary citizen may see himself as an investor and where, if securities regulation was in fact principally about investor protection, the SEC would have to intervene. In fact, from a general welfare perspective, it is far from clear which transactions should optimally be covered by securities regulation and which ones should fall outside of its scope. Without going into too much detail, the below list of exemplary edge cases demonstrates how much uncertainty and debate there exists at the scope level. Arguably, much of this scholarly debate surrounding the optimal scope in substance concerns the costs of securities regulation. As a result, the scholarly discourse surrounding the qualication as a security provides a substantial motivation for the theory developed within this chapter. In particular, as the second part of the eorem develops a cost-benet tool to minimize and optimally assign such costs, it is well-positioned to move the discourse in a direction that addresses the core cost concern, rather than ‘cloaking’ it behind the scope discourse.

1.3.1.2.1 Securities regulation of pre-sales

Pre-sales are economic transactions where a company has not yet produced any goods or services, nevertheless sells these to end consumers. Conceptually, pre-sales are reminiscent of the situation of many late eighteenth- and early nineteenth- century corporations described by Henry Hansmann and Mariana Pargendler where the ‘the principal shareholders

17328 U.S. 293 (1946). 18See Easterbrook and Fischel(1984). (contrasting the regulation of securities to the regulation of education ‘Fraud in the sale of education is more important to most people of moderate means (the supposed beneciaries of the securities acts) than fraud in the sale of securities; these people have a much greater portion of their wealth invested in human capital than in the stock market. Yet there are no federal laws addressing these other assets.’).

18 were also the rm’s principal customers’.19 However, in contrast to the historic situation described by Hansmann and Pargendler, consumers in pre-sales do not typically receive equity rights. us, such situations expose the consumer to substantial risks of non-performance in the downside scenario without the upside potential of equity rights.20 While the monetary sums involved can be substantial, these transactions are fully exempt from federal securities regulation. us, the scope of securities regulation, which excludes certain transactions from its purview, despite them having similar risk proles to transactions that are included, raises some concerns. While Howey explicitly asks for a ‘substance over form approach’,21 the below examples give some indication that this may not reect the regulatory approach in the sphere of pre-sales. As a result, it has oen been argued that securities regulators may have been too cautious in expanding the scope of securities regulation in the sphere of pre-sales. In this respect, it appears that regulators have been guided mostly by the costs imposed by securities regulation, rather than the legal qualiers. Rewards-based crowdfunding Unlike equity-based crowdfunding, which is regulated by securities regulation under Title III of the JOBS Act,22 rewards-based crowdfunding23 falls completely outside the scope of securities regulation. Firms oen manage to raise in the millions of dollars through online portals like Kickstarter or Indiegogo, promising their ‘backers’ rewards in the form of the products they intend to produce.24 Yet, oen times these rewards never arrive as the startups struggle with manufacturing and execution. In many past cases, these early stage companies have had to declare bankruptcy before they could even send out apology leers to their backers. As a result, backers are oen le with a full loss of their principal. Nevertheless, the SEC does not require the rms oering their non-existing products to comply with the mandatory disclosure obligations under security laws. is is not to say that these arrangements fully escape regulation. In particular, they are subject to the general consumer protection laws typically addressed by the federal trade commission (FTC) and state consumer protection laws. While the FTC has taken action in a number of reward-based crowdfunding oerings, the regulatory oversight of the FTC imposes ex-post costs on rms conducting crowdfunding oerings. is is in contrast to securities laws, which front-load compliance costs. Hyperfunding In his recent law review article,25 Oranburg draws aention to the fact that the practice of pre-sales has since moved from the – in relative terms – small Kickstarter crowdfunding campaigns, to multi-million dollar pre-sales of established public companies. As his leading example, he points to the pre-sale of the Tesla model 3,26 which allowed the company to raise north of 200 million dollars in interest-free funding without the substantial disclosure requirements typically encountered if these pre-sales were treated as investment contracts under security laws. Similarly, in the context of Tesla’s autopilot feature, the rm has been described as turning its costumers into investors.27 While the SEC has famously taken action against that rm’s chairman and former CEO, Elon Musk, for his tweets about taking the company private,28 the large-scale hyperfunding scheme has fully escaped the Commission’s aention. Digital assets In recent years, a large number of early stage startup rms and projects have engaged in the sale of their own digital currencies, commonly referred to as ‘Initial Coin Oerings’ (ICOs). Without oering a real product or service at the

19See Hansmann and Pargendler(2014) (‘local merchants and farmers were apparently the most eective source of capital’). 20See Wroldsen(2017) (reporting on the lost equity upside of the crowdfunding backers of Oculus Ri ‘e situation was especially painful for Kickstarter backers when estimates were released suggesting that if the original Kickstarter backers had received stock, instead of or in addition to rewards, the backers would have realized between a 145x to 200x return on their investment, in less than two years.’). 21SEC v. W.J. Howey Co., 328 U.S. 293 (1946), at 298 (“form [is] disregarded for substance and the emphasis [is] on economic reality.”). 2215 U.S.C. § 77d(a)(6). 23See Armour and Enriques(2018) (detailing the dierence between rewards-based crowdfunding and equity-based crowdfunding). 24See Bradford(2012) (‘Kickstarter requires its projects to oer what it calls ‘rewards,’ typically of the pre-purchase variety.’). 25See Oranburg(2018). 26See Oranburg(2018) (‘Tesla paved the way for an electric vehicle revolution by preselling hundreds of thousands of its Model 3 EV direct to consumers. Unwary consumers may not have realized that they were underwriting Tesla’s bold strategy to transform multiple product markets. Risks were not disclosed. Rewards proved illusory. Investors would have been entitled to disclosures and colorable claims of fraud when Tesla missed milestones and deadlines. But consumers can only get their $1000 deposit back, without interest, if Tesla has the nancial and reputational capital to refund consumers.’). 27See CNBC(2020) (Juheng Li of Warren Capital [at minute 38:21] on Elon Musk’s ability to pre-sell ‘e complete version of autopilot is already in the market for sale, even though the features are not available. He’s [Elon Musk] is great at turning consumer into investors.’). 28Complaint, SEC v. Elon Musk, (S.D.N.Y. 2018) (No. 1:18-cv-8865).

19 point of sale, these projects have largely escaped from the scope of securities regulation, by highlighting the ‘utility’ of these currencies or tokens.29 In essence, the initiators of these projects made the argument that these digital assets should be treated as pre-sales. Notably, the monetary sums involved in these ICOs were non-trivial, with the total market capitalization of cryptocurrencies reaching almost a trillion dollars at peak. e SEC has since taken notice30 of this industry-wide aempt to avoid securities regulation by re-classifying equity-like investments as ‘pre-sales’. As a result, the Commission has since produced a legally non-binding ‘framework’,31 which claries how the obscure Howey test is to be applied in the sphere of ICOs. Yet, this much-awaited bright line test has arrived much too late in the eyes of many – years aer the booms and busts of the burgeoning industry have already materialized. is digital currency example once again shows how the critical legal discussion in the eld of securities regulation oen revolves around the optimal scope of security laws, instead of directly addressing the costs of securities regulation, which is the underlying cause why many issuers aempt to circumvent SEC regulation in the rst place.

1.3.1.2.2 Securities regulation of peer-to-peer loans

In his law review article with the unequivocal title ‘e misregulation of Person-to-Person Lending’,32 Andrew Verstein analyzes the SEC’s application of security laws in the edge case of market-based lending. He makes the normative argument that the SEC overreached its competence by armatively asserting that P2P loans should be classied as securities under Howey. e SEC’s decision to place P2P lending transactions under the scope of securities regulation had interesting second- order eects from a rm-vs-market perspective. e small-lot consumer loan transactions, which were initially allocated through an unregulated market structure that enabled a direct contracting between borrowers and lenders,33 suddenly became subject to the SEC’s mandatory disclosure obligations. is meant that each individual P2P lending transaction now required a costly prospectus and that, if the existing unregulated market structure was to be maintained, each individual borrower would eectively have to become an issuer. Given the unreasonable costs of such a system, it was instead decided that the P2P platforms would act as issuers of securities and would ‘shelf register’ multiple securities in advance of the actual transactions.34 Legally speaking, this means that P2P platform creditors now have the platform operators as their contracting counterparties, rather than the ultimate borrower.35 In addition to defying the essence of P2P lending, this introduces a further platform credit risk and thus eectively re-transitions these loans into a rm-based allocation structure.36 Instead of enabling markets, securities regulation thus had the opposite eect of strengthening the role of intermediary rms. While Verstein challenges the qualication of P2P loans under Howey, one can make the argument that this maer is principally not a question of scope, but rather of the compliance costs imposed by security laws. e P2P example is thus another example, which clearly demonstrates how the scholarly discourse has resorted to arguing along the lines of the optimal scope of securities regulation, whereas the more fundamental problems are actually to be found in the scale of costs and the assignment of these costs.

29See Mendelson(2019)( “e company proceeded with their ICO, asserting their token to be a “utility” and not a security. No further action appears to have been taken as of the date of this article.”). 30See Mendelson(2019) (citing SEC Chairman Clayton ‘Merely calling a token a “utility” token or structuring it to provide some utility does not prevent the token from being a security. Tokens and oerings that incorporate features and marketing eorts that emphasize the potential for prots based on the entrepreneurial or managerial eorts of others continue to contain the hall- marks of a security under U.S. law.’). 31See SEC(2019a). 32See Verstein(2011). 33See Verstein(2011) (‘In the early days of Prosper and Lending Club, the platforms made loans to borrowers and assigned those notes to lender- investors.199 Lenders experienced no credit risk with respect to the intermediary.’). 34See Verstein(2011) (‘Under Rule 415, the issuer can register securities now, but not actually sell them until later when they “take them o the shelf” on which they have been metaphorically waiting. Rule 415 also allows issuers to enjoy economies of scale in registration. It is much cheaper to register a bundle of securities and then take them o the shelf at intervals rather than to begin registration anew with each small security issuance.’). 35See Verstein(2011) (‘While P2P lenders once had credit exposure only to the underlying borrower, they are now unsecured creditors of the intermediary platform. Consequently, these lenders now risk platform default, which could leave them vying for a share of the P2P notes against other, more senior, creditors.’). 36See Verstein(2011) (‘Under Rule 415, the issuer can register securities now, but not actually sell them until later when they “take them o the shelf” on which they have been metaphorically waiting. Rule 415 also allows issuers to enjoy economies of scale in registration. It is much cheaper to register a bundle of securities and then take them o the shelf at intervals rather than to begin registration anew with each small security issuance.’).

20 1.3.2 Transactions outside the scope of security laws

Given the rather extensive and vague denition of a ‘security’ under U.S. federal securities laws, one could imagine a range of economically similar transactions, which could potentially qualify under the obscure Howey test. However, the fact is that most transactions occurring in the economy do not fall under securities regulation. Even within the realm of nancial transactions, which would reasonably qualify under Howey, many end up falling outside the scope of the security laws. e reasons for this is two-fold: either these transactions are explicitly exempted by the security laws themselves or they are subject to rm-specic regulation, which governs these transactions more explicitly.

1.3.2.1 Transactions and parties exempted from security laws

Securities regulation provides for a whole catalogue of exemptions, which apply with respect to certain connecting factors. In particular, this includes:

• transaction mode exemption criteria;

• transaction party exemption criteria;

• investment rm exemption criteria.

e way in which the economy operates through these exemptions is substantial. In fact, a recent empirical study conducted by the SEC has found that more capital has been raised through exempted securities oerings than through securities oerings that fall under the scope of securities regulation.37 e most heavily used exemption is the private placement exemption under Rule 506(b)38 of Regulation D.39 Every year, trillions of dollars are raised under this exemption across a wide range of asset classes, including hedge funds, private equity funds and venture funds.40 Chapter 2: Startups and VC rms under Regulation D Chapter 2 of this PhD thesis discusses how exemptions under Regulation D allow both startups and venture rms to operate largely in the shadows of security laws. According to Bauguess et al.(2018), in 2017, of the total of c. $1.8 trillion raised through c. 51k individual fund and non-fund oerings, the venture capital allocation under Rule 506(b) of Regulation D accounted for only 3’913 individual oerings and c. $135.5bn raised. us, although venture makes up only a fraction of the exempted oerings, the venture asset class discussed in chapter 2 provides an interesting illustration of the wide range of connecting factors, which both startups and venture funds have to comply with in order to ‘avoid’ a public market allocation.

1.3.2.1.1 Transaction mode exemption criteria

For transactions to qualify under Rule 506(b) of Regulation D, the agents transacting must not engage in ‘general solicitation’ or ‘general advertisement’. While these terms are not dened in the statute, Rule 502(2) of Regulation D prohibits express advertisements, such as ads published in newspapers, magazines or other media as well as any sem- inars or meetings where the aendees have been invited by way of general solicitation.41 Further to that, through the ‘no action leer practice’ of the Commission, there exists more specic guidance on what is encapsulated under these terms. For example, where an investor has established a pre-existing,42 substantive43 relationship with the issuer, this

37See Bauguess, Gullapalli, and Ivanov(2018). 3817 C.F.R. § 230.506(b). 3917 C.F.R. § 230.500. 40See Bauguess et al.(2018) (‘In 2017, there were 37,785 Regulation D oerings reported on Form D lings, accounting for more than $1.8 trillion raised in new capital.’). 4117 C.F.R. § 230.502(2). 42See, E.F. Huon & Company, SEC No-Action Leer (Dec. 3, 1985) (‘In determining what constitutes a general solicitation the [SEC Sta] has underscored the existence and substance of prior relationships between the issuer or its agents and those being solicited’). 43See, Citizen VC, Inc., SEC No-Action Leer (Aug. 6, 2015). (holding that ‘the quality of the relationship between the issuer (or its agent) and an investor’ is a critical factor).

21 will not constitute a ‘general solicitation’ in the eyes of the Commission. As this example demonstrates, the connecting factors applied by the SEC can be rather subtle, detailing specic contracting behaviors and mechanisms. Exemptions are thus oen subject to extensive interpretation and discretion of the regulators.

1.3.2.1.2 Transaction party exemption criteria

Further to the criteria relating to the mode of transacting, Rule 506(b) of Regulation D sets out transaction party criteria as connecting factors.44 In particular, exempted transaction parties can either include rms or individuals, but must not include more than 35 non-accredited investors. Where the transaction party is a rm, the rm must either be a specialized rm45 and have assets in excess of $5m or be a legal entity46 in which all equity owners are accredited investors. With respect to individual investors, securities regulation requires an individual to have either an annual income of at least $200,000 per year ($300,000 with their spouse)47 or a net worth of at least $1 million.48 In summary, the connecting factors used by securities regulation in these instances rely on both institutional qualications or the monetary means of the investors.

1.3.2.1.3 Investment rm exemption criteria

Even where the underlying transactions are excluded from securities regulation, there still exist a considerable number of connecting factors at the level of the investment rms dealing in such exempt securities. To avoid these, rms are oen required to t within an elaborate exemption system. For example, in chapter 3 of this PhD thesis, it is discussed how venture capital rms avoid qualication as investment companies under the Investment Company Act. To avoid qualication as such, they typically rely on the ‘3(c)(7) exemption’.49 is exemption is like a sui generis accredited investor exemption for venture capital and private equity rms more generally: instead of relying on the term accredited investor as connecting factor under Rule 501(a) of Regu- lation D, it relies on the term qualied purchaser instead.50 In short, a qualied purchaser under this set of rules can be described as an accredited investor with a higher endowment of monetary assets, in particular $5m instead of $1m in the case of an individual. In addition to an exemption under the Investment Company Act, venture capital funds typically also seek exemption under Section 203(l) of the Investment Advisers Act of 1940 (“Advisers Act”), also known as the venture capital exemption.51

1.3.2.2 Transactions governed through industry-specic regulation

In addition to the exemptions above, many nancial transactions, which in theory should be subject to securities regulation, are instead governed through industry-specic regulation. e most prominent examples of industries with such industry-specic regulation are the banking and the insurance industry. Industry-specic regulations allow for an allocation of a whole set of transactions outside of the market and within specic rm structures. Structurally, transactions with industry-specic regulation require broad industry-level exemptions from security laws.52 At rst sight, this appears to bear similarities to the exemptions granted to investment rms. However, while the exempted investment rms (venture, private equity and hedge funds) mentioned above are largely le unregulated under the exemptions, rms which engage in transactions governed by industry-specic regulation are typically subjected to the close supervision of a dedicated regulatory body.

4417 C.F.R. § 230.506(b)(2). 4517 C.F.R. § 230.501(a)(1)-(3) (including banks, investment companies, retirement plans and charitable organizations, among others). 4617 C.F.R. § 230.501(a)(8) 4717 C.F.R. § 230.501(a)(6). 4817 C.F.R. § 230.501(a)(5). 4915 U.S.C. § 80a–3(c)(7). 5015 U.S.C. § 80a–2(a)(51)(A). 5117 C.F.R. § 275.203(l)-1. 52See SEC(2001) (detailing the sweeping exemptions of banks under the Exchange Act in l934 ‘When Congress enacted the Exchange Act in l934, it completely exempted banks from the regulatory scheme provided for brokers and dealers. […] Absent broker-dealer registration, bank securities activities generally are regulated only under banking law, which has as its primary purposes the protection of depositors and the preservation of the nancial soundness of banks. us, bank securities activities take place outside of the coordinated system of securities regulation that is designed to protect investors, leading to regulatory disparities.’).

22 Chapter 4: Industry-specic regulation of the banking rm Chapter 4 of this PhD thesis explores the industry-specic regulation of banking rms and compares it to the regulation of credit markets under security laws. e operation of a bank is among the most heavily regulated commercial activity a rm can engage in.53 A company that wants to qualify as a bank in the United States must rst undergo an intensive chartering process.54 Once operational, banks are subject to signicant risk restrictions, both on the asset and the liability side of their balance sheet. Traditionally, the focus has been on limiting the scope of activities of banks to allow bank supervisors to beer understand the risk exposure.55 Today, all banks and bank holding companies are subject to an extensive oversight regime: they are not only governed by multiple statutes,56 but also by multiple regulatory agencies.57 e federal regulators include the Federal Reserve System (Fed), which has the main supervisory authority over bank holding companies, the Federal Deposit Insurance Corporation (FDIC), which administers the Deposit Insur- ance Fund and has the supervisory power over the state-chartered banks and nally the Oce of the Comptroller of the Currency (OCC), which supervises national banks and federal savings banks. As reected by the detailed supervisory manuals issued by each of these regulators, bank examiners regularly ‘peer inside’ of banking rms, engaging in a close examination of all bank operations.58 Much like bank managers, bank regulators are tasked with monitoring risk exposures and addressing any deciencies they detect. While the SEC always favors more disclosure under its ‘sunlight is the best disinfectant’ mantra, bank regulators are in turn oen thought to be leaning toward condentiality and under-enforcement.59 On the other hand, in times of distress, they can also be more invasive and engage in substantive, judgment-laden decisions that the SEC typically leaves exclusively to market participants. In summary, the costs imposed by industry-specic regulation on the banking rm transactions are signicant. However, unlike under securities regulation, which regulates at the transaction level, banking law regulates at the rm level. is leads to higher xed costs of allocating through the banking rm. e bank requires a certain threshold amount of capital to become operational, go through the initial chartering process and provide the regulators with periodic disclosures. However, as chapter 4 shows, once operational, industry-specic regulation of banks allows for signicant economies of scale, allowing the banking rm to incur much lower marginal costs on each additional transaction compared to an alternative transaction regime governed by securities regulation.

1.4 e three functional layers of securities regulation

In this section, the three functional layers of securities regulation are introduced, which act as the main units of the regulatory analysis under the theory established in this chapter. ese layers relate to (i) specic market functions (ii) market-enabling rms, which allow market participants to carry out these functions, and (iii) sources of regulatory costs. e functional layers form a hierarchical or sequential governance structure: Once a transaction is deemed a security, the disclosure and information layer is triggered, requiring the issuer to either disclose or claim an exemption under security laws. Both the initial sale and the resale of transactions deemed ‘securities’ must be transacted through regulated intermediaries only. At the investment and liquidity layer, securities are sold to the public in the primary markets through SEC-regulated underwriters and maintained liquid in the secondary markets by SEC-regulated market makers and national securities exchanges. At the diversication layer, investors typically invest in such securities in a diversied manner, in particular through SEC-regulated investment companies, such as mutual funds or exchange-traded funds (ETFs).

53See Richard Carnell and Miller(2013) (‘Banking is among the world’s most heavily regulated industries.’). 54Id. at 71–73 (describing the chartering process). 55See Garten(1989) (‘Conning banks to particular activities enabled bank examiners to concentrate on one area, that of loan quality, in which they could develop considerable expertise.’). 56To name just a few of the principal ones: the Federal Deposit Insurance Act (FDIA), 12 U.S.C. §§ 1811 – 1835a, the Bank Holding Company Act (BHC), 12 U.S.C. §§ 1841 – 1852, the National Bank Act, 12 U.S.C. §§ 21 – 216d, the Federal Reserve Act, 12 U.S.C. §§ 221 – 522, the Home Owners’ Loan Act (HOLA), 12 U.S.C. §§ 1461 – 1470, Dodd-Frank Wall Street Reform and Protection Act, 12 U.S.C. §§ 5701 – 5710. 57ere exist three federal bank regulators as well as a multitude of state regulators. 58Bd. of Governors of the Fed. Reserve System., Div. of Banking Supervision and Regulation, Commercial Bank Examination Manual (2015); Fed. Deposit Ins. Corp., Compliance Examination Manual (2015) (1219 pages). 59See Coee and Sale(2009) (‘Instinctively, securities regulators favor full disclosure and transparency, while banking regulators fear that adverse information may alarm or panic investors and depositors, thereby causing a ‘run on the bank.’).

23 e goal of the three functional layers is to provide a set of intuitive and logical reference points along which a more detailed regulatory analysis can be conducated.

1.4.1 Disclosure and information layer

1.4.1.1 Denition

As outlined in the previous section 1.3, for transactions to fall under the scope of U.S. security laws, they must be deemed securities. Economically speaking and irrespective of the legal qualication, a security typically involves an economic relationship between:

• a surplus agent (investor or depositor, with net assets); to

• a decit agent (individual or rm, with net liabilities).

Within the scope of this chapter, a surplus agent refers to an investor in an equity seing or a creditor or depositor in a credit seing. On the other hand, a decit agent refers to an issuer in an equity seing or a borrower in a credit seing. In the context of this economic relationship, the disclosure and information layer is broadly dened as the ow of information between surplus agents and decit agents. e scholarly literature typically explains the existence of disclosures through information asymmetries that exist between investors and issuers.60 To allow the investor to overcome these information asymmetries, the issuer needs to make unilateral disclosures to the investor, which allow the surplus agent to evaluate and price the security. e role of security laws is to mandate certain disclosures to optimally calibrate them to the needs of the market, in particular to avoid underproduction of information or overproduction of information (see section 1.6.2.1.2 below). Against this backdrop, a ‘disclosure’ is referred to as the one-sided, unilateral ow of information from the decit agent to a surplus agent. Under the traditional scholarly perspective on disclosures, the functional layer would be limited to SEC-mandated disclosures. However, the theory developed in this chapter takes a broader perspective. Under the second part of the eorem under section 1.7, alternatives to a unilateral disclosure model are considered. In particular, this involves (i) a model where information costs and obligations are shared between decit and surplus agents and (ii) a stylized information regime with no information frictions, where the investor and the issuer can gather or produce issuer- specic information at the same cost. us, the disclosure and information layer is dened more broadly, going beyond the traditional focus of mandatory disclosures to include a wider range of information exchanges. In particular, the disclosure and information layer under my denition covers the entire information and data ow between (i) surplus agents, (ii) decit agents and (iii) third-party data providers. In concrete terms, the disclosure and information layer thus relates to both traditional regulated channels (the SEC EDGAR system and more novel forms of 8-K disclosures, such as social media) and currently unregulated, nontraditional data channels.

1.4.1.2 Disclosure and information layer under U.S. security laws

Under federal securities regulation, the mandatory disclosure obligations entail both initial disclosure obligations and continuing disclosure obligations. e disclosure requirements are divided between the Securities Act of 1933,61 which regulates the initial issuance of securities, and the Securities Exchange Act of 1934,62 which imposes periodic reporting requirements in connection with the subsequent trading of securities. Crucially, the costs of compliance with these disclosure obligations are fully placed on the decit agent.

60See Healy and Palepu(2001) (‘First, entrepreneurs typically have beer information than savers about the value of business investment opportunities and incentives to overstate their value. Savers, therefore, face an “information problem” when they make investments in business ventures. […] Another potential solution to the information asymmetry problem is regulation that requires managers to fully disclose their private information.’); Diamond and Verrecchia(1991) (‘is paper shows that revealing public information to reduce information asymmetry can reduce a rm’s cost of capital by aracting increased demand from large investors due to increased liquidity of its securities.’); Mahoney(1995) (‘Disclosure can contribute to informational eciency (and ultimately to social welfare) by enabling traders to gather information, and thereby reect new information in prices, at a reduced cost compared to a world without disclosure.’). 6115 U.S.C. §§ 77a-77z-3. 62Id. §§ 78a-78mm.

24 1.4.1.3 Market-enabling rms at disclosure and information layer

To comply with mandatory disclosure obligations, in particular the initial oering prospectus, as well as periodical disclosures thereaer, extensive legal, nancial and accounting disclosures are required from the issuer. Given the publicity and market scrutiny to which these disclosures expose an issuer and the issued securities, a high degree of care and costs is typically required in the preparation and publication of these documents. is means that issuers need to engage the services of professional services rms specialized on public accounting lings and SEC prospectuses to access the public markets.63 us, the traditional market-enabling rms of the disclosure and information layer are corporate law rms and accounting rms.

1.4.2 Investment and liquidity layer

1.4.2.1 Denition

Whereas the disclosure and information layer governs the ow of information between investors and issuers, the investment and liquidity layer governs the ow of funds between them. e investment and liquidity layer can be further broken up into two separate sub-functions:

• primary market activities govern the initial ow of funds from surplus agents to decit agents, typically facilitated by underwriters:

• secondary market activities govern the secondary ow of funds between surplus agents, typically facilitated through market makers and nancial exchanges.

ese sub-functions are all governed through security laws and provide the core mechanism of the public market operations.

1.4.2.1.1 Primary market activities

In practice, it is typically the case that where an investor and an issuer transact, there is no direct ow of funds from the investor to the issuer. Rather, the initial economic transaction goes through an intermediary, an underwriter rm, which purchases (or underwrites) the securities from the issuer.64 e underwriter then re-sells these securities to investors. e process of underwriting can be seen as a transitory (banking) rm allocation structure. is transitory banking-like allocation relieves both information asymmetries and liquidity concerns, as the underwriter acts as a reputational signal to the market and at the same time provides immediate liquidity to issuers. is initial purchase and re-sale of securities through underwriter rms is what the investment leg of the investment and liquidity layer refers to. In the realm of traditional equity IPOs, discussed in more detail in chapter 2 of this PhD thesis, primary market activities refer to the rm-commitment65 underwriting by an investment bank of new shares issued by a company going public. In the realm of credit markets, discussed in more detail in chapter 4 of this PhD thesis, primary market activities refer to the traditional underwriting of bond oerings and the originate-to-distribute model in the sphere of asset-backed securities (ABS). For illustrative purposes, traditional equity oerings are used in the following description of the investment and liquidity layer. Where information frictions between surplus agents and decit agents are high, the role of the underwriter rm is to alleviate these frictions. us, much like the disclosure and information layer, the underwriter’s role is to resolve

63See Haeberle(2018) (‘ese points can be seen by thinking about two of the biggest interest groups with sway at the SEC: lawyers and accountants. Members of each prot tremendously in direct proportion to the volume and complexity of corporate disclosures. Securities lawyers are the ones who prepare corporate disclosures. ey are likewise the ones who le and defend suits when those informational products are defective. Auditing rms also play integral roles with respect to both the provision of disclosure and litigation surrounding the same.’). 64See Sjostrom(2001) (describing this for the equity markets ‘Traditionally, a company ”goes public” by retaining an underwriter to sell shares of the company’s common stock to the general public.’). 65As opposed to a best-eorts underwriting, where the investment bank does not purchase the shares outright but only promises its best eorts in marketing the oering. See Bower(1989) (‘In both types of oerings, the investment banker distributes the new shares, but in a rm-commitment agreement he or she performs the additional function of insuring the proceeds.’).

25 information asymmetries. At this layer, information asymmetries are alleviated through a combination of both (i) data gathering and dissemination measures enabled by the underwriter and (ii) the reputational signal of the underwriter.66 In the context of equity oerings, data gathering and dissemination typically takes place in the context of a so-called ‘road show’.67 By means of this ‘road show’, both issuer and underwriter will share nancial and business details with investors and obtain indications of interest and price information in return (the so-called book building process). e costs of the underwriter are typically borne by the issuer. e issuer typically pays the underwriter indirectly through the underpricing of the securities oering. Underpricing means that the investment bank pays the issuer a lower price for the shares, than what is considered the fair market value aer the road show and the book building process. IPO underpricing is hard to quantify, but typically the dierence between the oering price and the closing price at the rst day of trading is used as a proxy.68 While such IPO underpricing can vary widely,69 it has been shown empirically to oscillate in the range of 7 percent.70

1.4.2.1.2 Secondary market activities

Once the securities have been placed with investors, they begin trading in the ‘secondary markets’. is secondary market is what the liquidity leg of the investment and liquidity layer refers to. Market makers In theory, if there was an equal number of buyers and sellers in the market at all times, they could transact directly with each other through an open order book. However, given that there are always imbalances between the number of buyers and sellers, the issuer typically engages the services of a market maker. In practice, this is oen the same investment bank acting as underwriter. e issuer pays the market maker either directly or indirectly through the IPO underpricing or on a recurring basis for providing liquidity in the secondary market. e role of the market maker is to provide bid and ask spreads to the market, i.e. to buy securities when everyone in the market wants to sell and, in the reverse scenario, to sell securities when everyone in the market wants to buy. is typically requires the market maker to hold securities of the issuer on inventory and in a transitory (banking) rm structure, similar to the transitory allocation in the primary market. Depending on the market sentiment, the market maker will adjust the bid and ask spread to allow the dealer to limit proprietary exposure, while still providing liquidity to the market. Financial exchanges Financial exchanges form the second element of the liquidity leg of the investment and liquidity layer. ey provide the infrastructure for the exchange of securities on the secondary market. To use the marketplace infrastructure of regulated nancial exchanges, the issuer is typically required to pay a recurring listing fees. In past times, prior to the existence of information technology, nancial exchanges constituted physical locations where broker-dealers would gather and physically transact by means of communicating through open outcry or hand signal systems. Nowadays, the role of physical locations has been marginalized and nancial exchanges are instead largely governed by soware and code. e market microstructures can vary across exchanges. However, the most ecient form of allocation appears to be through an electronic central limit order book, which both the New York Stock Exchange and the Nasdaq operate.71

66See Bower(1989) (Describing the underwriting as a signaling device itself ‘In addition, a rm-commitment agreement uses the reputational capital of the investment banker to certify the value of the issue to a greater degree than in a best-eorts oering’.). 67See Sjostrom(2001) (‘With an underwrien IPO, this involves responding to underwriter due diligence requests, aending draing sessions and participating in the road show’). 68See Hurt(2005) (‘Generally, the IPO share price usually rises above the oering price during the rst day of trading. is increase may be modest or almost incomprehensibly large. Assuming that the resulting price is the ”market price,” many commentators then refer to the IPO oering as being ‘underpriced’.’). 69See Derrien and Womack(2003) (recounting the IPO of Broadcast.com, which appeared to be heavily underpriced and thus popped by 277 percent in the rst day of trading); Baker(2000) (recounting the VA Linux Systems, Inc. IPO which was priced at $30 per share and popped to $239 per share on the rst day of trading). 70See H.-C. Chen and Rier(2002). 71While not important to this discussion, it should be noted that the market microstructure between the NYSE and the Nasdaq varies slightly due to dierences in market maker coverage, with the Nasdaq operating under a more open system where multiple rms can act as market maker for a given issuer.

26 ereby, buyers and sellers can enter either a market order or a limit order,72 which is then executed against a limit order book. is allows market participants to either provide liquidity to the market or take liquidity from the market.

1.4.2.2 Investment and liquidity layer under U.S. security laws

Under U.S. securities regulation, the investment and liquidity layer is regulated in an entity-centric manner.73 is means that, unlike at the disclosure layer, where the transaction or the investment contract is the regulatory object and category of analysis, security laws at the investment and liquidity layer focus on the individual market-enabling rms. In particular, the market-enabling rms are SEC-regulated brokers-dealers and national securities exchanges. Both of these are regulated under the Securities Exchange Act of 1934.

1.4.2.3 Market-enabling rms at the investment and liquidity layer

As already discussed above, the market-enabling rms at the investment and liquidity layer are in particular investment banks, such as Goldman Sachs or Morgan Stanley, as well as nancial exchanges, such as the NYSE or Nasdaq. Invest- ment banks act both as underwriters in the primary markets and as market makers in the secondary markets, while nancial exchanges provide the market infrastructure for both primary oerings and secondary markets.74

1.4.3 Diversication layer

1.4.3.1 Denition

While the disclosure and information layer governs the ow of information and the investment and liquidity layer the ow of funds between the surplus and decit agent, the diversication layer is concerned with the optimal parceling of investments by the surplus agent. Since surplus agents, by nancing decit agents, actively assume idiosyncratic economic risk under a market-based allocation, they have an incentive to hedge their risk exposure by parceling, or ‘diversifying’, such risks across multiple decit agents. At the extreme, as suggested by a dominant stream of the nancial economics literature, the surplus agent should maximally diversify the exposure by investing an innitesimal amount in the full universe of decit agents. In such a stylized diversication regime, individual investors would need to parcel out their total investment amount and deploy it to the entire universe of issuers. As this would clearly lead to excessive transaction costs, surplus agents regularly pool their funds with other surplus agents at the diversication layer and deploy capital with the help of a number of diverse market-enabling rms, including mutual funds, exchange-traded funds (ETFs) and pension funds. e most widely accepted theoretical foundation for diversication can be found in a nancial economics theory, known as the Ecient Capital Market Hypothesis (EMH).75 According to the most common denition of the EMH, a market is ‘ecient’ when prices fully reect all available information.76 As a consequence, in a fully information- ecient market, investors cannot earn an excess return over the market (so-called alpha) from actively managing the portfolio. Under a closely related theoretical stream in the nance literature, namely the capital asset pricing model (CAPM) developed by Sharpe(1964), Lintner(1965) and F. Black(1972), the central hypothesis states that the ‘market portfolio’ of invested assets is mean-variance ecient in the sense of Markowitz.77 Put dierently, the most ecient solution for investors is to diversify to the greatest extent possible by ‘buying the market’ or ‘indexing’. As observed by Ronald Gilson and Reinier Kraakman, the EMH has not only become dominant in the elds of nance and economics,

72Market orders are placed to be executed immediately ‘at market’, meaning at the present market price. On the other hand, a limit order sets a maximum or minimum price at which a party is willing to transact. 73See Krug(2013) (‘If the entity is the marketplace actor, then, logically, the entity should be the regulatory subject.’). 74e role of nancial exchanges could expand in the future, in particular making them more relevant for primary markets through the recent phenomenon of ‘direct listings’, discussed in more detail in chapter 2. 75See M. C. Jensen(1978) (pointing out that ‘there is no other proposition in economics which has more solid empirical evidence supporting it than the Ecient Markets Hypothesis.’). 76See Fama(1970) (stating that a market in which prices always ‘fully reect’ available information can be called ‘ecient’). 77See Markowitz(1952) and Markowitz(1959).

27 where it provides inter alia a theoretical foundation for the CAPM,78 but has also been adopted widely by security law scholars, regulators, judges and lawyers.79 In today’s nancial markets, as will be outlined in more detail in section 1.6.4.1, the ndings of the EMH and the CAPM are materialized to dierent degrees by market-enabling rms operating at the diversication layer.

1.4.3.2 Diversication layer under U.S. security laws

Under U.S. securities regulation, the diversication layer is, much like the investment and liquidity layer, regulated in an entity-centric manner.80 Mutual funds and ETFs are subject to comprehensive regulations, which are set forth in the Investment Company Act of 1940 (‘Investment Company Act’)81 and the associated SEC rules.82 Pension funds, on the other hand, fall outside of the scope of the Investment Company Act and are subject to a specic set of regulations.83

1.4.3.3 Market-enabling rms at diversication layer

As already discussed above, the market-enabling rms at the diversication layer include a wide range of investment rms, such as exchange-traded funds (ETFs), mutual funds and pension funds.

1.4.4 Summary overview and limitations

e below summary table gives an overview of the three functional layers of securities regulation developed above. It should be noted that, although these layers have been separated to add structure to the discussion of the eorem, these layers can oen not be viewed in isolation and instead overlap at multiple junctures. For example, if one looks at Section 22(e) of the Investment Company Act,84 then both the liquidity layer and the diversication layer are aected. e provision sets out the rights of investors in open-ended funds to demand prompt redemption and compels such funds to make payment on the investors’ redemption request within seven days of receiving the request. us, while the provision is addressed at a market-enabling rm at the diversication layer, compliance with it also requires the presence of liquid secondary markets under the investment and liquidity layer. Similarly, there exist many other instances where multiple layers are covered at once and where the categorization along the three functional layers may appear overly reductionist. Despite these obvious limitations, disaggregating the debate along these lines adds a logic to the analysis that is necessary for the development of a comprehensive theory of securities regulation.

Functional layer Market-enabling rms Primary Regulation Cost bearer Disclosure & Accounting rms Securities Act 1933 Issuer Information Corporate law rms Investment & Investment banks Securities Exchange Act 1934 Issuer Liquidity Financial Exchanges Diversication Mutual funds Investment Company Act 1940 Investor Pension funds

78See R. Gilson and Kraakman(1984) (‘As noted earlier, the assumption of perfect markets underlies the major theoretical developments in nancial economics between the late 1950s and the early 1970s, including CAPM and the Miller-Modigliani Irrelevance Propositions.’). 79See R. Gilson and Kraakman(1984) (‘of all recent developments in nancial economics, the ecient capital markets hypothesis (”ECMH”) has achieved the widest acceptance by the legal culture …the ECMH is now the context in which serious discussion of the regulation of nancial markets takes place.’). 80See Krug(2013) (‘Entity-centrism also plagues the regulation of mutual funds and other publicly oered funds (formally known as investment companies).’). 8115 U.S.C. §§ 80a-1 to 80a-64. 8217 C.F.R. §§ 270.0-1 to 270.60a-1. 83Outlined in more detail in section 1.6.4.1.3. 8415 U.S.C. § 80a–22 (e).

28 1.5 e Coase eorem of Securities Regulation

1.5.1 Doctrinal Classication

e Coase eorem of Securities Regulation is a novel theory of securities regulation, developed within the scope of this chapter, which analyses the eects of securities regulation on the formation of rms and markets. It can be classied as a functional theory of law and economics, which aims to oer value-neutral principles of lawmaking in the area of securities regulation. By integrating both foundational work of Ronald Coase and Guido Calabresi, the eorem has both positive elements of the Chicago-school and normative elements of the Yale-school of law and economics. As a functional theory of law and economics it is, however, placed most closely in the tradition of the Virginia school of economics. As such, it is skeptical of both the purely positive and the purely normative streams of the literature.

1.5.2 Substantive Provisions

e eorem developed within this chapter is a law and economics theory, which has two principal parts: e rst part seeks to explain why certain transactions are carried out through the rm or the market respectively. It views the rm and the market as competing legal regimes, which impose dierent prices on economic transactions. Based on these regulatory prices, economic actors choose to allocate their economic activity through either the market or the rm. us, the observed allocation mechanism can be regarded as a representation of rational preferences of economic actors, given a particular legal seing. Within this layer, security laws are viewed as endogenous transaction costs. is Chicago view of the allocation mechanism is subject to a number of limitations, in particular (i) externalities and internalities that may exist under either allocation structure and (ii) public choice failures through which regulations may be updated more slowly than socially desirable (‘pacing problem’). While the rst part of the eorem regards the regulatory costs of securities regulation as an endogenous transaction cost, the second part of the eorem re-conceptualizes the costs of the market as an externality. is allows us to make the costs of the market, and security laws in particular, the main unit of analysis and place them within the classical seing of the original Coase eorem. By building on the normative work of Calabresi, which has challenged the frictionless regime of the Coase eorem, this enables us to consider both optimal size and optimal assignment of these costs. ereby, the second layer of the eorem allows us to set out the foundations of a general cost-benet analysis tool for securities regulation that is rooted in established theories of law and economics and which can guide policy analysis of concrete security laws along the three functional layers. e overarching aim of the eorem is to enable policy makers to optimally design securities regulation such that the legislation is able the achieve the overall policy goal of fostering open and transparent nancial markets.

1.6 First Part of the eorem

1.6.1 Substantive provisions

1.6.1.1 eoretical foundation

1.6.1.1.1 ‘e Nature of the Firm’

In his seminal 1937 paper ‘e Nature of the Firm’,85 Coase asks the question: why do rms exist? Similarly, the rst part of the eorem developed in this chapter asks us to consider why we see certain economic transactions allocated through the rm and others through the market. Coase argues that parties encounter dierent costs whether they are operating through the rm or the market and that these dierential costs guide the allocation of transactions through either the rm or the market. Under the eorem developed herein, the focus lies on two distinct costs, which are referred to as:

85Coase(1937).

29 • baseline costs

• regulatory costs

Since these costs form the main object of analysis of my eorem, they require precise denition.

1.6.1.1.2 Baseline costs

Baseline costs are here referred to as the costs ‘naturally’ encountered by the transacting parties in a given contracting environment. Under Coase’s original work, this notion of baseline costs can be broken up into what is referred to as (i) pricing costs and (ii) commitment costs. Let us rst look at the pricing costs, which Coase describes as follows:

‘e main reason why it is protable to establish a rm would seem to be that there is a cost of using the price mechanism. e most obvious cost of “organizing” production through the price mechanism is that of discovering what the relevant prices are. is cost may be reduced but it will not be eliminated by the emergence of specialists who will sell this information.’86

Let us take the example of a bakery and consider how the presence of pricing costs aects the decision of the baker to operate as a rm, rather than allocating his economic activity through the market. If the baker shows up for work every morning and has to hire employees through the market, rent the baking machinery through the market and negotiate a commercial lease with multiple landlords, this would clearly be too costly. Each market transaction would require the baker to individually price both labor and capital ‘ingredients’ on the spot. erefore, the rm structure, where the bakery owns its machinery, hires employees and operates out of a xed location, appears to be a more ecient allocation mechanism for the baker. e second source of baseline costs, which is referred to as commitment costs, are described as follows by Coase:

‘It may be desired to make a long-term contract for the supply of some article or service. is may be due to the fact that if one contract is made for a longer period, instead of several shorter ones, then certain costs of making each contract will be avoided.’87

Commitment costs can be understood as a special form of pricing costs with respect to the time dimension. Instead of taking out long-term contracts through the rm, the baker could take out a series of short-term contracts through the market. is would, however, require the baker to price and re-price these short-term contracts. e baker can hire a new cohort of day laborers every morning, but the costs of retraining them every day makes it inecient for him to do so. Similarly, the baker could rent the baking machinery and equipment from another baker on an hourly basis, but the costs of moving the rented items back and forth during idle times would most likely be cost prohibitive. us, in the presence of commitment costs, it is more ecient for him to hire full-time employees, purchase the equipment outright and enter into a long-term lease with his landlord. As a result of the baseline costs described above, the market allocation – which Coase refers to as the ‘price mechanism’ – is typically dominated by transactions that can be broken up into clearly separable units: one apple, one barrel of crude oil, one share of a company. Where the transactions involve goods or services which are hard-to-measure, costly to acquire in small units or pertain to short periods of time (in the case of services rendered), then the rm allocation prevails. In other words, the baseline costs associated with the price mechanism can oen transition an economic activity into a rm allocation. 86See Coase(1937). 87See Coase(1937).

30 1.6.1.1.3 Regulatory costs

Regulatory costs here refer to the costs imposed on an economic transaction by regulation. In the context of the eorem developed in this chapter, this refers to the costs imposed by securities regulation or rm-specic regulation. Coase describes what is here referred to as regulatory costs as follows:88

“Another factor that should be noted is that exchange transactions on a market and the same transactions organized within a rm are oen treated dierently by Governments or other bodies with regulatory powers.”

To illustrate how regulatory costs are distinct from the baseline costs dened above, let us imagine a stylized seing were baseline costs are zero. In such a stylized scenario, the baker could now show up at work every morning and acquire all the necessary means of production and commerce through the spot market. A frictionless world would allow him to hire labor, machinery and land ‘on the clock’ through the market at the same costs as under the rm allocation. us, the baker could theoretically make the decision to operate a bakery on a daily, marginal basis: opening and closing the bakery depending on whether market conditions allow for a marginal prot or not. In such a seing, the presence of a government regulator, which is imposing regulatory costs on the the bakery industry, could fundamentally alter the business calculations of the baker. Let us assume the government requires every bakery to pay for an ‘annual bakery license’. Such a regulation would set an annual xed cost for the operation of a bakery. us, the marginal costs of selling a single loaf of bread would now be signicantly higher for the baker. e size of this license may in fact be so substantial that it could render the operation of a bakery unprotable, unless the bakery is operated for the entire year. us, in the otherwise frictionless market seing, the eect of the regulation could be to ‘price’ the allocation of the bakery out of the market allocation and into the rm allocation. us, regulatory costs can have similar pricing eects as baseline costs when it comes to shaping the allocation eciency of transactions. e rst part of the eorem focuses on these regulatory costs. In particular, it aims to identify and analyze the dierential eects of security laws and rm-specic regulation on the allocation of transactions through either the rm or the market.

1.6.1.1.4 Interaction between baseline costs and regulatory costs

Baseline costs and regulatory costs are not always easy to separate and they may interact over time. e economic activity is subject to both costs and its ecient allocation may change dynamically over time as the costs adjust. Transactions that used to be allocated most eciently through the rm can transition to the market and vice-a-versa. Where baseline costs fall signicantly through digital modes of delivery, transactions are likely to transition from a rm allocation to a market allocation. For example, in the recent decade, digital means of communication have enabled market-based sharing economy transactions, which would not have been possible prior to the internet and the wider adoption of smartphones. A sudden decline in baseline costs can create friction with regulatory costs that may be lagging behind (so-called ‘pacing problem’ further discussed below). As regulation may still be geared towards a regime with higher baseline costs, a technically more ecient mode of transacting may not be compliant with the existing laws.89 e market entry of Uber has famously created regulatory disputes across the globe, as local taxi regulations were oen geared towards an allocation of transportation services through incumbent taxi companies, in particular by limiting the supply of taxi medallions.90 Similarly, the market entry of Airbnb has collided with local zoning and short-term

88See Coase(1937) at 393. 89See Benjamin and Geradin(2016) (‘New soware platforms use modern information technology, including full-featured web sites and mobile apps, to allow service providers and consumers to transact with each other without costly intermediaries. Platform operators typically provide information about service providers (e.g., drivers) and services oered (e.g., short-term rentals), as well as online payment facilities, reputation mechanisms to assure quality, and assistance with dispute resolution. e resulting systems oer dierentiated products previously not readily available (such as short-term rentals more spacious than hotels), as well as lower prices. Despite signicant interest from consumers, these platforms tend to be in tension with existing regulatory frameworks. On one view, some regulations are outdated or protectionist, beneting incumbents more than consumers.’). 90See Rogers(2015) (describing the conict of a fall in baseline costs with regulation ‘While Uber is extremely aggressive toward competitors and seems to disregard the law when convenient, its success is not based just on regulatory arbitrage. Nor is it simply toppling an ancien regime of taxi regulations that merely protect medallion holders’ monopoly rents. Rather, Uber’s key innovation lies in having reduced the transaction costs that otherwise plague the sector and provided the justication for its extensive regulation in the rst place.’).

31 occupancy tax laws, which were geared to an allocation of hospitality services through specialized hotel rms.91 Like baseline costs, regulatory costs may also trigger a shi in the eciency of a given allocation structure. In highly regulated industries, regulatory costs may fall due to economic liberalization or structural reform. As a result, transactions may transition from a rm allocation to a market-based allocation. For example, the deregulation of the energy industry over the past decades has in many countries transitioned the allocation of energy from vertically-integrated, oen state-owned, utility rms to a more market-based allocation, in particular at the wholesale level.92 In summary, the empirically observed allocation of an economic activity at any given point can thus be understood to be the product of both baseline and regulatory costs. In the rst part of the eorem, both costs are considered endogenous transaction costs, which interact in a complex dynamic system with recursive feedback eects.

1.6.1.2 Limitations of the rst part of the eorem

e rst part of the eorem takes a positive perspective on the allocation of transactions through either the rm or the market. As such, for a given legal regime, it considers the allocation mechanism to be an ecient solution. However, this Chicago School perspective is subject to a number of limitations, such as:

• the presence of externalities/internalities;

• regulatory failures.

1.6.1.2.1 Externalities and internalities

ere exist both positive and negative externalities and internalities associated with the allocation through the rm or the market, which may not be fully captured by the legal regime. e presence of such long-term benets and costs may not have been recognized or anticipated by lawmakers when passing legislation. us, an otherwise ecient allocation can be rendered inecient when these eects are taken into account. While fundamental for the beer understanding of an optimal design of rm-specic regulation (or the scope of rm-specic exemptions of security laws), such eects fall outside the scope of the eorem developed within this chapter. However, within the broader scope of this PhD thesis, chapter 3 from the equity pair and chapter 5 from the credit pair analyze and reect on such costs and benets, thereby complementing the more limited analysis under the eorem:

• Chapter 3: positive internality of the venture rm allocation A unique feature of venture capital nancing is the role of VCs as active investors. Beyond merely providing nancing, VCs ideally oer advice and business support to entrepreneurs of startup rms. us, in the presence of such positive internalities, founders may ceteris paribus rationally prefer venture nancing over market-based nancing. Chapter 3 of this thesis reects on the non-monetary value-add provided by VCs and empirically analyzes venture rms that dierentiate themselves among the advice-giving dimension. In particular, it tries to identify the rise of founder-led venture rms, termed ‘founder-funder’ VCs, and analyze their performance relative to their peers.

• Chapter 5: negative externalities of the banking rm allocation Chapter 5 investigates the negative externalities that are encountered in the allocation of credit through the banking rm. In particular, the chapter develops a simple stress test model through which it analyses how large credit

91See Benjamin and Geradin(2016) (describing how Airbnb initially collided with local tax remiance regulation ‘is problem rst played out in Airbnb in New York, when the city questioned whether or not Airbnb should be required to comply with the 5.875 percent hotel room occupancy tax, which accounts for about one percent of the city’s tax revenue. Similar issues erupted shortly thereaer in San Francisco, New Orleans, Malibu, Berlin, and Barcelona, among other cities. In each instance, soware platforms allowed hosts to provide rooms without collecting or remiing tax, until regulators noticed the problem and insisted that tax be paid.’). 92See Spence(2008) (‘e recent restructuring of energy markets represents a sharp departure from traditional thinking and historical practice. Shortly aer the creation of the electric and gas industries more than a century ago, policymakers in Europe and the United States concluded that both industries were natural monopolies for which competition was inappropriate due to their large economies of scale, or decreasing marginal and average costs across a very large range of output. […] Under this traditional vertically integrated structure, arms-length wholesale energy transactions were rare.’).

32 losses under the banking rm allocation can propagate to the nancial system at large. In other words, it analyzes the negative externality of banking rms, widely referred to as ‘systemic risks’. In the course of the global nancial crisis (GFC) of 2008, these systemic risks have resulted in major costs to society at large through government bail-outs of banking rms. While such adverse eects of the rm allocation may at rst sight seem to favor a market-based credit allocation, the chapter shows through a separate simulation that similar wealth transfers may be encountered in the context of credit markets where governments stabilize credit markets through market bailout mechanisms (e.g. via government guarantees and purchase programs).

1.6.1.2.2 Regulatory failures

In the context of the rst part of the eorem, ‘regulatory failures’ refer to transactions, which in the absence of regulatory cost, would be allocated more eciently under a dierent allocation form. In other words, transactions allocated under the rm may be more eciently allocated through the market and vice-a-versa. e sources of such regulatory failures can be manifold, ranging from myopia of the lawmakers to regulatory capture. However, for the regulatory analysis in this PhD thesis, the focus lies to a large part on regulatory failures that result from a secular decline in baseline costs, which is not reected by a contemporaneous adjustment of the legal rules. In other words, as already described in section 1.6.1.1.4 above, the regulatory costs are lagging behind. Such a lag of regulatory costs, referred to as the ‘pacing problem’ in the scholarly literature, is the main object of analysis under the rst layer of the eorem.93 With respect to securities regulation, the ‘pacing problem’ can relate both to existing transactions, which might have been eciently allocated through the rm a decade ago, but are beer placed in the market a decade later as novel technologies have evolved. On the other hand, it can relate to novel forms of transacting, which are just emerging and which may be misplaced under either the rm or the market structure as a result of a lag in the legal regime.

1.6.2 Application to the disclosure and information layer

As set out in section 1.4.1, the disclosure and information layer relates to the ow of information between decit agents and surplus agents. is ow of information can be a direct exchange between the two sets of agents, as is typically the case in a market-based allocation, or it can be routed through an intermediary, as is typically the case under a rm-based allocation. Initiating and maintaining disclosures under the respective allocation structure entails substantially dierent regulatory costs, as will be further highlighted below.

1.6.2.1 Costs of the market

Under the market allocation, the ow of information through mandatory disclosures takes place directly between decit and surplus agents. Decit agents (issuers or borrowers) make public disclosures to the marketplace, directed to a wide range of heterogeneous surplus agents (investors or creditors). us, regulation takes eect at the level of the individual transaction.

1.6.2.1.1 Mandatory disclosure obligations under U.S. security laws

Under federal securities regulation, issuers are mandated to make initial disclosures at the primary oering of securities and continuing disclosures until the securities mature. Disclosure requirements are divided between the Securities Act

93See Fenwick, Kaal, and Vermeulen(2017) (‘is is particularly true in contemporary seings, where innovation is quicker and the global dissemination of that technology is much faster. In such circumstances, regulators can oen struggle to keep up.’); Butenko and Larouche(2015) (‘e ‘pacing problem’ commonly refers to the situation when technology develops faster than the corresponding regulation, the laer hopelessly falling behind. e metaphor of ‘the hare and the tortoise’ is oen conjured up. As summed up by Marchant and Wallach, ‘at the rapid rate of change, emerging technologies leave behind traditional governmental regulatory models and approaches which are plodding along slower today than ever before’); Marchant, Allenby, and Herkert(2011) (‘Moore’s Law notoriously states that the ‘functional capacity of ICT products roughly doubles every 18 months’, with the same dynamics manifesting in biotechnology, and namely in sequencing human genome. As a result, regulating innovation involves what is called a ‘pacing problem’ in the academic literature from the US, or the ‘challenge of regulatory connection’ or ‘regulatory disconnection’ in European–based scholarship.’).

33 of 1933,94 which regulates the initial issuance of securities, and the Securities Exchange Act of 1934,95 which imposes periodic reporting requirements in connection with the subsequent trading of securities. In general, all securities oered in the U.S. must be registered with the SEC, unless an exemption from the registration requirements is in place. e initial disclosures required by the SEC come in the form of a ‘registration statement’.96 In practical terms, the registration statement is a form that requires the issuer to make certain legal and nancial disclosures. e S-1 form is regulated in detail in the SEC’s regulation S-K.97 Once the securities have been issued, the 1934 Act comes into play. In section 13 of the 1934 Act, it is prescribed that ‘every issuer of a security registered pursuant to section 12 of this Act shall le with the Commission’ such information as the Commission shall require for the ‘proper protection of investors and to insure fair dealing in the security.’ 98 In turn, the registration requirement of section 12 can be triggered in three ways:

• Firstly, where securities are traded on a national securities exchange.99

• Secondly, where an issuer has more than 2’000 shareholders of record or more than 500 non-accredited shareholders and assets of more than $10 million.100

• irdly, where an issuer of registered securities under the 1933 Act is subject to the section 13 continuing disclosure obligations.101

In other words, every non-exempt issuer is subject to both initial and periodic disclosure requirements. e compliance costs of these disclosure obligations under U.S. security laws are substantial. As the above section highlights at a high level, there exist a range of detailed regulatory obligations when it comes to mandatory disclosure obligations at the level of the issuer. When applying this rst part of the eorem to a specic asset class, a more granular analysis of the applicable security laws is required. is will typically reveal a wide range of specic disclosure obligations as they relate to an individual asset class. A commonly held misconception, which is oen implicitly held is that compliance with SEC disclosure requirements implies a substantive assessment of the quality and merits of an investment contract. However, the disclosure obligations are actually completely value neutral with respect to quality and merits of the underlying security. As Easterbrook and Fischel have succinctly put it before:102

‘e dominating principle of securities regulation is that anyone willing to disclose the right things can sell or buy whatever he wants at whatever price the market will sustain.’

In other words, disclosure requirements do not keep anyone from selling lemons103 to investors, as long as these lemons are well-documented. In this respect, the theory developed within this chapter operates under the premise that mandatory disclosure obligations do not imply a substantive test.

1.6.2.1.2 Information overproduction and underproduction aws

Under the rst part of the eorem, disclosure obligations under a market allocation are compared to regulatory costs incurred under the rm allocation. In other words, the rm allocation is used as a baseline for comparision. In contrast, the scholarly literature has reected more generally on misspecications of security laws.

9415 U.S.C. §§ 77a-77z-3. 9515 U.S.C. §§ 78a-78mm. 9615 U.S.C. § 77f (‘Any security may be registered with the Commission under the terms and conditions hereinaer provided, by ling a registration statement’). 9717 C.F.R. Part 229. 981934 Act section 13(a), 15 U.S.C. § 78m. 991934 Act section 12(a), 15 U.S.C. § 781(a). 100See section 12(g) and Rule 12g-1; see also 15 U.S.C. § 781(g). 1011934 Act section 15(d). See 15 U.S.C. § 78o(d); Requirement of Annual Reports, 17 C.F.R. § 240.15d-1. 102See Easterbrook and Fischel(1984) and E. Murphy(2015) (‘SEC registration in no way implies that an investment is safe, only that the risks have been fully disclosed.’). 103On ‘lemon markets’, see below under section 1.6.2.1.2 regarding the ‘underproduction aw’.

34 In particular, it separates misspecications at the disclosure and information layer into the overproduction aw104 of securities regulation and the underproduction aw105 of securities regulation. Overproduction aw e overproduction aw is concerned with cases where the issuer is mandated to produce information that is neither relevant nor required by investors. Or put simply, the wrong information is produced and in large quantities. is can result in an information overload for surplus agents.106 In the baseline and regulatory cost seing, the overproduction aw can be seen as a scenario where the regulatory costs are signicantly higher than the baseline costs. In other words, the law requires disclosures from issuers that the market would not require to eectively price and trade these securities. As outlined in section 1.6.1.1.4, there exist a number of potential causes for such regulatory failures. Some legal scholars see the overproduction aw as a public choice failure, whereby the SEC is subject to regulatory capture by market-enabling rms.107 In particular, they consider that both law rms specializing in securities work and public accounting rms benet disproportionately from the existence of extensive disclosure requirements. us, regardless of the benets that these disclosures may provide to the market participants, these professional bodies have a positive incentive to sway regulators to put in place and uphold excessive disclosure requirements. Underproduction aw e underproduction aw relates to the opposite situation, namely to cases where – in the presence of mandatory disclosure requirements – the decit agent does not disclose enough information.108 Put dierently, regulatory costs are below what would be required to ensure the proper functioning of the market. A classical explanation in the economic literature for the underproduction aw is the ‘market for lemons’ problem.109 is describes a market seing where the seller strategically maintains an informational advantage to sell ‘poor quality merchandise’. Over time, as buyers react to this opportunistic behavior, this can clear out the market, leaving the market with nothing but lemons. Applied to the context of security markets, issuers may have an incentive to under-disclose and only enter the market when oering poor quality securities. As this may lead to a collapse of the market, security laws are socially desirable to reduce information asymmetries to a level were supply and demand can be matched eectively. As a result, the underproduction problem is commonly used as a policy justication for mandatory disclosure obligations. e underproduction aw and the lemons problem have been widely discussed in the scholarly literature relating to early-stage startups,110 IPOs111 and secondary markets.112 In the sphere of digital currencies, developments in recent

104See Haeberle(2018) (outlining the overproduction problem ‘Even if one believes, as we do, that there are instances when compelling disclosure has value for investors or society as a whole, there are several reasons to be concerned that government mandates will call for companies to disclose information when the social benets of the disclosure are outweighed by its production costs.’); Ben-Shahar and Schneider(2014) (detailing the overproduction problem more generally across multiple domains of the laws). 105See Haeberle(2018) (outlining the underproduction problem ‘e mandatory disclosure regime is also likely underinclusive at the same time, leaving the failure of market forces to produce enough corporate disclosure underaddressed in many instances’); Haeberle and Henderson(2018) (‘Public companies drive the United States economy. Information about them is thus of great value to society. Yet, these rms are prone to excessive secrecy.’); Kitch(1995) (detailing the decit agent’s interest in secrecy); Romano(2001) (questioning the existence of the underproduction aw ‘It is, in fact, implausible that there would be a signicant underproduction of rm information in the absence of a single securities regulator.’). 106See Gerding(2016b) (‘Former SEC Commissioner and law professor Paredes argues that securities disclosure overloads investors with too much information. e cognitive limitations and behavioral biases of investors and the costs of processing massive amounts of nancial disclosure, according to Paredes, mean that securities disclosure has not only become less eective in informing the investing public, but also has become counterproductive. Investor decision making can become suboptimal, Paredes contends, because investors take mental shortcuts to si through the massive amount of information that a securities issuer is required to disclose.’). 107See Haeberle(2018) (‘Government ocials operate subject to various political pressures and outside inuences that oen favor more information rather than less. ey will therefore reap benets from disclosure that is not of value to shareholders, traders, and society more generally. All the while, these ocials do not bear anywhere near the full costs of disclosure requirements, making them more inclined to impose those requirements even when society would not benet. ese points can be seen by thinking about two of the biggest interest groups with sway at the SEC: lawyers and accountants. Members of each prot tremendously in direct proportion to the volume and complexity of corporate disclosures.’); Easterbrook and Fischel(1984) (‘e securities laws may be designed to protect special interests at the expense of investors. [ ... ] Many lawyers are specialized in securities work, and other market professionals depend on the intricacies of the law for much revenue. [... ] [ey] would suer windfall losses if existing regulations were repealed. us, they have every incentive to support the status quo on an interest-group basis.’). 108See Haeberle(2018) (‘e mandatory disclosure regime is also likely underinclusive at the same time, leaving the failure of market forces to produce enough corporate disclosure underaddressed in many instances.’). 109See Akerlof(1970) (rst establishing the notion of the lemon’s problem). 110See Ibrahim(2015) (‘More pointedly, will Title III crowdfunding – the end goal of the legislation – turn into a market for ”lemons,”” existing only for low-quality startups and foolish investors?’). 111See Tinic¸(1988) (explaining the role of the underwriter as a form of insurance that helps to reduce the lemons problem in the IPO market). 112See Goshen and Parchomovsky(2006) (rejecting a lemons problem for the public secondary market ‘For asymmetric information to lead to a “lemons market,” the asymmetry should be between sellers and buyers. Nondisclosure by publicly traded corporations in the secondary market does not create asymmetric information between sellers (current shareholders) and buyers (potential shareholders); rather, both sides are in the dark.’).

35 years have suggested that in the absence of basic SEC disclosure obligations, issuers signicantly under-disclose and that the market can quickly converged to a ‘lemons market’.113 Some scholars have rightly noted that, since many issuers disclose a lot of information without being required to do so,114 one could expect the market to self-correct the underproduction aw over time.115 One explanation why the market may fail in this respect, is that voluntary disclosures can create positive externalities for other rms (in particular private ones) that may lead to competitive disadvantages for the rm making the disclosures.116 In the absence of self- correcting market mechanisms, security laws are considered the most eective remedy to the underproduction problem. ere are two possible explanations why security regulations may, however, fail to establish socially optimal regulatory costs. On the one hand, the regulators may be unable to identify the relevant data points that are under-disclosed.117 Alternatively, as for the overproduction aw, there may exist public choice failures, insofar as the SEC may be subject to regulatory capture or is guided by other political sources of inuence.118

1.6.2.1.3 Chapter 2: Mandatory disclosure obligations in the sphere of technology startups

Chapter 2 of this PhD thesis explores mandatory disclosure obligations under U.S. security laws in relation to technology startups. is analysis provides more specicity and granularity for the particular ‘asset class’ of equity in high-growth technology startups. In particular, disclosure obligations under securities regulations are analyzed as they relate to traditional initial public oerings (IPOs) of technology startups, as well as the alternative security oerings introduced by the JOBS Act,119 including public oerings under the (i) emerging growth company (EGC) status, (ii) Regulation A+ oerings and (iii) Regulation CF oerings. Echoing the many voices of startup founders, venture capitalists and legal scholars, it is found that these obligations are substantial, even under the far-reaching amendments made by the JOBS Act.

1.6.2.1.4 Chapter 4: Mandatory disclosure obligations in the sphere of credit

Chapter 4 of this PhD thesis explores mandatory disclosure obligations under U.S. security laws in relation to credit transactions. Again, the legal analysis is more specic, focusing on the regulations that are idiosyncratic to credit. In particular, the focus lies on disclosure obligations under securities regulations as they relate to traditional corporate bond oerings, asset-backed securities (ABS) and peer-to-peer loans. Again, it is found that mandatory disclosure obligations are substantial, in some credit segments fully thwarting market-based transactions (P2P loans) or substantially decreasing credit activity (non-agency MBS credit).

113Issuers of these digital currencies were oen disclosing lile more than a ‘white paper’, a few pages explaining the idea, and had lile to show in terms of product or revenue. See Essaghoolian(2019) (‘Without any of the required disclosures or restrictions on conditioning markets that are imposed on traditional securities, ICO scammers have been able to disappear with hundreds of millions of dollars from investors who were promised the opportunity to invest in the next big startup project.’). 114See Romano(1998) (‘rms the world over voluntarily release more information than their securities regulators require in order to raise capital’). 115See Palmiter(1999) (observing that where regulatory disclosure requirements have fallen, this has given rise to investor informational demands that compel issuers to disclose ‘at levels beyond that mandated – as a private, contractual maer’). 116See de Fontenay(2017) (‘One reason is that disclosure has material third-party eects or externalities – information disclosed by one company may help its competitor, for example, which discourages voluntary disclosure. In this view, a well designed mandatory disclosure regime should benet disclosing companies as a group and reduce their collective cost of capital by compelling them to disclose the optimal amount of information to the market.’). 117See Haeberle(2018) (‘At the most basic level, it is doubtful that the government is able to identify and compel all of the information production that users (and potential users) of corporate information would nd valuable beyond its cost of production. is conclusion should come as lile surprise. We wouldn’t think a government ministry of the internet would be able to accurately predict consumer demand for social media application features, so why would the SEC do beer with respect to demand for corporate information? e room for miscalculation is simply too great, at least with respect to the demand for company information. is is especially true because there is signicant heterogeneity of preferences across investors for a bureaucratic agency to satisfy investor demand optimally – let alone the demand of a broader range of information consumers.’). 118See Haeberle(2018) (‘On this note, the problems of bureaucracies and public-choice economics also frustrate the command-and-control approach to disclosure in a way that maers for the underproduction concern. Whether to satisfy political masters or because of regulatory capture, the SEC may resist requiring disclosure of things investors care about, or simply underestimate the benets of certain disclosures. While the SEC is tasked with serving the public interest, it may deviate from that goal for a number of other well-known reasons.’). 119Jumpstart Our Business Startups (JOBS) Act, Pub. L. No. 112-106, §§301–05, 126 Stat. 306, 315–23 (2012) (codied in scaered sections of 15 U.S.C.) (hereinaer “JOBS Act”).

36 1.6.2.2 Costs of the rm

Under the rm allocation, there is typically no direct ow of information between the decit and surplus agents. All communication is instead routed through the rm. As a result, regulation typically takes eect at the level of the rm, rather than the individual transaction level. While this can lead to higher xed costs, it can greatly reduce the marginal costs of transactions allocated through the rm structure. By channeling the information ow through the rm, which reports on transactions in the aggregate, the rm can control and adjust the ow of information more dynamically. In particular, the absence of disclosure requirements at the transaction level can enable rms to optimize on the scope of information collected and foster the use of technological means of delivery. In addition, the rm allocation may be beer at reducing information mis-specications typically encountered under security laws, in particular the overproduction aw and the underproduction aw.

1.6.2.2.1 Regulation of rm disclosures

e regulation of mandatory disclosures under the rm allocation is heterogeneous and can vary signicantly between dierent industries. ere exist two principal modes of regulation:

• Firm-specic regulation model: Under this model, the rm’s activities are exempted from security laws, but they are instead subjected to industry-specic regulations, such as banking or insurance regulations. rough such regulations, rms are typically required to make mandatory disclosures to an industry-specic regulatory body. Such disclosures are typically on an aggregate basis, rather than on a single transaction basis. In addition, the industry-specic regulation may also govern the form and substance of communication between the rm and its stakeholders.

• Full exemption model: Under this model, the rm’s activities are exempted from security laws and there exists no alternative set of regulations to capture the specic kind of transactions the rm engages in. Under this model, the rm operates mostly in the shadow of security laws, being subject to no or only minimal mandatory disclosure obligations. Disclosures by the rm towards its stakeholders are instead governed on a bilateral, contractual basis.

1.6.2.2.2 Chapter 2: Disclosure regulation under the venture rm allocation

In the sphere of startups and venture capital, chapter 2 provides a vivid example of the ‘full exemption model’ of the regulation of rm disclosures. In particular, allocation through the venture rm entails multi-layered exemptions from security laws, both at the level of the individual startup and at the level of the venture rm. As a result, the chapter nds that mandatory disclosure obligations can be avoided throughout the full information chain between decit agents (startup) and surplus agents (limited partners of venture rms as ultimate economic beneciaries). However, it is found that in the absence of mandatory disclosure obligations, the industry practice for VC-backed companies and venture rms is to make disclosures on a bilateral, contractual basis.

1.6.2.2.3 Chapter 4: Disclosure obligations in the sphere of credit

In contrast, chapter 4 looks at an example of the ‘rm-specic regulation model’ in the sphere of credit. In particular, industry-specic regulations require banks to periodically disclose information about the originated loans and the bank’s aggregate credit exposure to the bank regulator. It is established that, while this may trigger substantial regulatory costs for the banking rm, these disclosure costs arise at an aggregate level. us, rather than having to make individual disclosures for every single loan and deposit, as would be required under security laws, the bank can report aggregate exposures instead. is rm-level regulation can result in regulatory economies of scale and cost advantages of the rm allocation.

37 1.6.2.3 Comparative pricing

e regulatory costs at the disclosure layer can vary widely depending on industry, country and market. Given this heterogeneity, it is dicult to make a comprehensive cost comparison. While the SEC has previously provided estimates for the concrete costs faced by issuers of equity and debt securities, this is not sucient to conduct a comprehensive cost comparison, let alone to make a general empirical argument. However, from the above legal analysis, it appears that the rm-based allocation may enjoy a comparative cost advantage over the market-based allocation, in particular where the underlying transactions involve liquidity-constrained issuers (such as startups) and small lot-sized transactions (such as MBS or P2P loans). A key reason for this cost advantage of the rm appears to be the level at which disclosure requirements take eect. Under the rm structure, regulation typically takes eect at the rm level. is makes the rm the main subject of regulatory disclosure obligations, if such obligations even exist under the rm allocation (see ‘full exemption model’ above). e rm allocation can thus relieve issuers from the sting of mandated disclosure obligations, which may be critical where issuers have limited nancial resources or the issuance amount is small. Instead, under the rm allocation, the rm reports on an aggregated basis to surplus agents or regulators. While this may lead to comparatively high xed costs of operating the rm, it may also greatly reduce the marginal disclosure costs at the transaction level and as such favor the rm allocation. In contrast, security laws regulate at the transaction level. is means that the decit agent acts as issuer and is directly subjected to regulatory ling requirements. is can impose comparatively high marginal costs of disclosure and information for each individual market transaction. Given that compliance with mandatory disclosure requirements typically requires issuers to engage the services of market-enabling rms or gatekeepers, the threshold size for a market- based transaction appears to be relatively high. is implicit dierential pricing of mandatory disclosure obligations has been recognized in the scholarly literature before. In the early securities regulation research of Easterbrook and Fischel, it has been described as follows:

“Existing rules give larger issuers an edge, because many of the costs of disclosure are the same regardless of the size of the rm or the oering.” 120

A number of other authors have since echoed these concerns121 and have pointed to the potential of anti-competitive eects,122 which this regulatory pricing can have on an intra-issuer basis. e analysis under the eorem developed within this chapter adds to this literature by highlighting the rm-to-market relationship. In summary, the regulatory costs at the disclosure and information layer appears to give rise to situations where a number of transaction segments and/or security issuers could be priced out of the market and into the rm allocation. In chapters 2 and 4 of this PhD thesis, this question has been analyzed in more detail in the context of both equity and credit markets.

1.6.2.3.1 Chapter 2: Comparative pricing in the sphere of technology startups

Under chapter 2, it is found that the allocation through the venture rm provides for substantially lower costs under the disclosure layer, compared to the public market allocation. In particular, the multi-layered exemptions from security laws allow both startups and venture rms to operate largely in the shadows of security laws. While the industry practice is for venture-backed companies and venture rms to make frequent and detailed disclosures on a contractual basis, these

120See Easterbrook and Fischel(1984). 121See J. Schwartz(2012) (‘Moreover, while an increased regulatory burden falls on all rms, the costs tend to be felt most acutely by emerging ones. ese rms tend to be smaller, making the costs loom proportionally larger, and they have not been around long enough to routinize the process.’); Afshar and Rose(2007) (‘Sarbanes-Oxley critics have also pointed to the fact that compliance costs for small public companies appear to be disproportionately aected by the Act. […] is is because the costs of complying with securities laws and Sarbanes-Oxley have an element of xed cost that does not vary proportionally with rm size.’). 122See Ben-Shahar and Schneider(2011) (‘Second, mandated disclosure can have anticompetitive eects. Disclosure costs are substantially “xed costs”; many of them do not vary with the scope of activity or with the frequency of disclosures. ese xed costs – collecting information, draing forms, training employees – are roughly the same for large and small disclosers. is gives larger disclosers an advantage: their burden of disclosure per ”unit” is smaller. is, in turn, hurts small companies trying to enter and compete in the market.’).

38 disclosures tend to be made in a private seing and are perceived to focus on the most relevant metrics. In contrast, founders widely report the costs of public market disclosure obligations as being cost-prohibitive for them. In summary, the analysis in chapter 2 concludes that disclosure costs, despite its substantial recent reforms through the JOBS Act, appear to favour the venture rm allocation over the public market.

1.6.2.3.2 Chapter 4: Comparative pricing in the sphere of credit

e legal analysis for the disclosure layer in chapter 4 concludes that the allocation through the banking rm enjoys a comparative cost advantage over the market allocation. In particular, the fact that banks disclose at an aggregated rm- level to the bank regulator, rather than on an individual loan basis, signicantly decreases the marginal disclosure costs of individual transactions. While subject to regulatory disclosures at the rm level, the banking rm can transact more eciently with decit agents (borrowers) and surplus agents (depositors) on a bilateral, relationship-based basis. Loans originated through the market, on the other hand, are subject to individual transaction-level disclosure obligations, which can be cost prohibitive for issuers. In fact, the analysis reveals that these disclosure obligations may have priced a number of credit transactions, including non-agency ABS, P2P loans and even some corporate bond oerings out of the market.

1.6.3 Application to the investment and liquidity layer

As set out in section 1.4.2, the investment and liquidity layer relates to the ow of funds between decit and surplus agents. Under the rm allocation, funds are routed through the balance sheet of the rm, where they typically remain illiquid.123 In contrast, a theoretical market allocation involves a direct ow of funds between surplus agents and decit agents in primary markets (investors nancing a new securities issuance), and between surplus agents in secondary markets (investors trading already issued securities). In practice, however, markets are heavily intermediated by undwriters and market makers, which act as a transitory (and sometimes more permanent) banking rm allocation. Initiating and maintaining the ow of funds under the respective allocation structure entails substantially dierent regulatory costs, as will be further highlighted below.

1.6.3.1 Costs of the market

Under the market structure, the investment and liquidity layer is subject to a complex system of multiple market- enabling rms. While dening both baseline and regulatory costs at this layer is dicult, it is generally considered to be the costliest layer for issuers accessing the markets. Primary markets Since market-enabling rms (underwriters) take proprietary positions in the issuer’s securities in primary markets, the boundaries between a market-based and a rm-based allocation can oen be blurred. In theory, underwriting activities should be transitory in nature, lasting only for a logical second, as the underwriter intermediates between the issuer and the market. For some assets, such as public stock markets discussed in chapter 2 of this PhD thesis, capital provided by underwriters is indeed transitory in nature, whereas for other assets, such as structured credit markets discussed in chapter 4 of this PhD thesis, capital is provided on a more permanent basis by underwriters. Also, were the originated securities are sold primarily to banking rms, as is oen the case in credit markets, the nal allocation of assets may in substance resemble a traditional bank-based allocation. us, depending on the balance sheet exposure of nancial intermediaries and the surplus agents involved, the costs and regulatory objectives can vary:

• Transitory banking-like rm allocation: Where underwriters act purely as market-enabling rms, the relevant costs of the market are represented by both (i) the underwriting spread, the dierence between the price the underwriter pays and the price at which the securities are oered, and (ii) the underpricing of the oering,

123In the case of credit allocated through the bank, for the term of the credit contract, in the case of private investment rms, for the term of the fund or the sale of the asset.

39 the dierence between the price at which the securities are oered and their fair market value. Such traditional underwriting activities are predominantly regulated by security laws, oen relying on express exemptions under banking regulations.

• Permanent banking-like rm allocation: Where underwriters maintain longer term positions in securities oerings or sell such positions primarily to other banking rms, the costs of such a ‘market’ allocation resemble a rm-based allocation. In particular, they can be decomposed into (i) direct costs, the interest margin between the asset and liability side of the bank’s balance sheet and (ii) indirect costs, such as the potential negative externalities associated with a bank allocation, in particular systemic risks. As a result of the overlap in activities, both security laws and banking laws can aect such hybrid allocation structures.

e fact that the lines between the rm and the market are blurry at this layer challenges the strict rm-vs-market distinction under the eorem and makes it more dicult to disentangle the ‘pure’ costs of the market. Secondary markets Similar to primary market activities, market making in secondary markets requires broker-dealer rms to take positions in the issuer’s securities. Although market making is by its nature more transient in nature, the same problem that exists for primary market activities, also carries over to secondary markets. Namely, the lines between a rm and a market allocation are blurred and it becomes very dicult to disentangle the actual costs of a market allocation. In contrast, the role and costs of securities exchanges in secondary markets are more clear cut: they provide a market infrastructure for trading and do not take any proprietary positions. us, the costs of listing on regulated exchanges is a clearly identiable cost item.

1.6.3.1.1 Investment and liquidity layer under U.S. security laws

e most basic regulatory cost that securities regulation places on market participants at the investment and liquidity layer relates to the fact that surplus and decit agents are required to route their transactions through SEC-regulated broker dealer rms. In other words, security law regulates and limits the available transaction channels and thereby places regulated market-enabling rms in a privileged position. Indeed, if one looks at the legislative history of security laws at the federal level, it appears that underwriters were a main driving force behind their original adoption. In particular, it appears that it was mainly ‘high-prestige wholesale investment banks’ who wanted to have strict security laws in place in the 1930, such that they could be shielded from competition by retail banks.124 Under U.S. securities regulation, the investment and liquidity layer is regulated in an entity-centric manner.125 is means that, unlike at the disclosure layer, where the transaction or the investment contract is the regulatory object and category of analysis, security laws at the investment and liquidity layer focus on the individual market-enabling rms. Under securities regulation, underwriters must register as ‘brokers-dealers’ and nancial exchanges as national securities exchanges.126 A broker is a ‘person engaged in the business of eecting transactions in securities for the account of others’.127 us, brokers intermediate on a commission basis, not taking the securities on their own books in the process. In contrast, a dealer is ‘any person engaged in the business of buying and selling securities for his own account, through a broker or otherwise’.128 us, an underwriter, by buying the shares from the issuer, acts as a dealer. Broker-dealers are subject to a range of general conduct regulations, such as antifraud provisions,129 and extensive

124See Mahoney(2001) (‘e Securities Act of 1933 is a severe testing ground for rent-seeking theories of economic regulation. […] e statute itself was draed in haste and secrecy, and its principal drasman, James Landis, was an academic reformer interested in establishing greater public control over nance. […] e Securities Act did, however, produce winners as well as losers within the investment banking industry. […] One important consequence was to protect high-prestige wholesale investment banks, and the retail dealers who sold on their behalf, from competition by integrated wholesale/retail investment banks that had gained market share in the late 1920s. […] e prior literature on the origins of the Securities Act ignores the question whether the statute created rents for the regulated industry.’). 125See Krug(2013) (‘If the entity is the marketplace actor, then, logically, the entity should be the regulatory subject.’). 126Securities Exchange Act of 1934 section 6, 15 U.S.C. § 78 c-f. 127Securities Exchange Act of 1934 section 3(a)(4)(A), 15 U.S.C. § 78c. 128Securities Exchange Act of 1934 section 3(a)(5)(A), 15 U.S.C. § 78c. 129For example, Securities Exchange Act of 1934 section 9(a), 15 U.S.C. § 78i, prohibits a range of manipulative practices with respect to securities registered on a national securities exchange. Similarly, Securities Exchange Act of 1934 section 10(b), 15 U.S.C. § 78j, prohibits the use of ”any manipulative or deceptive device or contrivance” in connection with the purchase or sale of any security.

40 nancial responsibility regulations, such as the net capital rule.130 As the nature of underwriting activities is to route new issuances through the broker-dealer’s balance sheet, the lines between this being a market-enabling function or a banking operation are blurry. In the past, this dual nature has been reected by regulation, which has arguably meandered between separating and merging underwriting activities with banking operations. e Glass–Steagall Act of 1933131 can be seen as an early regulatory eort in this respect. It set out to clearly separate commercial banking activities from investment banks’ underwriting activities. e main objective of this separation was to prevent banks from taking proprietary positions in the underwrien securities and thus to clearly distinguish market-enabling functions from banking activities. Starting in the 1980ies, the SEC took a number of decisions, which (by its own admission) blurred the lines between the banking and the securities industry.132 By the end of the 1990ies, as Citibank acquired the broker- dealer rm Salomon Smith Barney (through Traveler’s Group) in 1998, the rigid divide between banking operations and market-enabling rms had been eroded. Shortly thereaer, in 1999, almost a decade before the global nancial crisis of 2008, the Glass-Steagall Act was formally repealed by the Gramm–Leach–Bliley Act.133 In the aermath of the nancial crisis, some of Glass-Steagall Act provisions were reinstated through the Volcker Rule134 in the 2010 Dodd-Frank Act,135 thereby aempting to once again separate market-enabling functions from banking. More specically, with respect to regulating more transient and more permanent forms of underwriting, the following can be said:

• Transitory underwriting: while ‘pure’ broker-dealer rms, which fall under the exclusive supervision of the SEC, have ceased to exist aer the nancial crisis of 2008,136 such underwriting practices are still predominantly regulated through securities regulation. Under the Volcker rule, there is an explicit exemption for underwritings that are transitory in nature. Bank entities acting as ‘underwriters’ for the ‘distribution’ of securities or as ‘market makers’ in the secondary markets are permied to take proprietary positions that do not ‘exceed the reasonably expected near term demands of clients, customers, or counterparties’.137 For initial public oerings (IPO) in equity markets, there further exists a long-established industry practice that prohibits broker-dealer rms to take more permanent proprietary positions in the equity oerings. FINRA rule 5130 holds that underwriter rms may not purchase a new issue in which the underwriter has a benecial interest, except for ‘sticky securities’. Sticky securities are those which the underwriter is unable to sell to the public.

• Permanent bank-like allocation: prior to the nancial crisis, more permanent underwriting positions taken by ‘pure’ broker-dealer rms were subject to SEC supervision only, in particular the net capital rule.138 Although controversial, it has been argued that the net capital rule reforms of 2004 have allowed investment banks to substantially increase their proprietary positions in the run up to the GFC.139 As ‘pure’ broker-dealer rms no longer exist, such ‘permanent’ underwriting positions are now subject to the capital adequacy regulations of banks, in particular the limitations on proprietary positions under the Volcker rule. 130Rule 15c3-1, 17 CFR § 240.15c3-1. is rule can be understood as the SEC’s version of the banking regulators’ capital adequacy requirements. e rule requires a broker-dealer to maintain enough liquid assets to promptly satisfy customer claims, even if the broker-dealer should go out of business. 13112 U.S.C. 227. 132See SEC(1983) (‘During the past year, the blurring of traditional boundaries between the banking and securities industries has resulted in the Commission’s taking various positions on Glass-Steagall.’). 13315 U.S.C. § 6801 et seq. 13412 U.S.C. 1851. 135Dodd-Frank Wall Street Reform and Consumer Protection Act, Pub. L. 111-203, §§ 1001-1100, 124 Stat. 1955-2113 (21 July 2010), 7 codied at 12 U.S.C. §§ 5301, 5481-5603. 136Having either (i) declared bankruptcy (Lehman Brothers) (ii) merged with a bank holding company (Bear Stearns/JPMorgan, Merrill Lynch/Bank of America) or (iii) opted to become bank holding companies (Goldman Sachs, Morgan Stanley). 13712 U.S.C. §1851 (d). 138Rule 15c3-1, 17 CFR § 240.15c3-1. 139See Beccalli, Boitani, and Giuliantonio(2015) (‘Finally, when analyzing changes in regulation, on the one hand our data conrm the common view that the 2004 SEC new net capital rule strongly increased the level of formal leverage of investment banks.’); Lo and Mueller(2010) (questioning the impact of the net capital rule ‘ ese reports of sudden increases in leverage from 12-to-1 to 33-to-1 seemed to be the “smoking gun” that many had been searching for in their aempts to determine the causes of the Financial Crisis of 2007–2009. […] While these “facts” seemed straightforward enough, it turns out that the 2004 SEC amendment to Rule 15c3–1 did nothing to change the leverage restrictions of these nancial institutions.’).

41 1.6.3.1.2 Chapter 2: Investment and liquidity costs of the market in the sphere of technology startups

At the investment and liquidity layer, chapter 2 looks at the costs of technology startups accessing capital markets through (i) traditional initial public oerings (IPO) or through (ii) novel equity oerings under the JOBS Act. Traditional tech IPOs are typically underwrien on a rm-commitment basis, with the balance sheet position taken by underwriting rms being transitory in nature. While this provides price certainty to the issuer, it is found that there exists signicant ‘underpricing’, whereby technology startups going public ‘leave money on the table’. Such underpricing results in a wealth transfer from the issuer to surplus agents (preferred institutional investors with an early allocation of IPO shares) and may be a deterrent for rms raising capital. In contrast, for new oerings under the JOBS Act (Reg A+ and Reg CF oerings), these oering are typically not underwrien, but marketed through funding platforms. While this may reduce underpricing, it reduces certainty with respect to the size of the nal oering amount and places signicant marketing and distribution costs on the issuer. In summary, the main cost driver at this layer appear to be baseline costs, rather than regulatory costs.

1.6.3.1.3 Chapter 4: Investment and liquidity costs of credit markets

Chapter 4 explores the costs of credit markets at the investment and liquidity layer, in particular with respect to bond and asset-backed securities (ABS). It is found that primary corporate and government bond markets operate similar to equity markets, with the nature of the underwriting traditionally being transitory in nature. For primary ABS markets, it is found that primary markets are substantially more complex, with ‘underwriting’ occurring both at the loan level and at the pool level. Furthermore, underwriting practices in the syndicated credit markets in the time period prior to the global nancial crisis of 2008 are discussed in more detail. In particular, it is highlighted how the transitory originate-to-distribute underwriting model of broker-dealer rms transitioned to a more permanent bank-like allocation over time and how this has aected underwriting activities under both banking and security laws in the aermath.

1.6.3.2 Costs of the rm

e investment and liquidity layer fundamentally dierentiates the rm allocation from the market allocation. e essence of rms under Coase’s notion of commitment costs (see section 1.6.1.1.2), is their ability to make idiosyncratic, long-term investments in illiquid assets. In contrast, short-term liquidity is a dening characteristic of markets. What allows the rms to make such long-term, illiquid investments, is their ability to contract simultaneous with surplus agents and decit agents at dierent terms. Agents transact ‘through the cloak’ of the rm. Surplus agents may provide short-term debt to rms, but rms can use these funds to nance long-term projects. e role of the rm as a central contracting party provides it with transactional eciencies, which can benet agents on both sides of the rm’s balance sheet:

• Firm eciencies dealing with surplus agents: when raising funds from investors or creditors on the liability side of their balance sheet, rms do not have to pre-specify particular assets or projects that will eventually be nanced and they can accept funds long ahead of actually deploying capital. Banks and investment rms regularly pre-raise funds from depositors and investors on a blind pool basis without specifying the nature and risks of the nanced projects that are eventually nanced. Surplus agents are diversied through the rm and must not select and monitor individual projects, but can rather assess the viability of the rm in aggregate.

• Firm eciencies dealing with decit agents: when investment opportunities arise on the asset side of their balance sheet, the ability to raise funds in advance, provides rms with readily available ‘dry powder’ to deploy. Once an investment has been made, the rm structure allows the investment to sit within the rm structure for years. is illiquidity is a distinctive feature of the rm and can oen be perceived favorably by decit agents. e absence of a secondary market through which the identity of surplus agents (investors or creditors) constantly change, provides stability and ‘calmness’ to the rm allocation. In a bank allocation, loans are oen held on the banking book until maturity, allowing the individual borrowers to form a long-term relationship with the

42 ‘house’ bank. Similarly, venture funds may hold their investments for decades, acting as long-term supporters and champions of founders and startups.

1.6.3.2.1 Investment and liquidity regulation under the rm structure

Firm-specic regulation at the investment and liquidity layer can vary considerably, depending on the nature of the rm and its assets and liabilities. In particular, under the rm allocation, there oen exists a mismatch of maturities, which may warrant industry-specic regulation of the rm’s capital structure. is maturity mismatch is directly related to the illiquid nature of assets under the rm allocation. Broadly speaking, one can distinguish between the following:

• Maturity-matched balance sheets: Where the nature of the rm’s balance sheet is such that it closely matches the maturity of its assets with its liabilities, regulation tends to be light-touch. An example of this are SEC- exempted investment rms, such as private equity funds (long-term assets, long-term liabilities) or hedge funds (short-term assets, short-term liabilities). Where the asset-liability structure is closely matched, the risks of creditor runs are mitigated through contract design. is reduces the need of additional regulation to mitigate potential negative externalities. In this respect, chapter 2 discusses venture capital rms, which largely escape securities regulations and are not subject to an industry-specic regulator.

• Maturity mismatched balance sheets: In contrast, where a rm’s balance sheet shows a considerable maturity mismatch in the asset-liability structure of its balance sheet, regulation tends to be high-touch. Examples of this are the banking industry (long-term loans/short-term deposits) and the insurance industry (short-term premiums/long-term claims). For banks, credit contractions on the liability side or credit shocks on the asset side can result in bank failures and systemic risks for the wider nancial system. us, industry-specic regulation is required to put limits on the extent to which rms can mismatch maturities. For example, through capital adequacy regulations in the banking industry, industry-specic regulation limits the extent to which these rms can engage in maturity transformation. In this respect, chapter 4 shows a range of regulatory tools that industry-specic bank regulation uses to mitigate this problem.

1.6.3.2.2 Chapter 2: Investment and liquidity costs of the venture rm

Under the investment and liquidity layer in chapter 2, the costs of the venture rm allocation in the sphere of technology startups are explored in more detail. Financing under the venture rm structure takes place at two levels, the venture rm level and the startup level, both of which operate in the shadow of securities regulation through extensive exemptions. At the level of the venture capital rm, fund raising occurs well ahead of the actual capital deployment and without specifying the nature of the future investments. is pre-nancing mechanism on a blind pool basis gives the venture fund an advantage in terms of speed of execution, once it has identied a concrete investment opportunity. On the other hand, at the level of the startup, nancing occurs over multiple rounds, which allows the startup founder to actively time the raise around a period in which the company has hit a particular milestone or achieved a certain level of traction in the market. In summary, it is found that the private nature of startups and venture rms provides a distinct set of both contracting and costs advantages.

1.6.3.2.3 Chapter 4: Investment and liquidity costs of the banking rm

In chapter 4, the cost elements of credit that is allocated through the banking rm are explored further. In the absence of ‘primary markets’, ‘primary activities’ are referred to as the manner in which the banking rm can nance its operations by mediating between credit suppliers and borrowers. In this respect, aention is drawn to the legal advantage of the banking rm to ‘pre-raise’ funding through deposits – without specifying the nature and the risks of the loans that are nanced with such deposits (‘blind pool’ basis). Furthermore, in the absence of ‘secondary market’ liquidity of the bank’s assets and the risks associated with the bank’s maturity transformation, it is highlighted how banking regulations require the rm to comply with a wide range of regulatory provisions. In particular, ex-ante regulatory tools, such as

43 capital buers, liquidity reserves and private bail-in instruments, are distinguished from ex-post regulatory tools, such as recovery and resolution plans or public bail-in measures. In summary, it is found that transacting through the banking rm structure comes with a range of regulatory privileges, but also substantial costs.

1.6.3.3 Comparative pricing

Comparing the costs of a rm allocation with the costs of a market allocation at the investment and liquidity layer is arguably the most dicult among all layers. To begin with, the role of underwriters, both in primary and secondary markets, oen blurs the lines between a market-based and a rm-based allocation. Underwriter activities may at the same time be subject to securities and banking laws. As a result, the direct costs of the underwriting spread, which scholars and the SEC sometimes use to quantify the direct costs of this layer, may provide an inaccurate proxy. Similarly, clearly identifying the costs at the rm level can be dicult. While maturity matched investment rms have clearly identiable fee structures, banking rms are regulated by a range of capital adequacy measures that make it dicult to make an overall cost assessment.

1.6.3.3.1 Chapter 2: Comparative pricing in the sphere of technology startups

Under chapter 2, the comparison of costs at the investment and liquidity layer revolves around the costs of obtaining liquidity through traditional tech IPOs with the costs of obtaining liquidity through venture capital nancings. With respect to IPOs, the chapter nds that both industry practitioners and academics have reported that companies going public oen ‘leave money on the table’, in particular through signicant underpricing. In contrast, the chapter nds that nancing through the venture rm oers companies some distinct advantages. At the level of the venture rm, money is pre-raised on a blind pool basis, allowing the rm to execute quickly in future nancings. At the level of the startup, nancing through multiple rounds allows the company to map funding to company progress. Furthermore, with the rise of so-called ‘secondaries’, founders can now oen get signicant liquidity, while still remaining private. In this respect, it is found that companies and founders over the last decade appear to have developed a clear preference for venture nancing over market-based nancing at the investment and liquidity layer. is preference seems to be rooted mainly in baseline cost factors, including the secular availability of venture capital, the leniency of terms that can be negotiated with venture rms and the speed of execution. While regulatory costs clearly aect venture capital formation and deployment of capital, they do not appear to be the most signicant cost driver at this layer.

1.6.3.3.2 Chapter 4: Comparative pricing in the sphere credit markets

At the investment and liquidity layer, chapter 4 compares the costs of bank-based nancings with market-based nancings. For credit markets, the heterogeneity across credit assets and the complex role of underwriters makes it dicult to disentangle regulatory costs from baseline costs, let alone compare them to a rm-based allocation. For some market- based credit assets, such as corporate bonds, the role of underwriters seems to be that of a transitory bank allocation. On the other hand, the chapter shows that in the timeframe before the nancial crisis of 2008, more complex structured credit securities (such as ABS, MBS or CDOs) have sometimes remained on the underwriters’ balance sheets for prolonged periods of time. In those cases, the observed allocation in eect converged to a bank-based allocation. e chapter shows how the regulation of such proprietary trading positions under security laws, in particular through the net capital rule, may have, for a short period of time, provided for a lower cost of capital for some broker-dealer rms compared to a bank-based allocation. As a result of this historical episode, all ‘pure’ broker-dealers rms have since either (i) declared bankruptcy (Lehman Brothers) (ii) merged with a bank holding company (Bear Stearns/JPMorgan, Merrill Lynch/Bank of America) or (iii) opted to become bank holding companies (Goldman Sachs, Morgan Stanley). As a result, credit underwriting activities and proprietary positions held in such underwritings are now governed in parallel by banking laws and security laws, making it very dicult to identify price dierentials between a rm and a market allocation.

44 1.6.4 Application to the diversication layer

As set out in section 1.4.3, the diversication layer relates to the optimal parceling of the investment by the surplus agent. Under the market allocation, a range of specialized market-enabling rms allow surplus agents to pool their funds with each other and invest them across a wide range of decit agents. In contrast, under the rm structure, diversication is governed within the rm and oen cloaked by the ‘veil of the corporation’.

1.6.4.1 Costs of the market

When it comes to the costs of the market under the diversication layer, the assignment of costs diers substantially from both the disclosure and the investment layer, as the costs are borne by surplus agents (investors), rather than decit agents (issuers of securities). rough market-enabling investment rms and pooling vehicles, including mutual funds, exchange-traded funds (ETFs), pension funds and structured credit vehicles (such as ABS, MBS, CLOs or CDOs), the idiosyncratic risk exposure of investors is parceled out across a large portfolio of decit agents. e costs of this layer can be quite substantial and have been subject to much scrutiny.140 Like for all functional layers, the costs incurred at the diversication layer can be distinguished between baseline and regulatory costs. Baseline costs include a range of costs, including the operational and disclosure costs associated with the fund management. Within the scope of this theory, the main focus lies on the following set of baseline costs:

• Investment scope: e broader the investment scope, the more closely the portfolio will be able to reect Markowitz’s ‘hypothetical market portfolio’.141 e larger the investment scope, the higher the baseline costs associated with making and administering investments. As Markowitz(1952) has pointed out, optimal diversication not only means holding a large number of securities within a specic industry or market segment, but also to diversify across industries and markets.142 In this respect, this PhD thesis pays particular aention to the extent to which market-enabling rms allow investors to diversify across both (i) public markets and (ii) privately held companies and assets.

• Active management: As we have seen in section 1.4.3, dominant streams of the nance literature view the passive Markowitz market portfolio as the mean-variance ecient allocation of investor’s wealth. In contrast to this theoretical nding, a large part of the population of market-enabling rms at the diversication layer engage in active portfolio management.143 Such active management costs are a primary source of baseline costs at the diversication layer.

• Diversication stack: Market-based diversication can further take place over multiple intermediary layers. For example, a pension fund may invest in another pooling vehicle, such as a mutual fund. is means that there exist multiple diversication cost layers between the issuer and the ultimate investor. e ‘diversication stack’ refers to the sum of the market-enabling rms placed between the investors and issuers. e larger the diversication stack, the higher the costs at the diversication layer.

In the below sections, a high-level overview of these baseline costs typically associated with mutual funds, ETFs and pension funds, as well as the regulatory costs is provided.

140See, for example, Ayres and Curtis(2015) citing Kwak(2013) (‘Critics have noted that many 401(k) plans include mutual funds with relatively high fees. Since investors in retirement plans are limited to choosing from the menu oered by their employers, high-cost funds in the menu can greatly aect the performance of a retirement account. e stakes are high: reforms that reduce fees incurred by investors by only ten basis points on average would save more than $4.4 billion annually, and these savings compound over the course of investors’ careers.’). 141See Markowitz(1952) and Markowitz(1959). 142See Markowitz(1952) (‘e adequacy of diversication is not thought by investors to depend solely on the number of dierent securities held. A portfolio with sixty dierent rail- way securities, for example, would not be as well diversied as the same size portfolio with some railroad, some public utility, mining, various sort of manufacturing, etc. e reason is that it is generally more likely for rms within the same industry to do poorly at the same time than for rms in dissimilar industries.’). 143See Stout(2003) (explaining how investors obviously do not follow the EMH ‘If they did, it is hard to explain why most mutual funds are actively managed.’).

45 1.6.4.1.1 Mutual funds and ETFs

Both mutual funds and exchange-traded funds (ETFs) oer highly diversied investment exposure to public equity and xed-income markets. By pooling capital with many investors, these diversication vehicles allow the individual investor to own a fraction of a larger, well-diversied portfolio. With respect to the main sources of baseline costs, the following can be noted:

• Investment scope: Open-ended mutual funds and ETFs invest predominantly in publicly traded securities, such as stocks and bond. However, as will be explained further below, security laws do not restrict these investment rms from also investing in privately held securities. For example, as shown in chapter 2, an increasing number of mutual funds now invest a portion of their assets on a crossover basis also into shares of privately held late stage startups. Similarly, as shown in chapter 4, many xed-income ETFs invest in illiquid asset- and mortgage-backed securities (ABS and MBS).

• Active management: Mutual funds aempt to outperform a passive index-tracking strategy and earn a positive alpha – an abnormal rate of return on a security or portfolio in excess of what would be predicted by the capital asset pricing model (CAPM). ere is a longstanding debate in the nance literature, on whether mutual funds are able to outperform a passive indexation strategy, with the vast majority of research indicating that they are unsuccessful in this regard.144 Even if alpha may temporarily exists, most of it can be aributed to luck rather than to superior stock-picking abilities of the fund managers.145 Furthermore, when true alpha is identied, this does not appear to persist over time.146 In contrast, exchange-traded funds (ETFs) are passive investment vehicles, which provide exposure to a specic market index. As such, they are more closely aligned with the ndings of the EMH and the CAPM.

• Diversication stack: Mutual funds and ETFs can be seen as siing somewhat in the middle of the ‘diversication stack’. While most of their investments in public securities are directly at the ultimate issuer level for stocks and bonds, they can also invest in structured credit vehicles, such as ABS and MBS securities, which add a further diversication layer.

Comparing the baseline costs between the two, it can be noted that exchange-traded funds (ETFs) avoid the costs of active portfolio management and oer a quintessential passive indexation, which is closely aligned with what is dictated by the nance theory. As a result, they typically oer investors lower fees147 and a higher degree of transparency with respect to their portfolio allocation. Furthermore, secondary trading in ETFs can reach high liquidity level, which can even exceed that of the underlying basket stocks.148 Against this backdrop, ETFs have experienced rapid growth over the past decade, leading to an overall trend of ‘passive indexation’ over ‘active management’ at the diversication layer.149

144See M. C. Jensen(1968) (‘e evidence on mutual fund performance discussed above indicates not only that these 115 mutual funds were on average not able to predict security prices well enough to outperform a buy-the-market-and-hold policy, but also that there is very lile evidence that any individual fund was able to do signicantly beer than that which we expected from mere random chance’); Daniel, Grinbla, Titman, and Wermers(1997) (‘Showing that managers may be able to select stocks that outperform the average stock, but that they do not seem to outperform a passive strategy with respective to timing portfolio weightings ‘Our results show that mutual funds, particularly aggressive-growth funds, exhibit some selectivity ability, but that funds exhibit no characteristic timing ability.”). 145See Barras, Scaillet, and Wermers(2010) (‘Most actively managed funds provide either positive or zero net-of expense alphas, puing them at least on par with passive funds. Still, it is puzzling why investors seem to increasingly tolerate the existence of a large minority of funds that produce negative alphas, when an increasing array of passively managed funds have become available (such as ETFs).’). 146See Carhart(1997) (‘Persistence in mutual fund performance does not reect superior stock-picking skill. Rather, common factors in stock returns and persistent dierences in mutual fund expenses and transaction costs explain almost all of the predict- ability in mutual fund returns. Only the strong, persistent underperformance by the worst-return mutual funds remains anomalous.’). 147In particular, unlike mutual funds, ETFs do not carry 12b-1 fees (annual marketing and distribution fees) or load fees (sales charges or commissions). Also, see Leau and Madhavan(2018) (‘e ETF structure also enables lower fees than traditional active mutual funds. Since mutual funds interact directly with investors they accrue distribution and record-keeping costs. Indeed, mutual funds may levy fees (such as transfer agency fees or 12b-1 fees that compensate the fund for distribution and service) that ETFs do not, raising the cost to own mutual funds.’). 148See Ben-David, Franzoni, and Moussawi(2018) (empirically analyzing and comparing the liquidity of ETFs and basket stocks and nding that ‘Along all three dimensions, the average ETF is signicantly more liquid than its basket stocks. In particular, the bid-ask spread is lower by about 20 bps.). 149See Ben-David et al.(2018) (‘To illustrate the growing importance of ETFs in the ownership of common stocks, we present descriptive statistics for the SP 500 and Russell 3000 universes in Table I. For SP 500 stocks, the average fraction of a stock’s capitalization held by ETFs has risen almost 50-fold, from 0.14% in 2000 to 7.05% in 2015.’).

46 1.6.4.1.2 Pension funds

Pension funds act as the primary market-enabling rms, which investors rely on – out of their free will or by way of government action150 – for the diversication of their long-term retirement savings. It can be distinguished between dened contribution (DB) pension funds,151 which specify a pension income for the surplus agent, and dened benet (DB) pension funds, which specify a periodic savings contribution.152 With respect to the main sources of baseline costs, the following can be noted:

• Investment scope: With respect to the investment scope, there exist signicant dierences between DB and DC pension funds. Dened benet plans make investments in a very wide range of asset classes, ranging from public securities, to real estate, to private equity and venture capital rms. is breath at the investment scope allows them to be well-diversied across both private and public markets. In comparison, dened-contribution plans, such as the most common 401(k) plans, oer the plan beneciaries a menu of mutual funds or ETFs to choose from. Since the laer are predominantly invested in public market securities, diversication of DC plan participants with respect to private companies and assets is arguably much smaller. Over the past decades there has been a macro-level shi from the allocation through DB plans to DC plans.153 As a result of this shi, it can be expected that the average exposure of retirement savings to private securities and illiquid asset classes, such as venture capital rms, has substantially decreased over the past decades.

• Active management: Just like the active management of mutual funds, the ability of pension fund managers to outperform a passive market portfolio has been scrutinized through a number of empirical studies. Both Ippolito and Turner(1987) and Lakonishok, Shleifer, and Vishny(1992) have found that pension plan’s active portfolio management has failed to outperform a passive index investment in the S&P 500 index.154 In a same vein, Blake, Lehmann, and Timmermann(1999) have empirically found both an average negative stock market selection ability, as well as a negative market timing ability for a sample of pension fund managers.155 In contrast, Coggin, Fabozzi, and Rahman(1993) have found a signicantly positive ability of pension fund managers to select assets, although their timing ability was found to be negative.156 Similarly, Tonks(2005) has found that pension fund managers in his sample generated consistent abnormal returns above a passive indexation strategy.157 us, the data on the positive value provided by the active portfolio management of pension funds appears to be inconclusive. However, from a nance theory perspective, there exist no fundamental reasons why pension fund managers should be able to outperform the market, other than through their ability to invest in a broader market portfolio.

• Diversication stack: Pension funds can be seen as siing very much on top of the ‘diversication stack’. Most

150See Bubb and Pildes(2014) (discussing ‘paternalistic’ regulatory measures relating to retirement savings, such as the switch from opt-in to opt-out programs of DC plans, which are rooted in behavioral law and economics). 151See Zelinsky(2004)(‘A dened benet pension, as its name implies, species an output for the participant. Traditionally, such plans dened benets for particular employees based on the employees’ respective salary histories and their periods of employment. us, for example, a prototypical dened benet formula species that a participant is entitled at retirement to an annual income equal to a percentage of her average salary times the number of years of her employment with the sponsoring employer.’). 152See Zelinsky(2004)(‘In contrast, a dened contribution arrangement, as its equally apt moniker indicates, species an input for the participant. Commonly, the plan denes the employer’s contribution for each participant as a percentage of the participant’s salary for that year. Having made that contribution, the employer’s obligation to fund is over because the employee is not guaranteed a particular benet, just a specied input. In a dened contribution context, the participant’s ultimate economic entitlement is the amount to which the dened contributions for her, plus earnings, grow or shrink.’). 153See Zingales(2009) (‘In 1975, the value of privately held pension assets represented only 18% of the gross domestic product (GDP) and 70% was represented by dened benet plans, which did not directly expose workers to nancial market risk; today, pension assets represent 60% of the GDP, 70% of which is in dened contribution plans and thus exposed to nancial market risk.’). 154See Ippolito and Turner(1987) (Over the 1977-83 period, approximately 1,500 private pension plans led complete 5500 forms with the Internal Revenue Service; the forms provide hitherto unavailable data on the performance of entire pension portfolios. A CAPM-based analysis of the data reveals that, net of investment fees and turnover expenses, private pension plans underperformed the S&P500 by approximately 44 basis points per year.’); Lakonishok et al.(1992) (‘On average, the equity portion of a representative fund has underperformed the S&P 500 by 1.3 percent per year.’). 155See Blake et al.(1999) (‘[…] the bulk of the selectivity measures are both economically and statistically small in absolute value, with more negative than positive estimates. Moreover, the vast majority of funds have negative market-timing estimates, however measured.’). 156See Coggin et al.(1993) (‘is paper presents an empirical examination of the selectivity and market timing performance of a sample of U.S. equity pension fund managers. Regardless of the choice of benchmark portfolio or estimation model, the average selectivity measure is positive and the average timing measure is negative.’). 157See Tonks(2005) (‘We found evidence of signicant persistence in the performance of fund managers at the 1-year time horizon using a number of dierent consistency tests, as well as weaker evidence of persistence at longer time intervals.’).

47 of their investments are made through mutual funds and ETFs, which can themselves be invested in other pooling vehicles, such as ABS and MBS securities. As such, investments made through pension funds can be routed through a multi-layer, costly diversication stack.

Given their nature as general retirement savings vehicles at the top of the ‘diversication stack’, pension funds can oer tremendous diversication across both private and public markets. In particular, given the abilility of dened- benet funds to invest widely across both liquid and illiquid asset classes, they can be diversied more broadly than ETFs in terms of investment scope. However, this also comes at signicant costs: the research on the value provided through active portfolio management is inconclusive, disclosures are typically much less granular in nature and liquidity for plan participants tends be poor as participation is typically linked to employment.

1.6.4.1.3 Diversication layer under U.S. security laws

Market-enabling rms are subject to their own set of securities regulations or rm-specic regulations, which in addition to the costs encountered by the single issuers at the disclosure and investment layer, adds a further layer of compliance costs to the allocation through the market. While securities regulation does not otherwise distinguish between dierent kinds of rms,158 the diversication layer has carved out a special set of regulations that Morley(2018) posits was developed to divide the client assets from the management more distinctly.159 Mutual funds and ETFs are subject to comprehensive securities regulations, which are set forth in the Investment Company Act160 and the associated SEC rules.161 A rm seeking to sponsor162 a mutual fund or an exchange-traded fund (ETF) is required to rst register the fund entity with the SEC under the Investment Company Act of 1940 (‘Investment Company Act’). is Investment Company Act is mainly directed at the corporate boards of mutual funds and ETFs, which are duciaries of the fund investors, much like under traditional corporate structures. e board performs tasks, such as formally approving the appointment of the fund’s service providers, establishing the fund’s valuation policies, and formally determining whether to authorize new classes of shares.163 e compliance costs of these regulations can be, however, extensive. us, fund vehicles typically need to reach a critical mass of assets under management (AuM) in order to be able to eciently absorb such costs through corresponding management fees. In contrast to mutual funds and ETFs, however, pension funds largely fall outside the scope of U.S. securities regulation. ey are explicitly excluded from the denition of an investment company by section 3(c)(11)164 of the Investment Companies Act and are instead regulated by specic rules and agencies.165 e applicable securities regulations directly or indirectly aect the main sources of baseline costs discussed above:

• Investment scope: Generally speaking, security laws relating to mutual funds and ETFs refrains from actively limiting the scope of investments. However, with respect to the investment rm’s ability to invest in private securities and assets, there exist a number of provisions, which indirectly restrict such investments. Firstly, the principle of ‘redeemability’ is deeply ingrained in the Investment Companies Act and eectively limits the abil-

158Morley(2018) (‘America’s securities laws are generic. We have only a single body of securities law for all types of companies. e two centerpieces of American securities regulation, the Securities Act of 1933 and the Securities Exchange Act of 1934, regulate almost every industry imaginable, from soware making to clothing retail to food service, banking, coal mining, insurance, for-prot higher education, hotels, book publishing, art dealing, and real estate investing.’). 159Morley(2018) (‘e most compelling answer thus turns out to be a surprising feature of investment funds that was only recently identied: their peculiar paern of organization. An investment fund’s tendency to maintain a distinct corporate existence and a distinct set of owners from its management radically transforms how investors relate to their managers and warrants a special body of regulation quite dierent from the rest of American securities law. .’). 16015 U.S.C. §§ 80a-1 to 80a-64. 16117 C.F.R. §§ 270.0-1 to 270.60a-1. 162‘Sponsoring’ generally includes (i) the fund’s formation, (ii) seing up the fund’s arrangements with investment advisers and other service providers, (iii) overseeing the commencement of the fund’s activities and (iv) monitoring the fund’s ongoing operations. 163See Warburton(2013) (detailing the boards of directors’ duties and responsibilities under the Investment Company Act 1940). 16415 U.S.C. § 80a–3(c)(11). 165In particular, where the plan is sponsored by private sector employees the pension plans are regulated by the U.S. Department of Labor (DOL) under the Employee Retirement Income Security Act (ERISA), 29 U.S.C. §§ 1001-1461, or, where it involves a dened benet pension fund, by the Pension Benet Guaranty Corporation under Title IV of the ERISA, 29 U.S.C. §§ 1301-1311. Where the plan is sponsored by public sector employees, it is regulated by the Oce of Personnel Management (OPM) under the Civil Service Retirement Act (CSRA), 5 U.S.C. §§ 8331-8351.

48 ity of funds to make substantial investments in illiquid securities.166 Section 22(e) of the Investment Company Act167 gives investors in open-ended funds a right to demand prompt redemption and compels such funds to make payment on the investor’s redemption request within seven days of receiving the request. In 2017, the SEC has further limited a fund’s ability to invest in illiquid asset by passing rule 22e-4,168 which requires funds to establish a ‘liquidity risk management program’, and rule 30b1-10,169 which requires funds with illiquid investments exceeding 15% of its net assets to le a condential notication to the SEC.

• Active management: Security laws do not regulated the degree at which fund managers have to diversify, let alone the extent to which they should follow the passive indexation strategy suggested by the EMH and the CAPM. Instead, like so many times, securities regulation resorts to disclosure requirements: Section 5(b)(2) of the Investment Company Act classies investment management companies with less than 75% of liquid assets as ‘non-diversied’ and requires such investment management rms to make investor disclosures to that eect.170 Similarily, pension fund managers of ERISA retirement plans are only very vaguely required by statutes171 and case law172 to ‘diversify’,173 securities regulation does not explicitly prescribe diversication.

• Diversication stack: e degree to which mutual funds and ETFs can ‘stack’ diversication layers on top of each other is not regulated directly by security laws. In theory, an open-ended mutual fund could thus make an investment in a private equity fund with a ten year commitment period. However, redeemability requirements discussed above, eectively limit the diversication through the regulation of liquidity.

1.6.4.1.4 Chapter 2: Costs of market diversication in the sphere of technology startups

In the sphere of technology startups, chapter 2 looks at the market-based costs of the diversication layer. With respect to mutual funds and ETFs, it nds that these pooling vehicles provide signicant and very eective diversication across public technology companies. However, due to liquidity restrictions imposed under security laws, their allocation to private technology startups is limited. Nevertheless, it is found that some mutual funds have recently moved into later stage nancing rounds of privately held technology startups. With respect to pension funds, it is found that this has historically been the primary means through which the average investor have participated in pre-IPO technology startups. However, given the structural transition from dened-benet (DB) to dened-contribution (DC) pension funds, the average exposure to the venture asset class is likely to have decreased over the past decades. In summary, it is found that market-based pooling vehicles provide an additional regulatory cost and baseline fee layer under the market allocation. Furthermore, while these diversication vehicles are very eective at diversifying across public securities, they provide investors with very limited exposure to the returns of high growth, privately held startups.

166See SEC(2017) (‘When the Investment Company Act was enacted, it was understood that redeemability meant that an open-end fund had to have a liquid portfolio.5 Since the 1940s, the Commission has stated that open-end funds should maintain highly liquid portfolios and recognized that this may limit their ability to participate in certain transactions in the capital markets.’). 16715 U.S.C. § 80a–22 (e). 16817 C.F.R. § 270.22e-4; SEC(2017) (‘e Commission is adopting new rule 22e-4, which requires each registered open-end management investment company, including open-end exchange-traded funds (“ETFs”) but not including money market funds, to establish a liquidity risk management program.’). 16917 C.F.R. § 270.30b1-10; SEC(2017) (‘e Commission also is adopting new rule 30b1-10 and Form N-LIQUID that generally will require a fund to condentially notify the Commission when the fund’s level of illiquid investments that are assets exceeds 15% of its net assets or when its highly liquid investments that are assets fall below its minimum for more than a specied period of time.’). 17015 U.S.C. § 80a–5 (‘At least 75 per centum of the value of its total assets is represented by cash and cash items (including receivables), Government securities, securities of other investment companies, and other securities for the purposes of this calculation limited in respect of any one issuer to an amount not greater in value than 5 per centum of the value of the total assets of such management company and to not more than 10 per centum of the outstanding voting securities of such issuer.’). 17129 U.S. Code § 1104 (a duciary shall discharge his duties with respect to a plan solely in the interest of the participants and beneciaries and by diversifying the investments of the plan so as to minimize the risk of large losses, unless under the circumstances it is clearly prudent not to do so 172See Marshall v. Glass/Metal Ass’n Glaziers Glassworkers Pension Plan, 507 F. Supp. 378 (1980) (‘Ordinarily the duciary should not invest the whole or an unduly large proportion of the trust property in one type of security or in various types of securities dependent upon the success of one enterprise or upon conditions in one locality, since the eect is to increase the risk of large losses’). 173See Cummins and Westereld(1981) (‘In addition to being generally prudent, the duciary under ERISA is required to select a diversied portfolio. Although the Act does not dene diversication, the weight of expert opinion is that the portfolio must be diversied by holding dierent types of securities. It probably would not be sucient, for example, to invest 100 percent of plan assets in a well-diversied portfolio of common stocks. e rule also can be interpreted as requiring diversication within each major portfolio segment.’).

49 1.6.4.1.5 Chapter 4: Costs of market diversication in the sphere of credit markets

In the sphere of credit, chapter 4 looks at the market-based costs of the diversication layer. With respect to mutual funds and ETFs, it is found that these pooling vehicles provide signicant and very eective diversication for liquid xed-income securities, such as corporate bonds and treasuries. However, due to liquidity restrictions imposed under security laws, their allocation to private credit is limited. Similarly, with respect to pension funds, it is found that these pooling vehicles diversify chiey through public credit funds and oer only limited exposure to privately held credit. Against the backdrop of this under-allocation to private credit by both open-end investment funds and pension funds, it is found that asset-and mortgage- backed securities have emerged over the past decades as pooled vehicles for small lot sized private credit. In summary, it is found that market-based pooling vehicles provide an additional regulatory cost and baseline fee layer under the market allocation. With respect to private credit, this can be particularly signicant, as investments involve a signicant ‘diversication stack’ over multiple intermedation layers.

1.6.4.2 Costs of the rm

In the rm allocation, diversication occurs ‘within’ the rm, more specically on the rm’s balance sheet. is entails, depending on the concrete rm, that neither surplus agent nor decit agent may necessarily be aware of the level of diversication. Diversication can occur behind the ‘veil of the rm structure’ in some cases, or be quite transparent in others. Dierent industry-specic regulations can apply to intra-rm diversication, which either limit or encourage more diversication. For example, as outlined in section 1.6.3.1.1, rm-level diversication restrictions through the Glass-Steagall Act have previously limited intra-rm diversication in the banking industry. Broadly speaking, one can distinguish between two extreme ends of the rm-level diversication spectrum:

• High diversication rms: these are rms that intentionally seek high levels of internal diversication, such as banks and insurances. Depositors of banks are diversied across thousands of borrowers that are impossible for the depositor to understand and track individually. e banking balance sheets of large banking conglomerates represent highly complex structures that make it impossible for both bank borrowers and creditors to see through.174

• High concentration rms: at the other end of the spectrum are rms with high levels of concentration, such as private equity or venture capital rms. ese intentionally take just a few large positions, which are individually disclosed to and tracked by investors. An example of this are venture capital rms, which typically invest in 20-30 startups per fund vintage.

Given the variation between rm-specic diversication levels and industry-specic regulation, it is dicult to make a general statement with respect to diversication costs under the rm structure. For example, a critical aspect that is relevant for the diversication costs of the banking rm, is the presence of what is referred in this thesis as means of ‘legal diversication’:

• Economic diversication: this relates to the (ordinary) diversication through the parceling and pooling of the surplus agent’s investment exposure to multiple decit agents (issuers or borrowers). Such economic diversication can either be through specialized pooling vehicles (mutual funds, ETFs, pension funds) or through a rm’s balance sheet. With a few exceptions, diversication under the market allocation relies exclusively on economic means of diversication.

• Legal diversication: in contrast to economic diversication, legal diversication relates to implicit or explicit government guarantees. ereby, the economic exposure is legally ‘diversied’ to all agents nancing the governmental body. In chapter 5 of this thesis, for example, government guarantees are modeled through future taxes on all surplus and decit agents.

174See Garten(1989) (noting how bank diversication has grown over time ‘Perhaps the most dramatic shi in bank regulation over the last decade has been the extent to which banks have been permied to diversify their activities and investments.’ ).

50 is distinction is particularly relevant for the understanding of banking rms (discussed in chapters 4 and 5) and rms with systemic relevance. For those rms, legal diversication can critically determine the costs of the rm-based allocation structure.

1.6.4.2.1 Chapter 2: Costs of rm diversication in the sphere of technology startups

In the sphere of technology startups, chapter 2 looks at the diversication through the venture capital rm. In this respect, it nds that venture rms intentionally provide low diversication to their investors. Even more so, over the lifetime of the funds, the portfolio allocation oen becomes more concentrated as funds make follow-on, pro rata investments in well-performing companies. Eventually, the performance of a particular venture fund is oen driven by one or two breakout companies. In this respect, it is found that the venture capital exemptions under security laws only provide very vague limits on diversication levels, while contractual provisions in the limited partnership agreements (LPA) between investors and the venture rm may be more specic.

1.6.4.2.2 Chapter 4: Costs of rm diversication in the sphere of credit

In the sphere of credit, chapter 4 looks at the diversication through the banking rm. In this respect, it nds that banking rms provide high diversication levels to their investors and creditors. Economic diversication takes place on the bank’s balance sheet and indirectly provides depositors with exposure to thousands of creditors. Nevertheless, this economic diversication can vary considerable depending on bank size and geographic reach. However, even more important to the banking allocation are means of legal diversication, in particular implicit and explicit government guarantees.

1.6.4.3 Comparative pricing

Comparing the costs of a rm allocation with the costs of a market allocation at the diversication layer is quite dif- cult. Firstly, while diversication under the market structure is both functionally and institutionally separated under a market-based allocation, diversication through the rm generally overlaps with other activities and layers. Sec- ondly, rm-based diversication is subject to industry-specic factors, such as legal diversication mechanisms, which signicantly alters incentives that would be encountered in a market-based allocation.

1.6.4.3.1 Chapter 2: Comparative pricing in the sphere of technology startups

In the sphere of technology startups, chapter 2 looks at the costs of diversication through public fund vehicles and compares it to the costs of the venture rm. While it nds that public fund vehicles are quite eective means of diversifying across public technology rms, security laws restrict them from investing larger amounts in private companies. As a result, public fund vehicles are systematically under-allocated to the high-growth venture asset class. In contrast, the allocation through the venture rm provides lile diversication and is systematically over-allocated to a few breakout companies. While this is benecial to the relationship between the startup and the venture rm, it means that the idiosyncratic risks of individual venture capital rms have to be diversied themselves through larger institutional portfolios.

1.6.4.3.2 Chapter 4: Costs of market diversication in the sphere of credit markets

In the sphere of credit, it is found that market-based diversication vehicles allow for a broad diversication across a range of xed-income securities. rough the rise of structured credit, even traditional bank-based credit is now accessible to investors. However, with only a few exceptions, market-based credit does not benet from legal means of diversication. In this respect, bank deposits have a fundamental advantage over market-based credit. is provides them with cheaper access to capital and gives the rm allocation an systematic advantage over the market.

51 1.7 Second Part of the eorem

1.7.1 Doctrinal Classication

e second layer of the Coase eorem is rooted in the work of both Ronald Coase and Guido Calabresi. As such, it can be considered to be in the vein of both positive and normative law and economics theory. Whereas under the rst layer of the eorem, security laws are considered to be an endogenous transaction cost, under the second layer of the eorem, the costs of the market are re-conceptualized as an externality. is allows us to place securities regulation in the context of the seminal paper ‘e Problem of Social Costs’ by Ronald Coase and oer a Chicago School perspective on the costs of securities regulation.175 From the frictionless environment, we then proceed to an environment of positive transaction costs and explore how the work of Guido Calabresi, in particular his work ‘e Cost of Accidents’,176 can help us in dening an optimal securities regulation regime under a normative law and economics perspective. Whereas the rst layer of the Coase eorem of Securities Regulation can be considered as a high-level policy tool to assess the costs of securities regulation relative to rm-based regulation, the second layer is a more granular policy tool that can guide the cost-benet analysis at the statute level. While both layers carry elements of positive, normative and functional law and economics theory, the eorem as a whole, with its emphasis on institutional mechanism design and individual choice, can be regarded as a functional theory of law and economics.

1.7.2 Substantive provisions

1.7.2.1 eoretical foundation

1.7.2.1.1 ‘e Problem of Social Costs’

e theoretical basis of the second layer of the eorem is the 1960 seminal paper by Ronald Coase ‘e Problem of Social Costs’.177 In this paper, Coase analyses the legal challenge of dealing with negative externalities in the law and addresses the maer in the context of tort law. A classical tort law seing involves a polluter and a pollutee or victim, with the pollution or damage constituting a negative externality. Coase argues that the parties can go to court to see whether the polluter should cease polluting or the victim needs to accept the pollution. In that respect, the core proposition which he makes is that regardless of whether the judge would rule that the polluter has to stop polluting, or that the victim has to put up with the pollution, there exists a mutually benecial bargain that can be reached between the parties which can achieve the same outcome of productive activity as the judicial decision. e consequence of this nding, which was later termed the ‘Coase eorem’ by George Stigler, can be broken up into two propositions:

• eciency proposition

• invariance proposition

e eciency proposition states that in the absence of transaction costs, parties can overcome ineciencies otherwise caused by externalities. In other words, in a regime without transaction costs, the costs involved with the negative externality can be optimally allocated between the parties involved. us, the polluter and the victim can reach an ecient selement without resorting to the law and the judicial system. e invariance proposition goes one step further and states that in a frictionless world, the initial assignment of legal entitlements or obligations does not aect the allocation of resources. In other words, in a frictionless seing, whether the law assigns liability to the polluter or the victim does not aect the eciency of the nal allocation.

175See Coase(1960). 176See Calabresi(1970). 177See Coase(1960).

52 1.7.2.1.2 ‘e Cost of Accidents’

It is clear that the frictionless seing discussed above, is not reective of the real world. Coase himself never considered such a regime without transaction costs to be a realistic regime either. In the ‘e Problem of Social Costs’ he clearly stated that such an assumption ‘is, of course, a very unrealistic assumption’.178 In fact, throughout his lifetime, Coase was personally always at odds with the notion of the ‘Coase theorem’, as he was largely purported to assume such a frictionless environment, when instead he wanted to study a world with positive transaction costs.179 Already in the ‘e Problem of Social Costs’, Coase outlines that the legal assignment of rights in a positive transaction cost environment ought to produce an outcome similar to what would result if the transaction costs were eliminated.180 Hence the law and the judicial system should be guided by the most ecient solution. In other words, Coase urges that the frictionless world should merely act as a guiding principle for seing regulation in the positive transaction cost world. In the later work of Guido Calabresi, specically in his 1970 book ‘e Cost of Accidents’, Calabresi further elaborated on the Coasean tort law seing under a positive transaction cost regime. His book and the propositions made therein can be considered to be in the Yale school tradition of normative law and economics. I want to focus on two salient propositions in his book:

• optimal tort liability proposition

• least-cost avoider proposition

Under what is termed the ‘optimal tort liability proposition’, Calabresi proposes a regime that, either by use of general and specic deterrence, minimizes the sum of the costs of accidents and the costs of avoiding accidents, including the administrative costs of the tort system. Furthermore, under what is termed the ‘least-cost or cheapest cost avoider proposition’, Calabresi proposes to assign the tort liability to the party able to minimize negative externalities most eciently.

1.7.2.2 Of positive and negative externalities

Denitions of economic externalities are ‘few and oen unsatisfactory’.181 Coase referred to negative externalities as ‘actions of business rms which have harmful eects on others’.182 As such, the Coase eorem is oen thought to be applicable only where rms are concerned and where the externality occurs outside of a contractual relationship with the rm.183 In the course of the eorem, a broader denition of economic externalities is relied on. In particular, the denition of Buchanan and Stubblebine(1962), which they established as a reaction to Coase’s original work, to add rigor and precision to the analysis of externalities. Under this more generic denition, an externality exists where the utility of a rm or individual184 is dependent upon activities,185 that are exclusively under his own control or authority, but also upon another single activity, which is, by denition, under the control of a second rm or individual. us, an externality is dened along the dimension of control. Notably, the denition does not set out whether or not a contractual relationship between the involved parties exists: where a contract between two parties exists and one party performs an activity that is outside the control of the other

178See Coase(1960) (‘e argument has proceeded up to this point on the assumption (explicit in Sections III and IV and tacit in Section V) that there were no costs involved in carrying out market transactions. is is, of course, a very unrealistic assumption’). 179See Frischmann and Marciano(2015) (‘Coase always expressed dissatisfaction with neo-classical economics and advocated for a new approach. Rather than using toy mathematical models built from unrealistic, idealized assumptions, Coase preferred to study real-world contexts, including actual legal cases.’). 180See Coase(1960) (‘In these conditions the initial delimitation of legal rights does have an eect on the eciency with which the economic system operates. One arrangement of rights may bring about a greater value of production than any other. But unless this is the arrangement of rights established by the legal system, the costs of reaching the same result by altering and combining rights through the market may be so great that this optimal arrangement of rights, and the greater value of production which it would bring, may never be achieved.’). 181See Scitovsky(1954). 182See Coase(1960). 183See Buchanan and Stubblebine(1962) (‘Strictly speaking, Coase’s analysis is applicable only to inter-rm externality relationship’). 184See Buchanan and Stubblebine(1962) (‘In what follows, “rms” may be substituted for “individuals”). 185e scope of what constitutes and ‘activity’ is rather broad, see Buchanan and Stubblebine(1962) (‘We dene an activity here as any distinguishable human action that may be measured, such as eating bread, drinking milk, spewing smoke into the air, dumping lier on the highways, giving to the poor, etc.’).

53 party, this would qualify as an ‘externality’ under their denition. While the analysis is based on this broad denition, aention is also given to a narrower denition and scope of externalities throughout the eorem.

1.7.2.2.1 Negative externalities under Coase

e main object of analysis in the classical Coasean seing is the negative externality, the ‘actions of business rms which have harmful eects on others’. All of the examples listed by Coase in the “e Problem of Social Costs” are therefore examples of negative externalities: He talks about the noise of a confectioner’s machinery, the straying cale of a cale-raiser and the sparks of the train. Under Coase’s original work, the negative externality arises fully outside of a direct contractual relationship between the polluter and the victim. In the case of the confectioner, the negative externality, the nuisance from his machinery, arises from the sweetmaker’s production of candy. In the case of the straying cale, the negative externality is a result of farming activity. In the case of the railroad, the sparks of the train occur in the course of commercial transit. In all of these seings the victim is a third party of a commercial and contractual seing between the polluter and another third party, such as the candy maker’s customers, producers purchasing milk or meat from the cale-raiser and the railroad passengers.

1.7.2.2.2 Positive externalities under Coase

Coase paid lile aention to consider and discuss the secondary eects of remedying the negative externality in the Coasean seing. Under Coase, a xed sum negative externality arises that can be assigned either to the polluter or the victim. Given this xed sum cost, the relevant transaction costs that Coase considers are the negotiation costs between polluter and victim.186 However, in practice, the negative externality in the Coasean seing, irrespective of whether it is remedied through the law or the market, almost invariably also entails a positive externality. A positive externality is an economic benet enjoyed by a third party as a result of an economic transaction. In the Coasian seing, the economic transaction is the remediation of the negative externality. Where the law or the market decides that the sweetmaker needs to cease use of his machine, he needs to purchase a less noisy machine or re-tool the machine such that it no longer disturbs the quiet dentist neighbor. Similarly, where the law or the market decide that the quiet dentist needs to sustain the nuisance of the candy maker’s machine, he needs to relocate or needs to noise-isolate his house. Even where the negative externality is not remedied, but simply assigned to one party by contract, the polluter and the victim may require the help of a lawyer to dra the contract. In either of these cases, there will arise positive externalities to third parties in the form of revenue: whether it is a lawyer, a candy machine producer, the candy machine engineer, the new landlord of the quiet dentist or the seller of noise isolation material, a positive externality will invariably result from remediation of the negative externality. 187

1.7.2.3 e Coase eorem in the securities regulation setting

Under the rst layer of the eorem, the costs of the market are considered, including the costs of securities regulation, as endogenous transaction costs. is is an adequate level of analysis, as the main focus here lies on comparing the costs imposed by market regulation to the costs imposed by rm-specic regulation. us, it did not require a level of granularity beyond that of the aggregate costs that are compared to alternative regulatory regimes. is changes materially under the second layer of the eorem. e core contribution of the second layer of the eorem is that it re-conceptualizes the costs of the market as negative externalities or market failures. By regarding these costs as externalities, we can analyze securities regulation

186See Coase(1960) (‘In order to carry out a market transaction it is necessary to discover who it is that one wishes to deal with, to inform people that one wishes to deal and on what terms, to conduct negotiations leading up to a bargain, to draw up the contract, to undertake the inspection needed to make sure that the terms of the contract are being observed, and so on.’). 187See Coase(1960) (One could argue that these positive externalities are what Coase implicitly refers to when he talks about the costs of negotiation ‘ese operations are oen extremely costly, suciently costly at any rate to prevent many transactions that would be carried out in a world in which the pricing system worked without cost.’).

54 under the traditional Coasean seing of ‘e Problem of Social Costs’. us, at its core, the second layer of the eorem is an application of the classical Coasean seing to the realm of securities regulation. In applying the Coase eorem, we need to (i) rst dene what constitutes an externality under the eorem and (ii) secondly, which parties constitute the equivalent to the polluter and the victim in the tort seing.

1.7.2.3.1 Externalities

Under the Coase eorem of Securities Regulation, the externalities analyzed are both the baseline costs, the costs ‘naturally’ encountered by transaction parties, and the regulatory costs, which are imposed on the parties by the government – the super-rm188 in the Coasean terminology. By dening these costs as externalities, the focus lies either on the (i) surplus agent, (ii) the decit agent or (iii) both as one economic unit under the denition of Buchanan and Stubblebine (1962) and consider how their utility as a contractually bound unit is aected by both (i) ‘natural’ and ‘technical’ hurdles and (ii) the regulatory actions of the super-rm, the legislative body or the SEC, which imposes the regulatory cost on them. is allows us to dene a negative externality or market failure for each functional layer:

• Disclosure and information layer: e negative externality at the disclosure layer is misinformation, which can be viewed as the sum of all information, which may originate from the decit agent (issuer), the surplus agent (investor) and other third parties. us, the relevant ‘activity’ of this negative externality in the sense of Buchanan and Stubblebine(1962) is information production, which can either be (i) absent or (ii) false and misleading.

• Investment and liquidity layer: e negative externality at the investment and liquidity layer is illiquidity, which can be viewed as the absence of market buyers and sellers of the issuer’s securities. By receiving bid and ask oers, the investor is able to price the initial investment (primary markets) and exit the investment in the future (secondary market). us, the relevant ‘activity’ of this negative externality in the sense of Buchanan and Stubblebine(1962) is liquidity provision by other market participants.

• Diversication layer: e negative externality at the diversication layer is misallocation, which can result from the overexposure of the surplus agent to a single decit agent. While this may seem like a purely internal inef- ciency of the surplus agent at rst, the investor’s reliance on the availability of like-minded investors that will pool and diversify with him, makes this an externality that can aect both parties. us, the relevant ‘activity’ of this negative externality in the sense of Buchanan and Stubblebine(1962) is the provision of pooling funds by other market participants.

Positive or negative externalities e question arises from this, whether these externalities are to be considered positive externalities or negative externalities. e denition as either a positive or negative externality is inconsequential for the treatment under the eorem. However, it maers for the perspective of the parties involved and for public choice implications. Let us rst take the perspective of the super-rm, the government regulating these market failure. In this respect, one can say that securities regulation is functionally aimed at pre-emptively remediating a negative externality that would result in its absence, namely misinformation at the disclosure layer, illiquidity at the liquidity layer and misallocation at the diversication layer. From the perspective of the market participants, the market failures are clearly considered negative externalities, as they impose costs on their transaction they would like to avoid. We could see this clearly under the rst part of the eorem for regulatory costs, where we saw that parties may avoid transacting over the market altogether, if there exist much cheaper alternatives to rm-specic regulation or exemptions from securities regulation. From the perspective of the market-enabling rms, this externality is clearly considered a positive externality. Like the producer of candy machines, which can benet from the nuisance case between the confectioner and the dentist,

188See Coase(1960) (‘e government is, in a sense, a super-rm (but of a very special kind) since it is able to inuence the use of factors of production by administrative decision.’).

55 the law and accounting rms benet from complex and lengthy mandatory disclosure requirements and the investment banks benet from being the only legally sanctioned channel through which rms can access the public markets.

1.7.2.3.2 Parties

e parties in the securities regulation seing involve multiple parties, including the surplus agent, the decit agent, the securities regulator and other market participants, such as market-enabling rms and other investors that pool funds with the surplus agent. e counterpart to the polluter and the victim under the eorem are the decit agent (issuer) and the surplus agent (investor). e eorem can be applied to both a contractual and a non-contractual seing, however, depending on the seing there can exist material dierences to the original Coase eorem as will be further discussed below:

• Non-contractual setting: is is the classical Coasean seing, where you have the polluter involved in a commercial activity with a third party negatively aecting the victim. e confectioner under Sturges v. Bridgman, preparing the candy for his customers, therewith being a nuisance to the quiet dentist.189 In the non-contractual seing, we could imagine the counterpart to the polluter under our eorem being the decit agent, while the counterpart to the victim under the eorem being a surplus agent that is not invested (or not yet invested) in the issuer. e victim, while not being part of any investment contract between issuer and investors, can nevertheless be negatively aected through a negative externality associated with this particular issuer, such as misinformation or illiquidity. Where the issuer has misreported its earnings, it can aect investors not directly invested in this issuer. Similarly, where the securities of a particular issuer become illiquid, this can cause market disturbances that can aect investors not directly invested in said issuer.

• Contractual setting: e other perspective one can take is that the externality actually arises within a contractual relationship, which could technically make it an internality if the contractual parties are considered as one unit. e classical Coasean seing is not explicitly addressing a situation where an internality in that sense instead of an externality arises. While Coase argues that the same solution will emerge out of any externality relationship, regardless of the structure of the property rights, as long as the price mechanism works smoothly, he limits his analysis to externalities in the sense that these are ‘harmful eects on others’.190 Under the broader externality denition by Buchanan and Stubblebine(1962), the Coase eorem can also be applied in such a contractual seing, as long as the utility functions of the investor and the rm do not fully converge. In other words, as long as the actions of the issuer are not fully within the control of the investor. is is a reasonable assumption in a market and security law seing with many small investors. In the literature, there already exist examples of the Coasean analysis being explicitly applied to contractual seings. Logue and Slemrod(2010), for example, apply the Coase eorem to contractual relations that are subject to taxes, which they view as the harm in the Coasian sense.

1.7.2.3.3 Limitations

One may ask, whether the ‘market costs’ dened as ‘externalities’ under the eorem are not really mere transaction costs as suggested under the rst layer of the eorem and thus, distinctly not externalities. In particular, under a narrower denition of ‘externality’, which is limited to non-contractual inter-rm relations, this is a valid question and concern, given that the concept of the externality should be clearly dierent from that of a transaction cost. Indeed, the concepts of transaction costs, negative externalities and market failures are closely linked and require precise denition. Arrow(1969) directly related market failures to transaction costs, arguing that transaction costs can be viewed as the general reason for the non-exsitence or failure of markets. With respect to the distinction between market failure and externalities, he viewed market failures as a more general category than externalities and negative externalities in

189See Coase(1960). 190See Logue and Slemrod(2010) (‘Notably the traditional Coasean bargaining situation involves conicting land uses in which there is no prior contractual relationship between the two parties. e injurer and the victim are not in a contractual seller-buyer relationship with each other.’).

56 particular as ‘a special case of a more general phenomenon, the failure of markets to exist’. In the same vein, Toumano (1984) argued that transaction costs, ‘provide an explanation for market failure’. Similarly, Williamson(1985) views transaction costs as the main cause of market failures. He later described the market failures that involve transaction costs, which lead to a rm allocation, as a special case of market failures.191 From this it appears that, what is referred to as a ‘market failure’, an ‘externality’ and a ‘transaction cost’ needs to be clearly dened in the context in which it is discussed. Under Coase, the negative externality is ‘pollution’ and the transaction costs he considers are the bargaining costs between polluter and victim. Similary, under the disclosure layer, the negative externality is ‘misinformation’ and the transaction costs we consider are the information production costs between investor and issuer. While this denitional concern may be seen, as indeed a limitation of the second layer of this Coase eorem of Securities eorem, it can also be viewed as its main contributions. Much like Calabresi’s most important contribution was his reconceptualization of tort law as accident law, it could be argued that the most important contribution of the second part of the eorem is its reconceptualization of the costs of the market, including the costs of securities law, as an externality in itself instead of a mere transaction cost. While Calabresi redened tort law as the social problem of accidents and identied the “costs of accidents” as the most important normative category, the second layer of the eorem can be said to do the same for security laws. It views security laws as a pre-emptive aempt of the regulator to reduce ‘negative externalities’ or ‘market failures’ associated with the market allocation. In that pursuit, security regulation creates its is own set of costs, which may overshoot baseline costs and create net regulatory costs. e parties to the market transaction are aected by the superrm of the securities regulator. It is not meant to be suggested that the eorem is the rst theory that views part of securities law as costly, replaceable or modiable. However, the second part of the eorem’s contribution in this regard is to synthesize these considerations within an overall theoretical framework.

1.7.2.4 Application and results

Now that we have established the basic building blocks of the eorem, we can apply the theory in the context of securities regulation and reect on what policy makers can learn from it with respect to the design of securities regulation.

1.7.2.4.1 Application in the frictionless Coasean setting

In a rst application, we assume the classical ‘Coasean’ seing, namely a frictionless environment where the parties are not restricted by any transaction costs. In this respect, we can recount the two propositions from above:

• eciency proposition

• invariance proposition

e eciency proposition states that in the absence of transaction costs, parties can overcome ineciencies otherwise caused by externalities. In other words, in a frictionless seing, the costs involved with the negative externalities or market failures (misinformation, illiquidity and misallocation) can be optimally allocated between the parties involved. us, in the context of securities regulation, the issuer and the investor can engage in Coasean bargaining and optimally allocate the regulatory costs of securities regulation. e invariance proposition goes one step further and states that in a frictionless world, the assignment of legal entitlements or obligations does not aect the allocation of resources. In the context of securities regulation, this means that in a frictionless seing, it does not aect the eciency of the nal allocation whether securities regulation assigns legal liabilities and obligations on the issuer or on the investor. is laer nding is particularly interesting, since it basically holds that in a frictionless environment, securities regulation cannot do material harm. inking about the disclosure and information layer for a moment: whether the

191See Williamson(1971) (What is referred to here as market failures are failures only in the limited sense in that they involve transaction costs that can be aenuated by substituting internal organization for market exchange).

57 SEC obliges the issuer to disclose substantial information or whether it holds the investors liable to inform themselves does not aect the nal allocation of resources. Like for Coase, this frictionless environment serves as a mere guiding principle, as we seek to conceive of a frictionless or near frictionless regime in the course of the eorem.

1.7.2.4.2 Application in the positive transaction cost setting

To analyze the securities regulation in a positive transaction cost environment, we look to Guido Calabresi, namely his 1970 book ‘e Cost of Accidents’, as outlined above. In particular we want to focus on the two salient propositions identied from his book:

• optimal tort liability proposition

• least-cost avoider proposition

In the context of the eorem, we refer to the optimal tort liability proposition as the optimal securities regulation proposition. ese two propositions are central to the ereom developed here, as they form the normative guiding principles for the cost-benet tool that is derived. Optimal securities regulation proposition Under the optimal tort liability proposition in the tort seing, Calabresi proposes a normative regime that minimizes the sum of the cost of accidents and the costs of avoiding accidents, including the administrative costs of the tort system. Note that under Calabresi’s optimization, the total costs naturally associated with externalities, as well the specic legal costs associated of externality are minimized. is is similar to our baseline costs, which are the natural costs associated with the market allocation, and the regulatory costs that arise in addition (or in parallel) to the naturally encountered costs. Similarly, the optimal securities regulation proposition under the eorem proposes that the optimal securities regulation regime is one that minimizes the sum of both baseline costs and regulatory costs. In other words, the optimal securities regulation regime is one where the market participants can transact as eciently as possible and where the additional cost of securities regulation is minimized. While the optimal securities proposition does not assume that transaction costs can be fully eliminated, the frictionless environment is again, like under Coase’s original paper, the theoretical yardstick. Least-cost avoider proposition Where the optimal securities regulation has been adapted, but there still exist positive baseline costs and regulatory costs, the question arises which party should bear these costs. In the context of tort law, under the least cost avoider proposition, Calabresi proposes to assign the tort liability to the party able to minimize negative externalities most eciently. If the car driver has fewer costs to pay aention to the road, the law should assign liability to him. If the pedestrian has lower costs in watching out for cars, the liability may be more optimally assigned to him instead. Similarly, under the least-cost avoider proposition in the eorem, the proposition is that the costs should be assigned to the party, which is able to minimize externalities most eciently. In the context of securities transactions, this means that for each functional layer, the normative proposition of the eorem is to consider whether market costs, including regulatory compliance costs, can be most eciently minimized by either investors, issuers or any third-party.

1.7.2.5 Securities regulation as catalyst and residual cost of markets

Within the legal system, securities regulation has the purpose and function of enabling free-market asset allocation. In other words, it is supposed to be a catalyst of markets. In many instances it may be successful in that regard, for example by addressing the underproduction aw through mandatory disclosure obligations or by reducing illiquidity concerns through regulated national securities exchanges and broker-dealer rms. Yet, given its stringent requirements, both for issuers of securities and market-enabling rms, security laws can also have the opposite eect. Rather than encouraging markets, which the regulation is originally intended to do, there is

58 a risk that as the baseline costs of nancial transactions decline, securities regulation becomes the residual net cost of markets. In other words, when the real-world costs of transacting have become marginal, the securities law costs may be the residual cost layer that stands between the rm and market allocation. is is a somewhat paradox situation, as it means that a mechanism, which is intended to reduce market friction, has turned into an externality in the Coasean sense. As laid out under the rst part of the eorem, securities regulation may thus end up preventing market allocation and eectively price transactions into the rm structure. is is where the second part of the eorem comes in and tries to provide both logical structure and a practical tool for the cost-benet analysis of each functional layer.

1.7.3 Application to the disclosure and information layer

1.7.3.1 Misinformation externality

At the disclosure and information layer, the relevant negative externality is misinformation. To understand the precise nature of this externality, let us recall the denition of an externality set out by Buchanan and Stubblebine(1962), an externality exists where the utility of a rm or individual is dependent upon activities, which are exclusively under his own control or authority, but also upon another activity, which is, by denition, under the control of a second rm or individual. In this respect, misinformation can be viewed as the sum of all information, which may originate from the decit agent (issuer) and other third parties, which is received and processed by the surplus agent prior to the investment. us, the relevant ‘activity’ that results in the negative externality of misinformation is either the absence of information production or false and misleading information production. e key insight of the second part of the eorem is that in the absence of transaction costs related to the market externalities, the legal assignment of rights and obligations under security laws does not aect the eciency of the nal allocation. For the disclosure layer, the relevant transaction costs related to the misinformation externality are information production costs. As we have seen under the rst part of the eorem, securities regulation aempts to remedy the presence of the misinformation externality by assigning mandatory disclosure obligations on decit agents. e second part of the eorem thus holds that with information costs approaching zero, whether security laws assign (i) mandatory disclosure obligations on the issuer or (ii) data gathering obligations on the investor, does not aect the eciency of the nal allocation. In a positive transaction cost seing, however, the initial assignment of rights and obligations by the law does maer. In this respect, an optimal regime is one that minimizes the costs of the market and assigns them to the least cost avoider, the party that is best positioned to reduce the costs of the market. us, in the context of the disclosure layer, the objective is to reduce disclosure and information costs and assign these costs to the party best positioned to reduce them. In the below chapters, a (hypothetical) optimal regime is explored, which disaggregates the monolithic disclosure costs, such that it allows for a more nuanced assignment of costs to the least cost avoider.

1.7.3.2 Optimal regime

Under an optimal disclosure regime, information asymmetries between surplus agent and decit agent are reduced under minimal information production costs. is is a challenging task, as it requires us to imagine a regime that neither exists currently, nor one that is readily available in the hypothetical. e challenge is to dene a technological specication that, given current technological means, allows for the most ecient ow of relevant information between agents.

• SEC initiatives: e SEC has a long history of using technological means to improve the dissemination of issuer information. Going back to the introduction of its electronic ling platform, the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, in the 1990is,192 the Commission has consistently implemented new

192In December 19, 1994, the Commission made electronic lings via the EDGAR system mandatory for all issuer. See Rulemaking for EDGAR System, Securities Act Release No. 7122, Exchange Act Release No. 35,113, Investment Company Act Release No. 20,783, 59 Fed. Reg. 67,752 (Dec. 30, 1994) (codied at 17 C.F.R. parts 228-230, 232, 239-240, 249-250, 259-260, 269, 274).

59 technological initiatives.193 For example, through the ‘XBRL’ initiative,194 the SEC requires issuers since 2009 to embed data ‘tags’ in electronic lings using a semantic XML-based data schemata.195At a later point, the SEC has decided to use the same XBRL data schemata to meet a congressional mandate under Dodd-Frank,196 requiring loan level data disclosure for asset- backed-securities (ABS) issuers.197 Similarly, the SEC has been open to new dissemination platforms, such as Twier or Facebook for Regulation FD disclosures.198

• Academic proposals: Wallman(1997), a former SEC Commissioner, has proposed shiing from an aggregated disclosure system to a real-time disaggregated data disclosure system. Langevoort(1997) has considered the possibility of abolishing period 10-K and 10-Q lings with ‘unitary company registration le’ that is maintained by the issuer.199 Prohs(2002) has proposed a customized investor interface, that dynamically tailors disclosures to individual users.200 Gerding(2016b) has thought about the possibility of requiring issuers to make risk models used to manage the issuer’s risk open-source.201

In the rst part of the eorem, the fact that the current regime places disclosure costs unilaterally on the shoulders of the issuer is identied as a major weakness of the existing mandatory disclosure system. In this regard, an optimal regime would be able to unburden the issuer by dividing the disclosure and information costs more equally between surplus agent and decit agent. As such, the essence of the optimal regime proposed here is to split the costs of information production costs into a data production layer and a data analysis layer. Given, that disaggregation of data is at the center of the proposal, it can be seen to be in the tradition of Wallman(1997). Our enquiry into the optimal starts with the realization that the main source of mandatory disclosure costs lies in the transformation of data, rather than the production of raw data.202 It is fair to assume that the current disclosure and information system, the source data required for all market disclosures is already stored to a large part digitally. As such, it is theoretically available ‘at the ngertips’ of the issuer. However, it is available in a raw and unprocessed format and requires further human or computational processing for the purpose of regulatory disclosures. Extracting the data from the respective data silos and subjecting it to human processing by market-enabling rms makes up the largest share of the information production costs. e nancial and business data required for a company to go public may be readily available in a company’s electronic le systems. However, it may still consume countless hours for employees203 and external lawyer and accountant hours204 to retrieve, qualify and re-arrange said information and data into SEC-compliant disclosures. Similarly, in the area of credit, all relevant data related to a mortgage loan secured by a specic property, may already be stored in an electronic database. Geospatial location, building properties as well as recent property transactions in the neighbour- hood may be readily available in a digital database operated by a local land registry. In securitizing the mortgage loan,

193See SEC(2006) (chronicling the dierent technological initiatives of the SEC since the 1980ies). 194See Gerding(2016b) (‘As an early example, the SEC’s ”XBRL’ initiative required issuers to aach data tags to their disclosures to enable investors to pull similar nancial data from a range of issuers and place it in spreadsheets or other analytic tools, enabling investors to compare issuers side-by- side.’). 195See Interactive Data To Improve Financial Reporting, Securities Act Release No. 9002, Exchange Act Release No. 59,324, Investment Company Act Release No. 28,609, 74 Fed. Reg. 6776 (Feb. 10, 2009) (codied at 17 C.F.R. parts. 229-230, 232, 239-240, 249). 196See Dodd-Frank Wall Street Reform and Consumer Protection Act, Pub. L. No. 111- 203, §942(b), 124 Stat. 1376, 1897 (2010) (codied as amended at 15 U.S.C. §77g(c) (2012)). 197Asset-Backed Securities Disclosure and Registration, Securities Act Release No. 9638, Exchange Act Release No. 72,982, 79 Fed. Reg. 57,184 (Sept. 24, 2014) (codied at 17 C.F.R. parts 229-230, 232,239-240,243, 249 (2015)). 198See Heyman(2015) (‘e SEC recently determined that companies can use social media outlets, such as Facebook and Twier, to announce material information in compliance with Reg FD provided that investors ”have been alerted about which social media will be used to disseminate such information.”49 As the SEC explained, ”[most social media are perfectly suitable methods for communicating with investors, but not if the access is restricted or if investors do not know that’s where they need to turn to get the latest news.’). 199See Langevoort(1997) (‘Let me frame the issue starkly by oering a proposal. In an electronic disclosure environment, the concept of the 10-K and 10-Q should be abolished in favor of a unitary company registration ”le.” is le should contain all the material currently required under Reg. S-K, plus-for the reasons articulated earlier-management’s risk discussion and analysis.’). 200See Prohs(2002) (‘A system that users could customize to access only portions of a company’s management information system would allow users to determine what information they want, how they want it presented, and when they want it presented.’). 201See Gerding(2016b) (‘A more radical approach would force disclosure of the algorithms in these models, in essence making this soware open- source. is would allow investors to analyze the models in greater detail and spot limitations, bugs, and the potential for dangerous homogeneity among the models of dierent rms.’). 202See Wallman(1997) (‘we know we have – obviously – the raw data that comprises our current nancial statements.’). 203See A. Schwartz(2019) (estimating it takes 1200 internal hours over six months). 204A. Schwartz(2019) (‘Moreover, companies are usually unable to fulll these disclosure requirements entirely on their own. is inability forces companies to add the expense of external aorneys, accountants, and underwriters.’).

60 the friction again comes from extracting this data and processing it in a manner to comply with the SEC’s disclosure requirements. Against this background, the optimal regime proposed here, considers an alternative where issuers can comply with the disclosure requirements through the dissemination of raw and unprocessed data, with no or only minimal additional data processing. Put more simply, the option of issuers to ‘data dump’ large amounts of unprocessed data on investors, with investors burdening the costs of aggregating and analyzing it, is considered. us, the regime proposed here is one that breaks information production into two distinct parts:

• Ground truth data layer: this refers to electronic, raw and unprocessed data sources, which the surplus agents can credibly rely on when making the investment decision. Such data sources can be nancial or alternative business data sources. e term ‘ground truth’ originates from the eld of statistical machine learning where it refers to the high accuracy of the training data set’s classication for supervised learning techniques.205 e ground truth data is considered to be raw and objective, because it has been – in a metaphorical way – collected directly from the ground. In other words, it is high quality data, sometimes referred to as ‘gold standard data’, which can be trusted and relied on.206 Since the data provided by issuers is used for the pricing of securities, this quality property is relevant both for the individual asset and the marketplace at large. To make such ground truth data available to investors, ‘data pipelines’ could be put in place between the issuer, third-party data providers and the SEC’s ling system. e term ‘data pipeline’ is used to refer to a set of connected data processing elements.207 Such data pipelines are generally implemented using ‘application programming interfaces’ or ‘APIs’. Such APIs are a highly ecient and scalable way to share digital data, by dening what information can be requested, how requests are made and what data is returned. As Wallman(1997) points out, real-time ground truth data pipelines are a common occurrence within the rm and some regulators already access them in their supervisory work.208 us, the idea of the SEC seing up an API to which issuers can link their data pipeline – either directly or through third-party soware providers – appears like a technical option worth exploring.

• Data processing and analysis layer: Under data processing, the preparation of the raw data for the end user is understood. In particular, this can entail the aggregation of ground truth data entries into consolidated information sets. Wallman(1997) refers to this aggregation function as ‘compiling’ in the context of nancial statement preparation.209 Given the proximity of data processing to the disaggregated data layer, it is a function traditionally performed internally by the rm or with the assistance of specialized rms, such as accounting rms or soware providers. Data analysis here refers to the use of processed data as an input for a wider range of calculations and model-based inferences, oen in combination with external data sets. In the sphere of stock or bond oerings, we can think of equity analysts and credit agencies using already aggregated and processed nancial data to make buy and sell recommendations or assign credit ratings. Similarly, in the sphere of smaller consumer or mortgage loans, ground truth data may be fed into a larger underwriting model. Data processing and analysis can be performed through human processors,210 where it may be subject to judgement and discretion, or it can be performed algorithmically,211 where it requires precise specication and can be subject to a more rigorous review.

205See LeCun, Bengio, and Hinton(2015) (referring to ground truth data as ‘natural data in their raw form’). 206See Wallman(1997) (pointing to the importance of data integrity in a disaggregated accounting disclosure system ‘e change will be, therefore, to aestation that is focused on the process, means of preparation and integrity of the data drawn upon by users, as opposed to the end product of the compilation (such as GAAP). Such aestation is necessary to avoid the old problem conveyed by the concept of “GIGO”—or “garbage in garbage out.”’). 207See Wu et al.(2016) (detailing the existing pipelining frameworks used in the analysis of large datasets ‘Many real-world data analysis scenarios require pipelining and integration of multiple (big) data-processing and data-analytics jobs, which oen execute in heterogeneous environments, such as MapReduce; Spark; or R, Python, or Bash scripts. Such a pipeline requires much glue code to get data across environments.’). 208See Wallman(1997) (‘In fact, in some aliated groups where a central nancing entity is expected to maintain nancial over sight of the group members,real-time access to nancial data is normal. In addition, certain nancial regulators in this country have begun accessing, on a real-time basis with direct computer-to-computer linkages, selected nancial information and underlying data regarding specic loan performance, portfolio values and capital levels.’). 209See Wallman(1997) (‘By “compiling” then, I mean making data useful by taking data bits that are not useful in their raw form, tracking and aggregating the data by categories over time periods, and presenting the results of the compilation in accordance with a standard language—in this instance GAAP - to make them usable.’). 210See Wallman(1997) (‘Clearly, there is a great deal of judgment and expertise inherent in performing the compiling function.’). 211See Wallman(1997) (pointing out the possibility of replacing human compiling with machine-processed compilers ‘However, readers familiar

61 Where data processing and analysis takes place algorithmically, data pipelining through APIs allows for a modular architecture,212 where the analysis of the data can be performed separately.213

1.7.3.2.1 Optimal regime in the context of mandated nancial reporting

To imagine what such a disclosure architecture could look like in practice, let us think through an example of nancial disclosures within a traditional equity oering. In a hypothetical regime oering full transparency, the issuer could comply with its ‘basic’ mandatory disclosure requirements by linking the raw nancial data feed, including all individual bookings entries, to an SEC-specied API.214 As such a dissemination of ground truth nancial data would overload investors with information, further data processing would be required to make it useful for end users.215 In particular, the data would need to be compiled into consolidated nancial statements (balance sheet, income statement and cash ow statement). e separation of the ground truth data from data processing and analysis allows us to think about this essential accounting function as a modular layer built on top of the ground truth data layer. It also allows us to think more exibly about the costs and the assignment of the costs to the cheapest cost avoider. Indeed, through this separation of information production costs into two distinct cost buckets, security laws could require either (i) the issuer or (ii) the investors to engage the services of an accounting service provider. Let us explore the option of investor-paid accountants further. Investors could engage an accounting rm with traditional ‘human’ accountants to help them make sense of the raw data. ese accountants could in theory, if the ground truth data feed was complete and unambiguous, retrieve the raw data through multiple calls to the API and fully construct the nancial statements for the issuer. On the basis of robust ground truth data, they could use their human judgement to make tricky accounting adjustments, such as expensing or capitalizing the purchase of a particular asset under the U.S. GAAP regime. Notably, all of this would be at the cost of the investors and without the direct participation of the issuer. Investors could also engage multiple accounting rms, which might each prepare nancial statements slightly dierently.216 Now let us imagine an even more radical option, where the accounting aggregation and processing of the raw ground truth data would occur entirely algorithmically.217 In such a case, the services of the accounting rm would in a sense be reduced to soware code. For a given set of accounting standards,218 the soware-based accounting module could classify the raw data, aggregate them into their specic buckets and produce the nal consolidated nancial statements. Like Gerding(2016b) proposed for risk models, such soware-based accounting modules could be made open-source, open to the review of every interested party and even be tailored to dierent accounting standards (in particular IFRS with computer compilers will recognize the utility of the analogy in examining the intersection of technology and accounting and nancial reporting.’). 212Notably, data pipelining through APIs has emerged from a 1972 principle in soware engineering, known as the principle of information hiding. Parnas(1972) rst developing the principle, which was intended to enable the ‘modularization as a mechanism for improving the exibility and comprehensibility of a system while allowing the shortening of its development time.’ e aim of this principle was to beer connect dierent components of a system by decreasing modular frictions. Under this principle, soware modules hide implementation details from other modules in order to decrease their interdependencies. e soware modules should be both ‘open (for extension and adaptation) and closed (to avoid modications that aect clients)’ (Larman, 2001). 213Under the principle of information hiding that underlies API data infrastructures, the designers of the two dierent modules only need to agree on the interface, the API, while the ‘design of the internals of each component can go forward relatively independently’ (Grinter, Herbsleb, & Perry, 1999). Grinter et al.(1999) describes it as follows: ‘[…] interface specications play the well-known role of helping to coordinate the work between developers of dierent components. If the designers of two components agree on the interface, then design of the internals of each component can go forward relatively independently. Designers of component A need not know much about the design decisions made about component B, so long as both sides honor their well-specied commitments about how the two will hook together.’). 214See Wallman(1997) (‘Under this new approach, companies providing the database will have complied with their disclosure requirements, and the soware of users would then take over to provide customized nancial reports.’). 215See Gerding(2016b) (pointing to the already existing information overload problem of SEC disclosures ‘Paredes claims that mandatory disclosure rules prove ineective because investors suer from ”information overload.”). 216See Wallman(1997) (‘Provided suciently discrete data users could formulate their own nancial statements based on their own particular ”recognition” criteria. An analyst might well determine that the more relevant presentation of a company’s nancial health would be generated with dierent criteria, backing out certain charges or adding in certain costs.’). 217See Wallman(1997) (‘Decreasing the emphasis on compiling by accountants changes the focus of aestation. As technology plays a greater, and nally the major, role in the compiling and analyzing of information in accordance with individual users’ customized instructions, the aestation function will shi.’). 218See Wallman(1997) (‘As any one standard compilation system, such as GAAP, or international accounting standards, becomes just one of many that users may employ, the need for aestation will be drawn to the underlying data itself, and the means for preparing the elements of the database that make it useful to users when accessing the data.’).

62 vs. U.S. GAAP).219 In such a seing, which would combine ground truth nancial data with open-source processing soware, investors could at all times verify the ‘data lineage’ and ‘data provenance’ of nancial reports.220 Where an error would occur, it would be fully traceable under this architecture. e error could be either due to a friction in the data pipeline or a soware bug in the accounting soware. Rather than bringing a negligence case against a ‘human’ accountant or auditor, nding and resolving such an accounting error would mean debugging either the data pipeline or the accounting soware.

1.7.3.2.2 Limitations

While the proposed optimal regime above is designed to take some weight o the shoulders of issuers, it is not without its limitations. While sunlight is said to be the best disinfectant, there can also be too much sunlight. Surely enough, a highly transparent regime will be subject to much pushback from some issuers, investors and incumbent gatekeepers. In particular, the following concerns can be highlighted:

• Competitive concerns: Wallman(1997) already noted the concern that issuers may want to restrict access to disaggregated data due to competitive concerns.221 is is a classical case of the underproduction problem already discussed in more detail under section 1.6.2.1.2. Wallman(1997) proposes that the informational demands of investors and the creativity in the database design will eventually be able to resolve such competitive concerns. Another option would be to adjust the granularity level or introduce ground truth data disclosures on a voluntary basis at rst, much like in the case of the SEC’s XBRL initiative.222 e laer option would allow some issuers to self-select into such a disclosure regime, if their competitive concerns are lower than the costs of a traditional disclosure regime. For example, under the ‘open startups’ initiative, the data analytics company Baremetrics provides highly granular revenue data of multiple technology startups that have opted to openly disclosure such real-time transaction level revenue data, down to the individual $10 monthly subscription.223 For this subset of companies, the benets of publicity clearly seem to outweigh their competitive concerns. It is thus very likely that a voluntary API-based disclosure regime by the SEC would also lead to the opt-in of at least for a small cohort of ‘early mover’ companies.

• Data types: another limitation relates to the dierent data types that may lend themselves to ground truth disclosures. So far, we have discussed chiey traditional nancial or accounting data. Wallman(1997) notes the opportunity to include a wide range of nonnancial data sources and forward-looking information, including patent or customer cohort data.224 In the same vein, chapters 2 and 4 of this chapter point to a wide range of alternative data sources that could be provided under such a disclosure regime. On the other hand, there also exists data that is by its nature less readily available for digital distribution or inherently prone to condentiality, such as data related to condential business methods or pending litigation. For such information, the existing disclosure regime through information gatekeepers that review internal information and provide an opinion will still be beer suited. 219See Wallman(1997) (‘Of course, the same technology, assuming the proper coding of the data, would also allow preparers to generate a standard form nancial statement that would be identical to those prepared under the current system. But no longer would that be the only nancial presentation available.’). 220Data lineage and provenance are critical concepts in data intensive contexts, see Wang, Crawl, Purawat, Nguyen, and Altintas(2015) (‘Generally speaking, provenance in the digital context is about the origin and various transformations of data. In the context of scientic computation and workows, provenance usually means the lineage and processing history of a data product, and the record of the processes that led to it.’). 221See Wallman(1997) (‘Access to more disaggregated data provides greater and more useful information to, among others, investors, suppliers, customers and regulators; it also provides greater and more useful information to competitors – a rather fundamental issue. As a general maer, a company should provide information to a constituency where doing so will provide a benet to the company.’). 222Until 2009, participation in the XBRL initiative was voluntary. See XBRL Voluntary Financial Reporting Program on the EDGAR System, Securities Act Release No. 8529, Exchange Act Release No. 51,129, Investment Company Act Release No. 26,747, 70 Fed. Reg. 6556 (Feb. 8, 2005) (codied at 17 C.F.R. parts 228-229, 232, 240, 249, 270). 223See Baremetrics(2020) (‘Welcome to the land of the brave. ese wonderful companies are embracing transparency and openness by sharing their metrics with everyone.’). 224See Wallman(1997) (‘In addition, and in particular, this shi would further our ability to convey, more easily, data that is increasingly viewed as critical to an understanding of knowledge-based companies. Such information might include nonnancial or forward-looking information, as well as information such as the number of patents obtained or their value or revenues generated relative to research and development expenditures, or the number of repeat customers among businesses in a particular industry.’).

63 1.7.3.2.3 Chapter 2: Optimal disclosure regime in the sphere of technology startups

Chapter 2 explores an optimal disclosure regime in the sphere of technology startups. With respect to the ground truth data layer, it is found that there exist a number of relevant ground truth data sources, including both nancial data points and alternative business metrics. is data is digitally stored and hosted by a limited number of gatekeepers and as such could (in theory) be made available through regulatory APIs relatively inexpensively. A select number of relevant data points are already provided freely and openly by third party data providers. With respect to the data processing and analysis layer, it is explored how such a layer could look like for technology rms, where investors have historically relied heavily on equity analyst coverage.

1.7.3.2.4 Chapter 4: Optimal disclosure regime in the sphere of credit

Chapter 4 explores an optimal disclosure regime in the sphere of credit markets. With respect to the ground truth data layer, it is found that there exist a number of disparate ground truth data sources depending on the originated kind of credit. ese data sources are hosted both electronically and physically by government agencies, credit bureaus, third party rms and credit issuers themselves. As such, the costs of establishing ground truth data layers in the sphere of credit are heterogenous. With respect to the data processing and analysis layer, the chapter explores how such a layer could look like for credit, where investors have historically relied heavily on credit rating agencies.

1.7.3.3 Least cost avoider

e least cost avoider proposition provides that disclosure costs should be assigned to the party, which is able to minimize the misinformation externality most eciently. Even in an optimal securities regulation regime, as the one discussed above, where the total sum of disclosure costs is minimized,225 yet still positive, securities regulation must optimally assign the costs to either the surplus agent, the decit agent or third party data providers. Wallman(1997) has proposed a regime where issuers fulll their disclosure requirements by providing access to disaggregated data, while investors assume the costs of aggregating this raw data into meaningful reports.226 In a similar vein, Haeberele and Henderson have proposed a securities regulation regime where the cost of disclosure is passed from the issuer to the investor by subjecting the disclosure costs to the market.227 Under their regime, the issuer sells information to the investor and thus provides a revenue-based motive for the issuer to produce such information. Against the backdrop of the optimal securities regulation regime outlined above, a more nuanced variant of their proposition is explored, which is grounded on the developed eorem. To identify the least cost avoider, we stay in the optimal securities regulation seing outlined above. It is here that the decomposition into a ground truth data layer and a data processing and analysis layer becomes really useful from a regulatory perspective, as it allows us to assign these costs in a more nuanced manner.

1.7.3.3.1 Ground truth data layer

With respect to the provision of ground truth data, it appears that the decit agent, as the data owner, is best positioned to minimize these costs. As the data is under the issuer’s control and directly relates to the internal operations of the entity, the issuer is well incentivized to optimize these costs. Such has been the logic of securities laws for decades and it appears that this still holds mostly true in the age of APIs. Let us for a moment, however, consider the alternative of assigning these cost on the surplus agents. In such a regime, the investors would proactively need to make enquiries, investing time and money to obtain ground truth data.

225See Wallman(1997) (‘Not only then does this access-based system have tremendous advantages for users, but it also stops the upward spiral of mandated costs and disclosure requirements imposed on preparers.’). 226See Wallman(1997) (‘Under this new approach, companies providing the database will have complied with their disclosure requirements, and the soware of users would then take over to provide customized nancial reports’). 227See Haeberle(2018) (‘is Article oers an innovative approach to the disclosure underproduction problem that has long challenged policymakers. It argues that a well-regulated market for corporate disclosures where rms can sell tiered access to their information would lead to improved disclosure. More specically, we argue that if public rms could sell early access to information that they must then make available to all, they would produce and share more information, in enhanced formats, more frequently.’).

64 is would in many ways resemble the inecient operations of the commercial credit bureaus in the 1800s, which were eectively private spy agencies which would maintain les of personal habits of creditors:228

“[... ] whether you called them spies or correspondents, the agencies relied on networks of locals sending wrien dispatches back to the central oce. ey sought information (oen unreliably subjective) about a person’s credit-worthiness, judged not just in terms of his nancial circumstances, but his personal character—Was he married? Did he have children? Who were his parents? What church did he aend? Sometimes this information wandered into the deeply embarrassing [... ]”

Against this background, it appears that issuers are indeed the least cost avoiders and are best positioned to reduce the ground truth data pipelining costs. Further to that, there may exist situations where the ground truth data is already ‘freely’ and ‘publicly’ available. We can think about data and information provided by government-operated registries or private third-party data providers. One example used in chapter 2 relates to website trac, which is provided openly by Amazon’s subsidiary Alexa (famously through the Alexa rank) and cross-nanced by other Amazon business units. In such a case, where the regulatory system does not demand for disclosure above what is already freely and publicly available, we have almost a classical frictionless seing under Coase. Assuming that these third-party data providers oer this data really ‘freely, openly and reliably’, then the second part of the eorem dictates that the assignment of the such information costs would not aect the eciency of the nal allocation. us, in such a situation, irrespective of whether issuer is obliged by securities regulation to disclose (public) information, or whether the security laws burden the investor with the cost of retrieving such (public) data, the eciency of the nal allocation is not altered.

1.7.3.3.2 Data processing and analysis layer

Under the current securities regulation regime, the data processing costs related to preparing the nancial accounting disclosures are borne by the issuer. Given the close proximity to ground truth data that is required for the preparation of nancial statements, issuers do indeed appear to the least cost avoiders. However, by spliing the information production costs into ground truth data pipelining costs and data processing and analysis costs, the outlined optimal regime would theoretically allow for a re-assignment of the laer costs to investors:

• Data processing: e ability to separate data processing from the issuer and to re-assign it to the investor seems particularly daunting with respect to the nancial accounting and auditing function. In the past, accounting rms hired by the issuer, have oen played a material role in prominent cases of security frauds.229 In fact, one of the original intents of the Senate in establishing the disclosure obligations in the 1933 and 1934 Acts was to standardize the error-prone accounting aggregation and processing and avoid window-dressing, the practice of misrepresenting the nancial situation of a company by means of questionable accounting.230 Where investors have access to granular, disaggregated nancial data of the issuer through a robust ground truth feed, one could hypothetically imagine re-assigning the data processing costs to investors.

• Data analysis: in contrast to data processing, there exists a long tradition of investors carrying the costs of data analysis. In the area of credit ratings, credit rating agencies until the mid-1970 would charge investors a subscription fee.231 Similarly, the research of stock market analysts is paid for by investors, with investment banks typically bundling research with other services.232 R. Gilson and Kraakman(1984) have described a situation in

228See Jeong(2016). 229See Aguirre(2003) (‘Enron’s investment banks, accountants, and lawyers now face billions in potential liability for allegedly helping Enron construct an $80 billion house of cards. Arthur Andersen, with its $9 billion in annual revenues, simply vanished aer it was caught and convicted for shredding Enron records. Why were these gatekeepers, either giant nancial institutions or the most sophisticated lawyers and accountants, condent the securities fraud laws would not apply to them?’). 230S. Rep. No. 73-792, at 11 (1934) (‘Many other instances of ”window-dressing” were observed, where inexcusable methods were employed to inate assets, obscure liabilities, and conceal decits.’). 231See Darcy(2009) (‘While these CRAs originally charged subscription fees, they switched to the issuer pays model in the mid-1970s.’). 232See Coee(1984) (‘Typically, securities research is reduced to an analyst’s report that is circulated among prominent institutional investors in return for expected future commissions or other investment banking business.’).

65 which traders in an inecient market acquire information to earn excess returns and, as more information is conveyed, these excess returns decrease until the market becomes fully ecient.233 In the same vein, Goshen and Parchomovsky(2006) describe the role of information traders, who specialize in gathering and analyzing rm- specic information in public security markets.234 ey go one step further by arguing that it is the essential role of securities regulation to facilitate and protect the work of such information traders.

From the above, an assignment of data processing and analysis costs to investors seems to be an interesting alternative. However, such an assignment comes with the well-known free-rider problem. is free-rider problem explains why credit rating agencies have switched to an issuer-nanced model235 and why equity research is bundled with other services.236 us, given the public nature of information, the assignment of these costs to investors would practically require an incentive structure or pooling mechanism to balance out the free-rider problem.

1.7.3.3.3 Chapter 2: Least cost avoider in the sphere of technology startups

Chapter 2 analyzes how the proposed least cost avoider regime maps to the sphere of startups. With respect to the ground truth data layer, it is found that issuers are the least cost avoiders for most data points, given their direct and proprietary access to the ‘data lake’ of both nancial and alternative data sources. For the select data points that are provided both freely and openly by third party gatekeepers, these gatekeeper rms appear to be the most ecient cost avoiders, as they are able to monetize the information production costs through alternative revenue streams. With respect to the data processing and analysis layer, it is found that both issuers and investors may be the least cost avoiders, depending on the purpose and granularity level of the analysis provided by the layer.

1.7.3.3.4 Chapter 4: Least cost avoider in the sphere of credit

Chapter 4 analyze how the proposed least cost avoider regime maps to the sphere of credit. With respect to the ground truth data layer, it is found that, given the heterogeneity of data sources and data hosts in the sphere of credit, the least cost avoider of this layer can vary. While in many cases, it appears to be the credit issuer, for some segments of the credit market, specialized data providers may be the least cost avoiders. With respect to the data processing and analysis layer, both issuers and investors may be the least cost avoiders, depending on the purpose and granularity level of the analysis provided by the layer.

1.7.4 Application to the investment and liquidity layer

1.7.4.1 Illiquidity externality

At the investment and liquidity layer, the relevant negative externality is illiquidity. To understand the precise nature of this externality, let us again recall our denition of an externality: as outlined by Buchanan and Stubblebine(1962), an externality exists where the utility of a rm or individual is dependent upon activities, which are exclusively under his own control or authority, but also upon another activity, which is, by denition, under the control of a second rm or individual. In this respect, liquidity can be viewed as the availability of market buyers and sellers of the issuer’s securities, while illiquidity is their absence. By receiving bid and ask oers, the investor is able to price the initial

233See R. Gilson and Kraakman(1984) (Traders would initially acquire information because, in an inecient market, they could earn returns on their investment in acquisition. As more traders became initially informed, however, the price system would convey more information to uninformed traders, thereby lowering the returns to informed traders. At the point at which the market became fully ecient, there would be no return to informed traders for having acquired the information, and, as a result, information acquisition would cease.). 234See Goshen and Parchomovsky(2006). 235See Darcy(2009) (‘e decision to do so stemmed largely from the nature of their business-CRAs produce information, and information is a public good. e subscription-based model created a “free rider” problem because the agencies could not feasibly stop paying subscribers from sharing the information with non- subscribers. rough the issuer pays model, the agencies can eectively charge all users of their product since the issuer can pass the cost of the rating on to investors in the form of a slightly reduced interest rate.’). 236See Coee(1984) (‘Once securities research is initially disseminated in this fashion (or any similar fashion), however, free riding is predictable: news leaks out almost immediately because the condentiality of a circulated report cannot be protected for long and because institutional investors have an incentive (aer they trade) to make the analyst’s report a self-fullling prophecy by encouraging others to trade.’).

66 investment (primary markets) and exit the investment in the future (secondary market). us, the relevant ‘activity’ in the sense of Buchanan and Stubblebine(1962) is the liquidity provision by other market participants. e key insight of the second part of the eorem is that in the absence of transaction costs related to the market externalities, the legal assignment of rights and obligations under security laws does not aect the eciency of the nal allocation. For the investment and liquidity layer, the relevant transaction costs related to the liquidity externality are liquidity provision costs. As we have seen under the rst part of the eorem, securities regulation aempts to remedy the presence of the illiquidity externality by mandating issuers and investors to transact through regulated broker-dealers and national exchanges in both primary and secondary markets. Although the assignment of costs is not explicitly regulated by security laws, such regulated intermediaries are in practice compensated primarily by issuers. e second part of the eorem holds that with liquidity provision costs approaching zero, whether security laws require (i) the issuers or (ii) the investors to compensate such intermediaries, does not aect the eciency of the nal allocation. In a positive transaction cost seing, however, the assignment of these costs by the law (whether explicitly or implicitly) maers. In this respect, an optimal regime is one that minimizes the costs of the market and assigns them to the least cost avoider, the party that is best positioned to reduce the costs of the market. us, in the context of the investment and liquidity layer, the objective is to reduce liquidity provision costs both in the primary and secondary markets and assign these costs to the party best positioned to reduce them. In the below chapter, a (hypothetical) optimal regime for both primary markets and secondary markets are sketched out that could minimize liquidity provision costs and allow for a more nuanced assignment of costs to the least cost avoider.

1.7.4.2 Optimal regime

At the investment and liquidity layer, the objective of an optimal regime is to minimize liquidity provision costs both for primary and secondary markets. As we have seen under the rst part of the eorem, the investment and liquidity layer is a notoriously expensive functional layer. e costs, which are primarily driven by baseline underwriting costs, are traditionally fully borne by the issuer. us, the optimal regime sketched out below aempts to identify mechanisms through which costs can be shared more equally between investors and issuers.

1.7.4.2.1 Primary markets

As we have seen under the rst part of the eorem, the engagement of underwriting rms in primary markets blur the lines between a market-based and a rm-based allocation. Both in equity and credit markets, the transitory banking allocation that emerges in rm-commitment underwriting leads issuers to leave considerable amounts of ‘money on the table’. Against this background, two market-based mechanisms could reduce such frictions in primary markets:

• Direct market access: the obvious alternative to a traditional underwriter model is for issuers to access capital markets directly. Sjostrom(2001) has described internet direct public oerings (DPOs) which emerged in the wake of the internet.237 However, such oerings were never widely adopted, as they were seen as inferior oerings of issuers that could not nd an underwriter to support their oerings.238 In contrast, chapter 2 describes in more detail how direct listings have recently emerged as a very eective way for larger technology startups to list their (existing) shares without the intermediation costs of traditional underwriters. However, as direct listings pass the entire costs of marketing the oering to the issuer, it remains to be seen whether they are indeed a viable option for smaller issuers.

• Pre-nancing vehicles: another alternative to the traditional issuer-paid underwriter model are what is here referred to as ‘pre-nancing vehicles’. Under the rm allocation, transactions are typically pre-nanced by way of

237See Sjostrom(2001) (‘Consequently, many companies that desire to go public are unable to do so because no underwriter will handle their oerings. e Internet, however, has changed this. Now a company can market its stock directly to the public by posting its oering document on the Web, making it accessible to hundreds of millions of potential investors […] Internet DPOs provide an example of “disintermediation,” the term used to describe the bypassing of middlemen that the Internet has enabled.’). 238See Sjostrom(2001) (‘e logic of Internet DPOs is straight forward; a company that cannot convince an underwriter to take it public can get around this obstacle by going public through an Internet DPO and save as much as 13% in underwriter commissions and expenses in the process.’).

67 the rm obtaining nancing in advance, without pre-specifying the nature of the ultimate investment. e idea of pre-nancing vehicles is to replicate this rm property through a market mechanism. In the sphere of equity oerings, special purpose acquisition companies (SPACs) provide a way through which investors can pre-nance a blank-check company with business activities, which will be determined at a later point by way of a merger.239 is passes underwriting costs on to investors and is has been said to enable faster and less expensive oerings.240 Similarly, in credit markets, the to-be-announced (TBA) market for agency mortgage-backed securities (MBS) provides for such a pre-nancing mechanism.241

1.7.4.2.2 Secondary Markets

Similar to primary markets, the cost burden in secondary markets is primarily shouldered by issuers: they pay the market maker and for the listing on a regulated exchange. Against this backdrop, an optimal regime would allow for some of these costs to be placed on investors instead:

• Plurality of trading venues: Before the advent of electronic trading, search costs were substantially higher. is provided the microeconomic incentives for single, centralized marketplaces to emerge and capitalize on this friction and create a high entry barrier for rival markets.242 Securities regulation of national securities ex- changes243 has originally been designed for this high-transaction cost environment. However, in the rst decade of the twenty-rst century, with the rise of crossing systems, dark pools and electronic communication networks (ECNs), there has been substantial fragmentation of trading venues.244 Menkveld(2013) reports that the NYSE’s market share in its listed stocks was still 80% in 2005 and declined to 25% in 2010. Conrad et al.(2003) report that the nine operating ECNs account for almost 40% of the Nasdaq trading volume. In the same vein, Buti et al.(2017) note that the 19 dark pools for which data is available account for more than 14% of consolidated equity trading volume. Notably, these alternative trading pools are nanced, not by the issuer, but by investors. An optimal regime would take account of these secular developments, in particular by enabling issuers to forgo a traditional listing and instead list exclusively on investor-nanced market venues.

• Plurality of liquidity providers: Closely related to the fragmentation of nancial markets, has been the fragmentation of liquidity providers. In particular, over the last decades, traditional market making rms have increasingly been replaced by high-frequency trading (HFT) rms.245 In a report by the SEC(2010), the Commis- sion has provided an estimate that these liquidity providers account for more than 50% of trading volume in equity markets.246 Carrion(2013) notes that HFT are generally considered to have become the dominant liquidity providers, although ones without armative obligations to provide said liquidity.247 While issuers typically pay

239See Dimitrova(2017) (‘Special purpose acquisition companies (SPACs) are blank-check companies that have no operations but go public with the intention of merging with or acquiring a company with the proceeds of the SPAC’s initial public oering (IPO) of shares.’). 240See Kolb and Tykvova´(2016) (‘e readily available liquidity may also provide existing SPAC rm shareholders the possibility of cashing out their holdings immediately at the SPAC acquisition. In addition, this route is expected to be relatively fast and cheap because SPAC rms do not have to undergo the lengthy and costly process of SEC registration; the SPAC vehicle has already gone through this process.’). 241See P. Gao, Schultz, and Song(2017) (‘ey are also traded in a forward market known as the TBA market where the seller has an option to deliver any MBS that meets agreed-upon criteria. Most trading takes place in the TBA market. Mortgage originators use TBA trades to sell mortgages forward and to hedge their production. Investors also use the TBA market to buy and sell already-issued MBS.’). 242See Pagano(1989) (‘In both cases the model predicts that when there is fragmentation, traders will cluster together according to the size of their desired transactions. One should expect to nd all the relatively small traders on one market and all the relatively large ones on the other market—or searching o the Exchange.’). 243Securities Exchange Act of 1934 section 6, 15 U.S.C. § 78f. 244See Conrad, Johnson, and Wahal(2003) (‘e emergence of alternative electronic trading systems over the past decade has radically altered institutional trading practices. Such trading systems are frequently grouped into two categories: crossing systems (such as ITG’s POSIT) in which institutional orders are brought together and “crossed” at some prevailing price, and electronic communication networks (ECNs), such as Instinet, that allow counterparties to trade anonymously at negotiated prices.’); Buti, Rindi, and Werner(2017) (‘Dark pools are Alternative Trading Systems (ATSs) that do not provide their best-priced orders for inclusion in the consolidated quotation data. ey oer subscribers venues where anonymous, undisplayed orders interact away from the lit market yet execute at prices no worse than the National Best Bid Oer (NBBO).’). 245See Menkveld(2013) (‘micro-economic analysis of its trading strategy shows that the HFT is primarily a modern, multi-venue market marker. Its‘operation’uses capital to produce liquidity.’). 246See SEC(2010) (‘Estimates of HFT volume in the equity markets vary widely, though they typically are 50% of total volume or higher. By any measure, HFT is a dominant component of the current market structure and is likely to aect nearly all aspects of its performance.’). 247See Carrion(2013) (‘ A commonview is that HFTs have taken over the market-making function. Under this scenario, they generally benet the market by increasing competition to provide liquidity, but there are still concerns that they lack the armative obligations that bound traditional market-makers and could cause disruptions by exiting the market at their discretion.’).

68 market-makers for the armative obligation to provide liquidity, the strong presence of HFT seems to indicate that investor-paid liquidity providers are able to provide sucient market liquidity. us, an optimal regime would reduce the emphasis on issuer-paid market makers and instead encourage the proliferation of investor-nanced liquidity providers.

1.7.4.2.3 Chapter 2: Optimal investment regime in the sphere of technology startups

Chapter 2 explores an optimal investment and liquidity regime in the sphere of technology startups. With respect to primary markets, the possibility of (i) means of direct market access and (ii) pre-nancing vehicles is analyzed. While there already exist examples for both in market-based startup nancings, such as the rise of direct listings and special purpose acquisition vehicles, the adoption of such cost-eective primary market mechanisms is still limited to small segments of the markets. With respect to secondary markets, the chapter nds that technology startup rms may prefer lower liquidity levels than the full liquidity option what is currently oered by default for public markets.

1.7.4.2.4 Chapter 4: Optimal investment regime in the sphere of credit

Chapter 4 explores an optimal investment and liquidity regime in the sphere of credit. With respect to primary markets, the possibility of (i) means of direct market access and (ii) pre-nancing vehicles are analyzed. While there already exist examples for both in credit markets, such as the direct sale of treasury securities or the to-be-announced (TBA) forward market for mortgage-backed securities, the adoption of such cost-eective primary market mechanisms is still limited to small segments of the credit markets. With respect to secondary markets, it is found that a wider adoption of central limited order books would optimize secondary market liquidity provision.

1.7.4.3 Least cost avoider

At the investment and liquidity layer, the issuer has traditionally carried the bulk of the costs, both in primary and secondary markets. Along with the disclosure cost burden, this may further discourage the decit agent from entering the public market in the rst place. us, the optimal regime presented above has explored ways through which these costs could be minimized and – to the extent possible and reasonable – transferred to investors.

• Primary markets: For primary markets, the two proposed mechanisms under the optimal regime are diametri- cally opposed with respect to the assignment of costs. While market access mechanisms place the entire costs of marketing and distribution of the oering on issuers, the pre-nancing vehicles instead place them on investors. However, this plurality in mechanisms is reective of the dierences that exist at the issuer level. For large and widely-known issuers, the direct market access mechanisms may provide an eective way to access capital markets. In contrast, smaller issuers may benet from the fast liquidity provided by pre-nancing vehicles.

• Secondary markets: Since the main beneciaries of secondary market liquidity are investors, which are enabled by secondary markets to enter and exit positions, the question arises whether they are also the least cost avoiders. Secular changes with respect to trading venues and liquidity providers indicate that investor-paid intermediaries already oer a large portion of secondary market liquidity. As a result, the fragmentation of both trading venues and liquidity providers may be a function of investors being the cheapest costs avoiders with respect to secondary market liquidity.

1.7.4.3.1 Chapter 2: Least cost avoider in the sphere of technology startups

Chapter 2 analyzes how the proposed least cost avoider regime at the investment and liquidity layer maps to the sphere of startups. In primary equity markets, issuers are traditionally seen as the least cost avoiders of liquidity provision costs, since they can choose both underwriter rms and listing exchanges. With respect to the costs of direct market access in primary markets, the chapter nds that the option of direct market access passes signicant additional distribution costs and price risks to the issuer. While this can be mediated to some degree by allowing the issuer to ‘test the market’

69 by seing (volume and price) acceptance thresholds, it appears that direct market access is most suitable for later stage (consumer) technology rms. For the smaller issuers, the traditional underwriter model may still yield beer results. In both cases, the least cost avoider appears to be the issuer. In contrast, pre-nancing vehicles pass the costs of liquidity provision on to investors, who nance startups on a forward seling, blind pool basis. With respect to secondary markets, where issuers currently pay market makers for liquidity provision, the option of passing liquidity costs on to investors is explored as investors appear to be the least cost avoiders.

1.7.4.3.2 Chapter 4: Least cost avoider in the sphere of credit

Chapter 4 analyzes how the proposed least cost avoider regime at the investment and liquidity layer maps to the sphere of credit. Similar to chapter 2, it is found that the direct market access option is most suitable for large credit issuers, which may be a chief reason why this has been pioneered in the sphere of U.S. Treasuries. For the smaller issuers, the traditional underwriter model may still yield beer results. For both, the direct market access and the traditional underwriter model, the least cost avoider appears to be the issuer. In contrast, pre-nancing vehicles, such as the to- be-announced (TBA) forward market for mortgage-backed securities (MBS), pass the costs of liquidity provision on to investors, who nance credit on a forward seling, blind pool basis. With respect to secondary markets, it is found that the least costs avoiders for liquidity provision costs are investors, which already seem to shoulder these costs in credit markets.

1.7.5 Application to the diversication layer

1.7.5.1 Misallocation externality

At the diversication layer, the relevant negative externality is misallocation. To understand the precise nature of this externality, let us again go back to our working denition of an externality: as outlined by Buchanan and Stubblebine (1962), an externality exists where the utility of a rm or individual is dependent upon activities, which are exclusively under his own control or authority, but also upon another activity, which is, by denition, under the control of a second rm or individual. If we look at the individual investor, he is able to control only his own investment. In a frictionless world, the investor could diversify the investment in ‘single player mode’ all on his own. However, given the transaction costs encountered at the investment and liquidity layer, the investor needs to collaborate with other investors to diversify eectively. e availability and the willingness of other market participants to pool their funds lies outside the control of the investor. e misallocation externality can thus be understood as excessive idiosyncratic risk exposure in the absence of suitable pooling partners. In this context, the ‘activity’ in the sense of Buchanan and Stubblebine(1962) is the provision of pooling funds by other market participants. It can be further distinguished between the following two types of misallocation:

• Market-induced misallocation: this misallocation results from the structure and the costs of the market. In particular, cases where frictions at the disclosure and investment layer lead to a situation where a large part of assets remain private are considered. As a result, the investor’s portfolio is underweight with respect to private assets.

• Firm-induced misallocation: this misallocation results from the dominance of the rm, which nances assets through the rm-structure that could otherwise be nanced through the market. As a result, the investor’s portfolio is underweight with respect to rm-nanced assets.

e both sources of misallocation presented above may clearly overlap, as most privately held assets are also rm- nanced. However, the distinguishing criteria between the two is investment access. In the case of market-induced misallocation, if friction at the disclosure and investment layer is reduced, decit agents may seek market-based funding instead. In contrast, for rm-induced misallocation, the rm actively outprices the market or shuts out market nancing. For example, in the case of bank loans, unique access through a bank’s branch network may completely shut out other

70 market-based nance providers. Similarly, in the context of startups, the reputation and value proposition of the venture rm may provide venture rms with a distinct competitive advantage over market-based nancing. e key insight of the second part of the eorem is that in the absence of transaction costs related to the market externalities, the legal assignment of rights and obligations under security laws does not aect the eciency of the nal allocation. For the diversication layer, the relevant transaction costs related to the misallocation externality are pooling costs. As we have seen under the rst part of the eorem, securities regulation aempts to remedy the presence of the misallocation externality through regulated pooling rms, which are compensated in practice by the surplus agent. e second part of this eorem thus holds that with pooling costs approaching zero, whether security laws require (i) the issuer (ii) or the investor to bear the costs of pooling does not aect the eciency of the nal allocation. In a positive transaction cost seing, however, the assignment of these costs by the law, whether through explicit or implicit assignment, does aect the nal outcome. In this respect, an optimal regime is one that minimizes the costs of the market and assigns them to the least cost avoider, the party that is best positioned to reduce the costs of the market. In the context of the diversication layer, the objective is to reduce pooling costs and assign these costs to the party best positioned to reduce them. In the below chapter, an optimal regime with respect to both market-induced misallocation and rm-induced misallocation costs is sketched out that could minimize pooling costs and allow for a more nuanced assignment of costs to the least cost avoider.

1.7.5.2 Optimal regime

To begin our enquiry into the optimal, it must rst be noted that the diversication layer is – as the ‘highest’ layer within the functional hierarchy developed under the eorem – in a sense ‘standing on the shoulders’ of both the disclosure and the liquidity layers. Where both of these other layers would be optimally designed, friction would be reduced to the minimum and the costs of diversication would have very lile marginal eect. e surplus agent could diversify in ‘single player mode’ without having to pool funds with other market participant. On the ip side, this positioning of the diversication layer also oers substantial opportunity for it to remedy some of the shortcomings of the other functional layers. In particular, by providing access to private assets on a pooled basis, the diversication layer can allow both investors and issuers to leapfrog over these lower-level frictions. e starting point of any meaningful discussion about an optimal diversication regime should be a common understanding of what optimal diversication entails. In section 1.4.3, it is highlighted that the dominant position held in the nance literature, in particular under the EMH and the CAPM, is that the market portfolio provides mean-variance ecient diversication. While the proposition that indexation is the best choice is not without its critics among scholars,248 we proceed by taking the market portfolio as our baseline when analyzing misallocation. us, if we consider an economy that consists of N assets, then the optimal portfolio is the market portfolio where exposure is equally split across all N assets. In this respect, misallocation can be understood as diversication across only n assets (with n

• Market-induced misallocation: under this type of misallocation, the investor’s portfolio is underweight (n

248See Stout(2003) (questioning the validity of the EMH ‘More recently, however, the idea of market eciency has fallen into disrepute as a result of market events and growing empirical evidence of ineciencies. is essay argues that the weaknesses of the ecient market theory are, and were, apparent from a careful inspection of its initial premises, including the presumptions of homogeneous investor expectations, eective arbitrage, and investor rationality.’).

71 end funds249 are not subject to the same liquidity constraints and can therefore oer investors more exposure to privately held assets. Since these funds do not oer and redeem shares, they can invest in illiquid assets on a pooled basis, without subjecting the underlying assets to manadatory disclosure obligations or liquidity requirements. us, the adoption of closed-end fund structures can allow investors to ‘leapfrog’ over frictions at the disclosure and investment layer, increase exposure to private assets and reduce market-induced misallocation.

• Firm-induced misallocation: under this type of misallocation, the dominance of the rm results in the investor’s portfolio being underweight (n

In summary, it appears that the diversication layer oers much room for market operators to cure the decits of securities regulation at the lower functional layers, namely the disclosure and investment layers. Existing security laws already oer closed-end fund structures, which are an eective way of dealing with market-induced misallocation. Under and optimal securities regime, the scope of closed-end fund structures could be further expanded by tailoring these funds structure to specic asset classes. Furthermore, security laws could reduce rm-induced misallocation by providing regulatory incentives (or obligations) for either decit agents or dominant rms to provide investment access to market-based nancing sources.

1.7.5.2.1 Chapter 2: Optimal diversication regime in the sphere of startups

Chapter 2 explores an optimal diversication regime in the sphere of technology startups. It is found that traditional open-ended mutual funds, which have increasingly moved into late stage nancing rounds of privately held technology startups over the past years (so-called crossover funds), are ill-positioned to nance such illiquid assets. us, with respect to market-induced misallocation, the possibility of specialized closed-end fund structures to nance privately held startups is explored. With respect to rm-induced misallocation, the possibility of encouraging (or requiring) startups or venture rms to provide such public pooling vehicles with investment access is discussed. While late stage startups have already built a record of obtaining nancing from such pooling vehicles, investment access at the venture capital rm level is more exploratory in nature.

1.7.5.2.2 Chapter 4: Optimal diversication regime in the sphere of credit

Chapter 4 explores an optimal diversication regime in the sphere of credit markets. It is found that diversication vehicles for public xed-income securities, such as corporate bonds and treasuries, oer highly ecient diversication menus. However, with respect to credit traditionally originated through banks, such as mortgages or consumer loans, eective diversication vehicles are still nascent. In particular, diversication is typically ‘stacked’, with asset-backed securities at the boom and mutual funds or ETFs on top. is architecture leads to a maturity mismatch at the pool level and puts liquidity restrictions on the underlying assets. us, with respect to market-induced misallocation, the possibility of establishing specialized closed-ended fund structures, which eectively act like a maturity-matched bank balance sheets, is explored. With respect to rm-induced misallocation, the possibility of encouraging (or requiring) borrowers or banks to oer credit to a competitive marketplace at the time of origination is discussed.

1.7.5.3 Least cost avoider

24915 U.S.C. §80a–5 (a)(2).

72 assess (i) whether this unilateral assignment of costs is ecient and (ii) to potentially conceive a more nuanced way of assigning these costs to both investors and issuers. Market-induced misallocation Investors are the ultimate economic beneciaries of optimal diversication, as they bear the losses from owning a portfolio that is not mean-variance ecient. As such, they are directly incentivized to drive allocation to the optimum. It thus seems logical that they should be the least cost avoider when it comes to the market-induced misallocation. Where their portfolio is underweight (n

• Issuer-sponsored diversication vehicles: thereby issuers would be required to pay the fees of diversication vehicles, investing in their shares or credit securities, on behalf of investors. At rst sight, such a cost assignment may seem preposterous. Why would issuers have any incentive to shoulder such costs? Yet, a number of empirical studies have found that stocks included in the Standard and Poor’s (S&P) 500 index report an ‘index addition eect’, which can result in a substantial stock price increase aer the announcement of the addition, with a fraction of this eect being permanent.250 Against this background, one could very well imagine that surplus agents could have an incentive to pay for the inclusion in indexed pooling vehicles, where such an inclusion conveys signal and price advantages. However, such issuer-sponsored diversication vehicles could also lead to adverse selection, namely aracting low-quality issuers wishing to gain a price advantage. us, index inclusion should be limited to high quality issuers. In summary, for some segments of the market, issuer-sponsored diversication vehicles may provide an alternative to the traditional cost assignment to investors.

• Internal diverication: thereby the issuer reduces market-induced misallocation by way of rm-level diversication, or ‘internal diversication’. Such internal diversication is regularly encountered in mature industrial conglomerates, such as General Electric. ere is a long-standing debate in the nance literature, whether such internal diversication increases rm value (diversication premium)251 or decreases rm value (conglomerate discount)252 and whether, as a result, we should have smaller, specialized rms, or larger, diversied conglomerate rms. In the area of technology startups discussed in chapter 2, for example, companies like Google, Facebook or Amazon provide an example where mature public technology rms oer considerable diversication through internal, rm-level diversication. us, such internal diversication eectively re-transitions assets into a larger rm structure: either a holding structure in the case of equity investments, or a bank allocation in the case of credit securities. However, while such internal diversication may be considerable, it will never provide a fully indexed exposure to the market, N, and can thus only complement investor portfolios.

In summary, while both investors and issuers could in theory reduce market-induced misallocation, surplus agents seem to be beer positioned and incentivized to do so in practice. Firm-induced misallocation 250See Shleifer(1986) (nding an abnormal price increase of 2.79 percent on the day following the announcement ‘Since September, 1976, stocks newly included into the Standard and Poor’s 500 Index have earned a signicant positive abnormal return at the announcement of the inclusion. is return does not disappear for at least ten days aer the inclusion.’); L. Harris and Gurel(1986) (nding an abnormal price increase of 3.13 percent ‘is problem is addressed in an examination of prices and volume surrounding changes in the composition of the S&P 500. Since these changes cause some investors to adjust their holdings of the aected securities and since it is unlikely that the changes convey information about the future prospects of these securities, they provide an excellent opportunity to study price pressures. e results are consistent with the price-pressure hypothesis: immediately aer an addition is announced, prices increase by more than 3 percent. is increase is nearly fully reversed aer 2 weeks.’); Beneish and Whaley(1996) (nding an abnormal price increase of 4.388 percent ‘is study analyzes the eects of changes in S&P 500 index composition from January 1986 through June 1994, a period during which Standard and Poor’s began its practice of pre-announcing changes ve days beforehand. e new announcement practice has given rise to the “S&P game” and has altered the way stock prices react. We nd that prices increase abnormally from the close on the announcement day to the close on the eective day’); Wurgler and Zhuravskaya(2002); Lynch and Mendenhall(1997). 251See Villalonga(2004) (nding empirical evidence for a diversication premium). 252See Lang and Stulz(1994); Berger and Ofek(1995); Servaes(1996) (all nding a diversication discount).

73 Firm-induced misallocation results from the dominance of rm-based nancing for a specic segment of the market. In this respect, as suggested by the proposed optimal regime above, either (i) decit agents (issuers) or (ii) rms appear to be best positioned to reduce such misallocation. As the economic beneciaries of rm-based nancing, decit agents are the decision agents when it comes to obtaining funding through the rm or the market. Where the oering of the rm is more readily available and at beer terms than market-based nancing, they will rationally prefer rm-based sources of capital. us, to short-circuit the rm-based ow of funds from issuers to rms, either issuers can be targeted directly, or investment access is provided at the rm-level. Given that their contractual relation is at the center of this misallocation, it appears that these two parties are also the lowest cost avoiders. Again, we should consider the alternative cost assignment. In particular, we should ask whether and how the investors could minimize this type of misallocation and at what costs. As mentioned above, the issuer will rationally prefer rm-based nancing over market-based nancing, where the oer on hand is more convincing. us, it appears that market-based nancing providers, through their pooled investment vehicles could try to compete on terms with rm- based nancing providers. While this may work for some segments of the market, the value proposition of specialized rms may be too strong in others or the market-based nancing providers may not have the scope to reach the decit agent in the rst place. From this, it appears that the costs of market-based nancing providers to reduce rm-induced misallocation is prohibitively large, given that the rms they are competing with have specialized resources (e.g. venture rms oering signal and expertise) or greater reach (e.g. banks with a national branch network).

1.7.5.3.1 Chapter 2: least cost avoider in the sphere of technology startups

Chapter 2 analyzes how the proposed least cost avoider regime maps to the sphere of startups. It is found that for market-induced misallocation, which results from inadequate diversication, due to the market’s structure and costs, investors appear to be the least cost avoiders. While the availability of pooling vehicles that provide access to privately held technology startups is still very limited, investors are best positioned to encourage the formation of such vehicles by providing liquidity to the existing ones. Similarly, for rm-induced misallocation, which results from limited investment access to startup investments because of dominant VCs, either startups or venture rms are the least cost avoiders. In particular, this relates to situations where the exposure of public pooling vehicles to privately held startups is inef- ciently underweight. In these scenarios, either startups or venture rms are best positioned to alleviate misallocation by providing market-based investment access.

1.7.5.3.2 Chapter 4: least cost avoider in credit diversication

Chapter 4 analyzes how the proposed least cost avoider regime maps to credit markets at the diversication layer. It is found that for market-induced misallocation, which results from inadequate diversication due to the credit market’s structure and costs, creditors appear to be the least cost avoiders. As is currently the case, they bear the costs of pooling providers, such as xed-income mutual funds or ETFs, and are best positioned to deploy funds to the providers who diversify most eciently. Similarly, for rm-induced misallocation, which results from limited investment access to bank-originated credit, it is found that either decit agents or banking rms are the least cost avoiders. is relates to situations where banks originate credit to long-standing costumers or through a dominant bank branch network. In such situations, either borrowers or banking rms are best positioned to alleviate misallocation by providing market-based investment access.

1.8 Application across asset classes

e Coase eorem of Securities Regulation presented in this chapter is an abstract model, not tailored to a specic jurisdiction or asset class. As such, it is a broad framework, which can guide policy considerations in the hypothetical. Yet, it is in the concrete application, where the theory really shines. In particular, it is at the level of the individual asset class, placed in a particular jurisdiction, where the legal eects of securities regulation are most palpable. rough the

74 application of the theory to concrete institutional seings, it is possible to conduct a principles-based assessment of existing allocative structures and at the same time provide the starting point of any inquiry into the transition from one allocative structure to another (disintermediation). In its (aspiring) role as a foundational theory in the domain of market regulation, the theory can oer guidance and logical structure for understanding the role of securities regulation in relation to the existing modes of allocation. is means that by applying the eorem, the policy analyst may be able to explain, why a particular rm or market structure has established itself under existing legislative conditions and at the same time conceive actionable recommendations for legislative changes.

1.8.1 Asset classes analyzed within the scope of this PhD thesis

Within the scope of this PhD thesis, the theory is applied to two principal asset classes from both the equity and credit side: equity investments in emerging technology rms and structured credit. On the equity side, chapter 2 looks at the ‘asset class’ of early stage technology startup rms. In particular, it looks at equity investments in Silicon Valley technology start-ups, through either venture capital rms or the public markets. As an asset class, this provides a particularly interesting use case for the eorem, since this is an asset class where a gradual transition from a market-based to a rm-based allocation has taken place over the past decades. Given the outsized innovation output of rms emerging from this technology cluster, the policy questions of where value should be captured, through the VC rm structure or in public markets, becomes increasingly relevant. On the credit side, chapter 4 looks at structured credit transactions, such as asset-backed and mortgage-backed securities, which relate to small lot sized loans that have traditionally been nanced by banking rms. Given that this market allocation has been fraught with design errors that have put it at the center of the last global nancial crisis, this asset class provides another interesting application of the eorem. In particular, the analysis of these credit assets through the lens of the eorem aims to provide us with novel insights on the legal challenges of transitioning these credit assets from a rm-based to a market-based allocation.

1.8.2 Application to other asset classes

Beyond these applications, there exist a wide range of potential asset classes to which the eorem could be applied, ranging from real estate, private equity, infrastructure to insurances. Without going into greater detail, but a few are highlight below. On the equity side, the allocation of more mature companies through private equity rms could provide another interesting application of the eorem. Leveraged-buyout (LBO) funds regularly take public companies private. us, in the reverse order of venture rms taking startups public, they transition market-based rms into a rm-based allocation. As such, an analysis under the eorem could reveal how security laws can foster both the formation of such private equity funds and the viability of an investment strategy that arbitrages price dierentials between the rm and the market allocation. Another asset class on the equity side is the infrastructure asset class. In this respect, infrastructure assets, such as toll roads, railways or airports, are regularly transitioned from public ownership to a private allocation. e resulting private allocation can either be rm-based, through infrastructure funds that act much like private equity funds, or through the public markets. With respect to these privatizations, an application of the eorem may reveal interesting dynamics, given that publicly held infrastructure assets are regularly governed by a specic set of regulations. On the credit side, there exist a number of niche credit asset classes, such as distressed and mezzanine debt, which are currently nanced by specialized investment rms. ese credit assets typically require active management for the investment strategies to play out. For example, through loan-to-own strategies, investment rms may seek to convert credit into equity (debt-to-equity swaps). Given the complex legal nature of such transactions, the rm-based allocation oen outperforms a market-based allocation. By applying the eorem to this asset class, frictions in the regulation of such assets could be identied. For example, the absence of ‘cram down’ provisions in market-based debt can be a hurdle to coordinating the restructuring of market-based debt. us, an analysis under the theory could help to identify

75 ways in which security laws (in combination with corporate and bankruptcy laws) could foster a more market-based allocation of this high-yield credit asset class. Lastly, beyond credit and equity, insurance contracts could provide another interesting use case for the developed theory. With the exception of a small segment of insurance-linked securities (ILS), these contracts are largely allocated through the rm structure. As a whole branch of the nancial services industry, the insurance industry is, much like the banking industry, subject to its own set of industry-specic regulations. An application of the eorem to this asset class could reveal ways in which securities regulation competes with insurance regulation and how it may foster a more market-based allocation of insurance contracts.

1.9 Conclusion

e chapter makes three distinct contributions to the scholarly literature dealing with securities regulation. Firstly, it introduces the three functional layers of securities regulation as the main units of a regulatory analysis of security laws. While the functions, institutions and regulations across these layers are well-documented, the literature so far has failed to bundle them into a common set of reference points. Introduction of the three layers provides much-needed structure and a regulatory framework that allows scholars and policy makers alike to disentangle regulatory functions and objects across dierent market activities. Secondly, within the rst part of the eorem, the chapter introduces a novel perspective on securities regulation, which makes security laws an integral part of explaining why certain transactions are allocated through the rm or the market. e rst part of the eorem views the rm and the market as being governed by competing legal regimes, which impose dierent prices on economic transactions. In this regard, securities regulation determines the price of the market, while rm-specic regulation and exemptions from security laws determine the regulatory costs of the alternative rm seing. Based, in part, on these regulatory prices, economic actors chose to allocate through the market or the rm. us, the observed allocation mechanism can be regarded as a representation of rational preferences of economic actors, given the existing legal seing. is macro-perspective on securities laws provides a major contribution of this chapter, which can provide security scholars with a robust theoretical framework that put markets at the center of the analysis. As such, it may encourage the literature to look beyond the outdated perspective that the main regulatory objective of security laws should be the protection of investors. irdly, through its second part, the eorem re-conceptualizes the costs of the market – and more specically the costs of securities regulation – as externalities. is allows us to make these costs the main unit of analysis and place them within the classical seing of the original Coase eorem. Applying the original Coase eorem in this context leads us to a number of novel insights. Just like the ‘invariance proposition’ of the original Coase eorem states that in a frictionless world, the assignment of legal entitlements or obligations will not aect the nal allocation of resources, we can conclude that in the absence of transaction costs, it does not maer to which party security laws assign the costs of the market. At the disclosure layer, for instance, in a perfect information seing, it does not maer whether security law mandates the issuer to make certain disclosures or whether it imposes a burden on the investor to make inquiries into the issuer. Stated dierently, the second part of the eorem leads us to the realization that the role of securities regulation will gradually decrease with a secular fall of transaction costs. In the absence of a frictionless environment, the theory further builds on the normative work of Calabresi and develops an objective function of securities regulation, which dictates that security laws should minimize market costs and assign them to the least cost avoider. Without applying it to specic asset classes, the second layer of the eorem thus provides us with the foundations for a more granular cost- benet analysis tool for securities regulation. In particular, it develops a policy tool that is rooted in established theories of law and economics and which can guide the policy analysis of concrete security laws along the three functional layers.

76 Chapter 2

e Problem of Startup Disclosure Cost

77 2.1 Introduction

In recent years, the market capitalization of ‘big tech’ companies, in particular the FAANG conglomerates,1 which were founded in recent decades and scaled in the public markets since, has surpassed2 the dominant industries of past centuries, including banking, energy and media. At the same time, newly created venture-backed technology startups emerging out of the Silicon Valley ecosystem constantly keep challenging incumbents across all industries through innovative business models and digital delivery of products and services. In the course of this secular transition, signicant monetary value has accrued to shareholders of the breakout technology startups that have been at the center of this industrial displacement. However, unlike in previous eras, a large part of the economic value has been realized outside the scope of the public markets and was instead captured by a closed circle of private venture investors.3 Specialized venture capital rms, rather than stock markets open to retail investors,4 have emerged as the major catalysts of high-growth tech companies, fueling the ‘scale up’ of early stage tech startups through increasingly large funding rounds.5 However, this transition from public markets to private venture rms has not been privy to early stage tech companies alone, but has also permeated the tail end of tech companies’ lifecycle, as an increasing number of exits by late-stage tech companies have gradually moved from initial public oerings (IPOs) to private acquisitions by industry incumbents.6 is urges the question why it is the case that early stage tech founders nowadays prefer to fund their startups for decade-long stretches through brand name Sand Hill Road rms, such as Sequoia Capital, Accel or Kleiner Perkins, and at the later stage oen prefer to get acquired by large technology conglomerates,7 rather than tapping the public markets like they used to do? While the increased availability of capital at the later stage is a recent macro phenomenon that has been driving this trend, this chapter focuses on the role of U.S. security laws, which are widely viewed as a critical factor8 in the entrepreneur’s decision-making process and a key policy lever for fostering and reviving public market allocation and broadening access to the value created by technology rms. While security law in the United States have been rather static since the 1930ies, the JOBS Act9 was signed into law in 2012 as the rst major revision in decades, aer substantial lobbying of the venture capital and technology industry with the explicit purpose of encouraging technology startup funding.10 However, many of the key revisions11 implemented through the JOBS Act have had the opposite eect

1e acronym ‘FAANG’ refers to the stocks of Facebook (FB), Amazon (AMZN), Apple (AAPL), Netix (NFLX); and Alphabet (GOOG) (formerly known as Google). 2See Wigglesworth(2018) (‘In total, the ve Faangs now have a total market capitalisation of $3.35tn — making them bigger than the entire FTSE 100, Hong Kong’s Hang Seng index or France’s Cac 40.’). 3See Waters and Foley(2015) (‘e renewed burst of private capital-raising has provided fresh evidence of how the tech industry’s nancing boom has been limited to a charmed circle of investors able to get a foot in the door of the hoest new prospects. All the risks of this tech cycle are being shouldered by a far narrower circle of investors — but so are the rewards.’); Rodrigues(2013) (‘us, the overall impression may be of an America where those who are already wealthy can cash in on investments that make them even wealthier, while average Joes can only press their noses up against the glass and await an IPO-at which point, as with Facebook, the big money has already been made.’). 4See Waters and Foley(2015) (‘is has led to a “bifurcated market”, says Mr Kupor at Andreessen Horowitz, one of the VCs that has invested most aggressively in the boom. “We’re certainly closing o a large part of the market to the broader retail [investor] base.” Private investors like Andreessen, he adds, will benet “disproportionately”.’). 5See Rowley(2018) (‘ere was a time not so long ago when nine-gure venture capital rounds weren’t a near-daily feature of tech business news. But now funding rounds of $100 million or more cross the wires with stunning frequency. e era of supergiant rounds is now the new normal. is is aributable, in part, to billions of dollars owing into new venture capital funds — the largest of which are raised by the oldest, most entrenched rms — and competition from relative newcomers, like SoBank.’). 6See Bayar and Chemmanur(2011) (‘According to the National Venture Capital Association (NVCA), there were more exits by VCs through acquisitions than by IPOs in each of the last 11 years. e NVCA reports that in 2010, while acquisitions of venture-backed rms with disclosed values accounted for $18.31 billion in value, IPOs of venture-backed rms accounted for only $7.02 billion.’). 7With some of these tech acquisitions even occurring shortly before an already initiated IPO, see Gonzalez(2018) (‘Earlier this month, SAP acquired survey soware maker altrics for $8 billion, only a few days before it was set to go public on the Nasdaq. In June, Workday acquired Adaptive Insights, a nancial planning startup, two days before its planned IPO, and in 2017, Cisco bought soware analytics startup AppDynamics for $3.7 billion – the night before it was scheduled to go public.’). 8See A. Schwartz(2019) (‘Mandatory disclosure imposes signicant costs – it costs millions of dollars to produce the necessary disclosures for an IPO (initial public oering), not to mention the ongoing costs of quarterly and annual reporting – to the point that the rule eectively excludes startups and small businesses from going public.’). 9Jumpstart Our Business Startups (JOBS) Act, Pub. L. No. 112-106, §§301–05, 126 Stat. 306, 315–23 (2012) (codied in scaered sections of 15 U.S.C.) (hereinaer “JOBS Act”). 10See Berdejo(2015) (‘e Jumpstart Our Business Startups Act of 2012 (JOBS Act) represents one of the most comprehensive overhauls of the securities laws in recent years. One of the principal goals of the JOBS Act is to improve access to the capital markets for smaller issuers, referred to in the Act as emerging growth companies, or EGCs.’). 11In particular, by quadrupling the threshold of registered shareholders from 500 to 2000 shareholders before a company is required to le an S-1 for going public with the SEC. See JOBS Act § 201(a) and 17 C.F.R. § 240.12g-1(b)(1). Also, by abandoning the ban on general solicitation for Reg

78 of encouraging public markets and have instead entrenched startups further in the venture rm structure. An industry study conducted by McKinsey in 2016 has investigating why soware companies have been staying private for longer streches of time. e study identied that security laws can be seen both as a primary driver12 for companies staying private longer, as well as a main factor of ultimately forcing them to go public.13 Against this backdrop, the chapter investigates the role of securities regulation in technology startups’ rm-versus- market allocation choice, using a law and economics framework developed in the rst chapter of this PhD thesis, the Coase eorem of Securities Regulation (the “eorem”). In applying this eorem, the chapter in a rst part investigates the dierent costs placed on startups by security laws – both direct and indirect costs – if they allocate their shares through the market, as opposed to the venture rm structure. In a second part, the chapter further breaks down the regulatory costs of the public market into its constituent components and granularly analyzes dierent means of optimizing security laws – both through a reduction in regulatory costs and through the re-assignment of costs to the least cost avoiding party. e overarching goal of this chapter is to provide a novel perspective on securities regulation by shedding light on the role it plays in fostering technological innovation and capital formation in venture deals. Moving beyond the merely hypothetical, the chapter strives to provide actionable policy suggestions for a more eective securities regulation of startup equity through novel forms of disclosure, liquidity provision and diversication. Within the limitations of such a sweeping analysis, the chapter addresses a number of idiosyncratic regulatory challenges of the emerging technology asset class and identies multiple policy levers that should be considered by policymakers and researchers alike.

2.1.1 Research process

As part of the author’s PhD research on this chapter and the following chapter 3 of this thesis, time was spent between 2016 and 2017 in the San Francisco Bay Area, formally as a PhD visiting scholar at U.C. Berkeley. During this time, the author studied technology startups, the venture capital ecosystem and the laws and regulations governing startups ‘on the ground’. Local qualitative research was conducted through informal interviews with entrepreneurs, venture capitalists, angel investors, security lawyers, academics and early startup employees. is has provided insights into the inner workings of building, nancing and scaling breakout technology startups. Most of these interviews have been conducted informally, predominantly in the context of lectures, telephone conversations, tech conferences and meetups, and as such have informed the broader understanding of the subject maer, rather than answering specic questions. A selection of these interviews have been conducted and recorded over Skype and have since been made available to a broader audience through a curated video library.14

2.2 Allocation mechanism

is chapter contrasts the allocation of early technology rms through the public stock markets with the rm allocation through the venture capital rm. is section aims to provide a high-level overview of the respective allocation form.

D oerings, the JOBS Act has given rise to a prolic (accredited) angel investor segment that has allowed startups to defer later stage institutional funding rounds and ultimately going public. See JOBS Act § 201(a)(1) and 17 C.F.R. § 501(a). Also see Constine and Ferenstein(2013) (correctly predicting the rise of larger angel rounds and angel intermediaries such as Angelist ‘General solicitation will fuel a new coage industry of investor matching-making sites that aim to broaden the investment pool to nancial whales outside the insular world of Silicon Valley.’). 12See Erdogan, Kant, Miller, and Sprague(2016) (‘ere are several reasons for this new dynamic. e US Jumpstart our Business Startups (JOBS) Act, which passed into law in 2012, increased fourfold the maximum number of shareholders a company can have before it must disclose nancial statements.’). 13See Erdogan et al.(2016) (pointing to the registered shareholder threshold mentioned above ‘Despite the benets, few soware companies can stay private indenitely. Unless they become acquisition targets (which is challenging at sky-high valuations), most that survive and thrive in the private market should eventually expect to go public. Two factors oen make an IPO inevitable. First, if a company exceeds the maximum number of shareholders allowed as a private entity, it will be forced to go public.’). 14is video library is accessible through the following link: hps://channel.sandhillroad.io.

79 2.2.1 Market allocation

Within the scope of this chapter, market allocation relates to the public stock market allocation. As will be shown, public markets are notoriously complex, multi-layered and highly regulated. Whereas during the Dotcom boom of the late 1990s, hundreds of technology startups went public every year,15 a decade later there were only a handful of VC- backed IPOs every year.16 During the Dotcom era, it still used to be the greatest dream of startup founders to build a company and take it public on Nasdaq or the NYSE through an initial public oering (IPO), underwrien by a leading investment bank, such as Goldman Sachs or Morgan Stanley. In contrast, recent cohorts of entrepreneurs have become increasingly skeptical of the process and consequences of going public. In a rst step, going public requires the startup to document every internal process, prepare lengthy reports for auditors and lawyers and engage in a cumbersome ling process with the SEC. At a second step, startup issuers engage with the market through investment banks and oen leave considerable money on the table through underwriting fees and IPO underpricing. Once public, the startup is exposed to the rigidities of the public price mechanism and constant ling requirements to keep investors informed. Investors, in turn, are largely comprised of large pooling vehicles, such as mutual funds and pension funds, which may lack a true understanding for either the startup or its long-term strategy. is makes the public markets a challenging environment that only few founders still aspire to operate in.

2.2.1.1 Market regulation

2.2.1.1.1 Traditional securities regulation

As outlined in more detail in chapter 1 of this PhD thesis, the public market allocation is governed by ‘traditional’ security laws, in particular the Securities Act 1933, the Securities Exchange Act 1934 and the Investment Company Act 1940. ese regulations impose costs on the involved market participants at dierent ‘functional layers’ of the market, in particular the disclosure, the investment and the diversication layer.

2.2.1.1.2 JOBS Act

In 2012, the Jumpstart Our Business Startups (JOBS) Act17 was passed to reduce the costs of traditional securities regulation and encourage capital formation for startups through the market. In particular, the regulation introduced reduced disclosure obligations for ‘emerging growth companies’ (ECG), a ‘mini-IPO’ through Reg A+ oerings and an internet equity crowdfunding option through Reg CF. However, the JOBS Act at the same time also amended the ‘500-shareholder- rule’ under Section 12(g) of the Securities Exchange Act, which previously forced technology startups, including Apple, Google and Facebook, into the public sphere. By raising the threshold to 2’000 shareholders, the JOBS Act eectively allowed startups to stay private for longer, entrenching them deeper into the venture rm allocation structure.

15See Cumming and MacIntosh(2004), who cite statistics, showing 273 VC-backed IPOs for 1999 and 261 for 2000, respectively. 16See, for example, Ibrahim(2012) (citing statistics, showing 6 VC-backed IPOs for 2008 and 12 for 2009, respectively). 17Jumpstart Our Business Startups (JOBS) Act, Pub. L. No. 112-106, §§301–05, 126 Stat. 306, 315–23 (2012) (codied in scaered sections of 15 U.S.C.).

80 2.2.2 Venture rm allocation

Venture capital rms are professionally managed pools of capital that are invested in private technology startups at various stages in a company’s lifecycle and across dierent industry verticals. Unlike public market fund vehicles, venture capitalists are known to be actively involved in the management of the startups they fund. From the perspective of the startup, venture capital rms are capital bundled with advice and business support.18 In addition to their formal roles on the startup’s board, venture investors actively assist the companies with the hiring of key employees, they make introductions to potential clients and partners and oen help founders when it comes to securing follow-on nancing. e prevailing organizational form in the industry is the limited partnership, whereby the fund managers act as general partners (GPs) and the outside investors as limited partners (LPs).19 ese partnerships have an average term of roughly ten years.20 e venture fund partnership is a contractual arrangement that sits between outside investors that supply capital (limited partners), fund managers (general partners) and the nanced startup rms. Venture capital partnership have a fee structure that goes to the GPs that depends on both the size of the total fund (management fee) and a performance related fee (carried interest). Under the traditional 2/20 system, an annual management fee of typically 2% is charged, used to cover operating expenses, pay salaries, benets and other compensation, while the performance fee is typically 20% above an 8% preferred return hurdle (with 100% catch-up).21 While venture capitalists screen hundreds of potential startup investments per year, they typically invest in only a handful of deals per partner. Because venture rms typically provide hands-on advice and support, venture capital funds have size limitations for a given strategy.22 Furthermore, the nature of nancing provided at the level of the startup is typically staged, meaning that startups are nanced in installments.23 Follow-on nancing is only provided where startups show promising signals or hit pre-specied milestones set by the VC rms.24 Over the past decades, venture rms have managed to raise increasingly large funds. In addition to the agship early stage funds, many of the marquee venture capital rms now have an additional ”growth”, ”opportunity” or ”select” fund oering, which allows them to nance startups beyond just the early product-market t stage. Partially as a market function, where the supply of capital grows faster than the supply of high-quality startup companies, venture capital rms and their nancing term have become more ‘founder-friendly’. is has arguably turned venture funding into ‘soer’ capital relative to previous technology cycles, in particular the Dotcom boom. As a result of venture nancing becoming a ‘calmer’ place and venture rms being able to support breakout companies over multiple nancing rounds (oen through their growth capital funds), founders have increasingly been able to stay private and scale in the shadows of the public markets.

2.2.2.1 Venture rm regulation

Venture capital rms largely operate in the ‘shadows of securities regulation’ by relying on the exemption from registering with the SEC under Rule 506(b)25 of Regulation D26 when raising their funds. As will be shown in this chapter, this allows the funds to pre-raise funds and deploy capital in a very time-ecient manner. All of this while escaping the

18See Gil(2018) (Interviewing Naval Ravikant, founder of Angelist, on this aspect ‘Fundamentally, venture capital is a bundle—it’s a bundle of advice, control, and money. e more options you have, the more you can unbundle those three things, and get the advice from the people you want and the money from the cheapest source of money, and leave the control behind.’). 19See Sahlman(1990) (‘e relationship between investors and managers of the venture funds is governed by a partnership agreement that spells out the rights and obligations of each group.’). 20See B. Black and Gilson(1998) (‘e limited partnership agreement typically sets a maximum term for the partnership of 7—10 years, aer which the partnership must be liquidated and the proceeds distributed to the limited partners.’); Klausner and Venuto(2013) (‘Venture capital funds are limited partnerships with a term of roughly ten years.’); Phalippou and Goschalg(2009) (‘e typical private equity fund partnership contract stipulates that funds have a life of 10 years, with a possible extension of 3’); Kupor(2019) (‘Most venture funds have a ten-year life with two or three one-year extension periods.’). 21See Cumming and Dai(2011) (‘e typical compensation package for VCs consists of management fee which is a xed percentage (typically 2%) of the fund’s capital and ”carried interest” which is a xed percentage (oen 20%) of prots as investment returns are realized.’). 22See Bernile, Cumming, and Lyandres(2007) (‘Because the number of experts is limited, venture capital is not easily scalable. e higher the number of ventures nanced and advised by a VC, the lower the amount of advice provided to each venture.’). 23See R. J. Gilson(2003) (‘e initial venture capital investment usually will be insucient to fund the portfolio company’s entire business plan. Accordingly, investment will be “staged.”’). 24See Sahlman(1990) (‘e most important mechanism for controlling the venture is staging the infusion of capital.’). 2517 C.F.R. § 230.506(b). 2617 C.F.R. § 230.500.

81 rigidities of public disclosures with the SEC and the price mechanism of the public markets.

2.3 Silicon Valley technology startup nancing over time

2.3.1 Startup nancing during the Dotcom era

e Dotcom boom, which started in the mid-1990ies and ended in the 2000s, famously saw the quick rise and fall of a plethora of rst wave internet and technology startups. During this time, both the venture capital industry and the startup ecosystem looked very dierent from today. Venture capital was a small niche asset class, raising funds in the lower $100m,27 rather than billions, as is oen the case today. With respect to the startup ecosystem, a lot of the ‘plumbing of the internet’, such as scalable cloud server infrastructure, open-source building blocks for developing web applications, did not exist yet, making it costlier for people to build and launch the rst version of their product. To highlight a few salient features of the Dotcom era, one can point to:

• ick road to IPO and fewer funding rounds: With a median age of 5 years at IPO, the time from founding a startup to going public was remarkably short during the Dotcom bubble, less than half of what it is today (Rier, 2019). As a result, there were fewer private funding rounds. Typically, startups went public aer their Series C at the latest. An illustrative example of this ‘fast track to IPO’, is the well-known e-commerce company Amazon, which went public in 1997, just three years into its existence and aer having raised just one institutional venture round, an $8m Series A from Kleiner Perkins the year before the IPO.28

• Higher funding requirements: Coyle and Green(2014) hold that in order to successfully establish a soware company in the 90ies, ‘serious money’ was required, with the conventional wisdom being that a startup had to raise between $3m to $5m in venture funding, just to test the viability of their idea. is corresponds with the experience of tech-entrepreneur-turned-venture-investor Mark Suster, general partner at Upfront Ventures (LA’s largest venture rm), who breaks down the startup costs of his own tech startup during the Dotcom era as follows: ‘When I built my rst company starting in 1999 it cost $2.5 million in infrastructure just to get started and another $2.5 million in team costs to code, launch, manage, market and sell our soware. So it’s unsurprising that typical “A rounds” of venture capital were $5–10 million. We had to buy Oracle database licenses, UNIX servers, a Sun Solaris operating system, web servers, load balancers, EMC storage, disk mirrors for redundancy and had to commit to a year-long hosting agreement at places such as Exodus.’ (Suster, 2011).

• Pre-product nancing: During the Dotcom boom, it was still customary to raise venture funding on the basis of a business plan29 or sometimes a mere idea,30 as opposed to a live product with initial revenue traction or even a minimum viable product (MVP). As a result of the rush to public markets and the higher funding requirements, there was arguably less focus than today on building and perfecting the product before major nancing was deployed. us, many startups ended up scaling up operations quickly, oen with a suboptimal product and

27See NVCA(2019) and NVCA(2011) (reporting an average fund size in the U.S. venture industry of $35.4m in 1988, $84.6m in 1998 and $203.3m in 2018). 28See Roso(2016) (‘Later, in June 1996, Amazon raised an $8 million Series A from Kleiner Perkins, and that was its only VC investment before going public.’). 29See Rosenstein(1988) (describing the business plan as the key element for fundraising ‘e business plan which is a key element in obtaining initial funding is in eect a statement of strategy that is carefully scrutinized by the venture capitalists, some of whom will assume positions on the board of directors.’). 30See Jehane Noujaim and Chris Hegedus (Directors)(2001) (e documentary ‘Startup.com’ portrays the quick rise and fall of a startup (GovWorks) at the peak of the Dotcom bubble. At minute 11:25 of the documentary, co-founder Kaleil Tuzman is shown in the car on his way to a pitch meeting with Kleiner Perkins talking to his co-founder ‘I want to pass on some good news, I can hardly contain myself. I got a lunch at Kleiner Perkins at noon. I just got o the phone with the guy there and he said ‘we really like your idea, we know everybody in the Valley, we just want to talk about the idea, we’ll help you shape the idea, if we are able to do this, we can move extremely fast.’ Notably, the startup was at the idea/pre-product stage at the point of pitching VCs, which is a rather uncommon occurrence in today’s funding environment. While Kleiner Perkins passed on them following said meeting, the company managed to raise and quickly burn through c. $60m in venture and growth funding from venerable venture and private equity rms, including Mayeld and KKR.).

82 before the notorious ‘product-market t’31 or with negative ‘unit economics’.32

• Dominant venture capital rms: During the Dotcom era, venture capital funds oen assumed the role of dominant capital providers. As portfolio company board members,33 they were known to quickly replace founders with professional managers,34 cut future funding when founders would not meet milestones35 and impose stringent nancing terms, which could sometimes heavily dilute36 founders over time.

• GPs with traditional nance backgrounds: In line with the picture painted above, many venture capitalist at that time had more traditional nance and business backgrounds. Oen they had previously worked at leading investment banks, management consultancies or in senior managerial positions at larger technology companies, rather than having previously founded a startup themselves. For example, using the WayBackMachine, we can look at the biographies of leading venture funds Accel Partner and Sequoia Capital in 2000. Accel Partners listed ten GPs, thereof three GPs37 had a professional background in management consultancy, two GPs38 in investment banking, three GPs39 in the large tech incumbents of that time (i.e. Intel, Xerox, Sun Microsystems and Apple), with only two GPs40 having founded and exited a startup prior to transitioning to venture capital.41 Similarly, venture rms that were newly established during that time were oen started by general partners (GPs) with traditional nancial services careers. Again, using the WayBackMachine, we can see that the now leading venture rm Benchmark, established in 1995, was founded to a large part by former bankers,42 lawyers,43 executive recruiters44 and management consultants,45 with only one founder-turned-VC46 among the founding GPs (Benchmark Capital, 2000). 31See the above example of the startup GovWorks portrayed in the Startup.com documentary Jehane Noujaim and Chris Hegedus (Directors)(2001) and Knight(2012) (‘As the lm portrays it, the business quickly runs into a variety of real-life, day-to-day problems that many new companies face. e website is buggy; governments are slow to sign up as paying clients’). 32A notable example of this is the startup Pets.com, which scaled a loss making operation and became an emblematic startup failure of that era. See Nicholas(2019) (‘But with negative margins, Pets.com only lost money as it grew. At its pinnacle in January 2000, Pets.com bought advertising time during the Super Bowl at a cost of $1.2 million’.). 33See Rosenstein(1988) (‘high-technology rms funded by venture capital organizations are characterized by a board of directors that has high power relative to management.’). 34See Camp(2002) (‘Venture capitalists are famous for requiring companies to agree to bring in what they call “adult supervision” – seasoned managerial talent – before they will invest. is usually involves replacing founding CEOs with CEOs who have more experience.’); Hannan, Burton, and Baron(1996) (Analyzing 100 startups and nding that in the rst 20 months of the startups’ life, the likelihood that a non-founder is appointed as CEO is around 10%; increasing to 40% aer 40 months and over 80% aer 80 months.); Hellmann(1998) (Developing a model of why, and under what circumstances, entrepreneurs would voluntarily relinquish control and noting ‘e transition from founder CEOs to professional management received considerable aention in some of the most successful startup companies, such as Apple Computers, Cisco Systems, and Silicon Graphics. e replacement of a founder CEO is a widespread phenomenon.’). 35See R. J. Gilson(2003) (‘While rst round investors expect to participate in subsequent investment rounds, oen they are not contractually obligated to do so even if the business plan’s milestones are met; the terms of later rounds of investment are negotiated at the time the milestones are met and the prior investment exhausted.’). 36See Bob(1998) (outlining the common Series A terms at the time ‘In a typical startup deal, for example, the venture capital fund will invest $3 million in exchange for a 40% preferred-equity ownership position’). In contrast, dilution at the Series A has tended to be much lower in recent years. See Holiday(2015) (‘Generally, the valuation range results in the group of Series A investors taking 15-25 percent of the company.’). However, this cannot be generalized, as founder dilution through VCs varies greatly depending on the traction, negotiation power and funding path of individual startups, with salient examples of both low and high dilution levels found both during the Dotcom era and in the following decades. See, for example, Abdullah(2018) (analyzing VC ownership levels of 105 startups at IPO between 1980 and 2018 and providing a wide range of dilution levels for Dotcom era IPOs (on the low end e.g. eBay (14% VC-owned at IPO in 1999) and Amazon (16% VC-owned at IPO in 1999), on the high end e.g. Netix (59% VC-owned at IPO in 2000) and Yahoo (51% VC-owned at the IPO in 1996)) and IPOs in the recent decade (on the low end e.g. Facebook (17% VC-owned at IPO in 2011) and Snap (18% VC-owned at IPO in 2016), on the high end e.g. Wix (78% VC-owned at the IPO in 2013) and Sendgrid (73% VC-owned at the IPO in 2017) with the average VC ownership at IPO being 50% over the entire sample). 37Peter Wagner (McKinsey), Jim Breyer (McKinsey), eresia Gouw Ranze (Bain). 38Arthur Paerson (Citicorp), Jim Schwartz (Citicorp). 39Jim Flach (previously a VP at Intel and before that for 17 years at Xerox), Bruce Golden (previously for 8 years at Sun Microsystems), Joe Schoendorf (previously a VP of Marketing at Apple). 40Bud Colligan (co-founder of Macromedia) and Mitch Kapor (founder of Lotus Notes). 41See Accel Partners(2000). 42Bruce Dunlevie (Goldman Sachs) Andy Rachle (Paine Webber), Bill Gurley (Credit Suisse). 43Operating Partner Steve Spurlock (previously a corporate lawyer at Gunderson Demer). 44Dave Beirne (previously executive recruiter at Ramsey/Beirne Associates). 45Bob Kagle (BCG). 46Kevin Harvey (having founded Styleware and Approach Soware).

83 2.3.2 Modern startup nancing landscape

Aer the Dotcom bubble burst, the startup and venture capital environment has changed signicantly across the dimensions highlighted above. Venture capital now is an established and highly institutionalized asset class, with many funds managing multiple billions in assets under management (AuM).47 With respect to the startup ecosystem, a lot of the ‘plumbing of the internet’ is now in place, allowing entrepreneurs to build, ship and iterate on soware products in increasingly quick cycles. To highlight a few salient features of the Dotcom era, one can point to:

• Long road to IPO and multiple funding rounds: With a median age of 11 years at IPO, the time from founding a startup to going public is now more than double of what it used to be during the Dotcom boom (Rier, 2019). At the same time, startups have steadily raised more and more private funding rounds from venture capital rms at the early stages and from growth capital and private equity rms at the later stages. e ride-sharing startup Uber, for example, went public in 2019 aer a total of 23 funding rounds through which it raised c. $23bn from venture funds and other institutional investors (most notably Sobank).

• Rise of private acquisitions: At the same time, an increasing number of startups have exited by way of acquisitions to a larger technology company.48 is includes so-called ‘aqui-hires’, whereby the startup is acquired primarily for the talent, rather than the product or underlying technology.

• Rise of FPOs: In addition to fewer IPOs and the rise of acquisitions, another common way for founders to exit their companies in the recent decade has been by way of secondary transactions in the course of late stage nancing rounds, so-called ‘nal private oerings’ (FPOs).49 A notable example of such a FPO is the $700m secondary transaction of the founder ahead of the (planned) WeWork IPO.50

• Lower funding requirements: Coyle and Green(2014) report that around the year 2005, a number of technological developments have led to a dramatic decline in the costs of launching a technology startup. In particular, this includes the rise of a robust cloud computing infrastructure and third-party cloud providers, most notably Amazon’s Amazon Web Services (AWS). Mark Suster, founding general partner at LA-based venture rm Upfront Ventures, estimated in a 2012 blogpost51 that, on the back of open-source movements52 and advances in cloud computing53, the costs of starting an internet startup had fallen from $5m to $5k in the decade between 2000 and

47See NVCA(2019) and NVCA(2011) (reporting total cumulative assets under management of the U.S. venture industry of $689bn compared to cumulative assets of $30.8bn in 1988 and of $127.8bn in 1998). 48See Amor and Kooli(2020) (‘Although an IPO is an aractive exit mechanism to realize returns, VC rms also have the alternative of cashing out via acquisition, a merger, or a trade sale. e appeal of MAs has become popular over the past decade. According to the National Venture Capital Association (NVCA) 2016 Yearbook, there were more exits by VCs through acquisitions than via IPOs for the period between 1996 and 2015.’); Bayar and Chemmanur(2012) (‘However, an equally important but less studied exit option for private rms is an acquisition by another (usually larger) rm. e ratio of acquisitions to IPOs among private rm exits has increased dramatically in recent years. Over the last decade, a private rm was much more likely to have been acquired than to go public.’). 49See Massa and Barr(2019) (‘It used to be that technology company initial public oerings (IPOs), were as exciting and potentially enriching an event as the markets had to oer, as investors fought to get in on the ground oor of a hot startup. Now there’s a dierent ground oor: the nal private oering, or what some in technology circles are calling an FPO.[…] In addition to gaining the money companies need to fuel their growth, these private deals can also let insiders and other early investors sell some of their existing shares.’); Massa, Barr, and McBride(2019) (‘Promoters of private investing say the new IPO is the FPO - the nal private oering. Even big mutual fund companies including Blackrock, Fidelity, T. Row Price, and Wellington have embraced private investments. So far this year, 27% of VC rounds that raised more than $100 million had at least one such “crossover” investor normally focused on public markets.’). 50See Eliot and Anupreeta(2019) (‘WeWork co-founder Adam Neumann has cashed out more than $700 million from the company ahead of its initial public oering through a mix of stock sales and debt, people familiar with the maer said—an unusually large sum given that startup founders typically wait for the IPO to monetize their holdings.’); Pla, Kruppa, and Fontanella-Khan(2019) (‘WeWork founder Adam Neumann has sold shares and taken out loans against his equity stake in the multibillion-dollar property company, raising at least $700m for himself in recent years as the group moved towards an initial public oering, according to people briefed on the maer.’). 51See Suster(2012) (‘Cloud computing and the open source movements have brought down the costs of starting a company by more than 90%.’). 52See Suster(2011) (‘e rst major change in our industry was imperceptible to us as an industry. It was driven by the introduction of open-source soware, most notably what was called the LAMP stack. Linux (instead of UNIX), Apache (web server soware), MySQL (instead of Oracle) and PHP.’). 53See Suster(2011) (In particular making reference to the entry of Amazon’s AWS ‘e biggest change in the soware industry beyond open-source was “open cloud. […] Enter Amazon. ey came from a dierent perspective. ey have the mass retailer mentality of “stack ’em high and sell ’em cheap.” ey started by oering cloud storage (S3) on a super cheap, pay-as-you consume basis. Every startup I knew in 2005 (when I started my second company) was using this. Why would we commit hundreds of thousands to EMC before we knew whether we had a big business? ey then launched processing capabilities (EC2) and startups suddenly didn’t need to buy production servers. en they launched a simple database management tools and so on.’).

84 2011.54 With the entry of new cloud providers, such as Google’s ‘GCP’ and Microso’s ‘Azure’ cloud oering, as well as a burgeoning open-source ecosystem, startup costs have further decreased over the past decade. is has enabled many startups since then to self-nance or ‘bootstrap’ their startup for a considerable time55 before raising their rst round of venture funding and sometimes allowing them to even forego56 venture funding altogether.

• Product focus and traction: e lowered entry barriers have, on the other hand, raised the bar for entrepreneurs seeking venture funding. With the declining costs of starting a soware company, startups are also expected to show more traction and ideally so-called ‘product-market t’ and positive ‘unit economics’ before approaching institutional capital providers. With the ‘lean startup methodology’, pioneered by Ries(2011), this experimentation practice of building a minimum viable product (MVP) and iterating on it before raising venture capital funding has now been well-documented and studied in the startup ecosystem.

• Emergence of ‘founder-friendly’ funds: However, for those entrepreneurs who successfully meet the criteria of VC funds, the pendulum of power has now swung towards them. While founders were oen tightly controlled by venture rms in the 90ies, the top entrepreneurs can now oen command favourable terms, both nancially and in terms of governance, ranging from dual-class share structures57 to uncapped convertible notes.

• GPs with operational/founder backgrounds: In line with the picture painted above, many of the leading venture capital GPs now have an operational background as startup founders or early employees of breakout startups. is reects the maturation of the technology industry, as in the 1990ies, there was still a scarcity of entrepreneurs who had successfully built, scaled and exited technology companies. Venture capitalist thus oen came from nance and investment banking backgrounds.

• Rise of the ‘mega funds’: In chapter 3 of this PhD thesis, aention is drawn to the rise of so-called ‘mega funds’ over the past decades, referring to venture rms raising billion dollar growth capital funds.58 ese are oen venerable venture rms with decade-long track records, which give them an advantage in fundraising larger venture funds from deep-pocketed institutional capital providers, many of which have formed their existing LP base for their agship early stage funds.

• Emergence of ‘founder-funder funds’: A further phenomenon outlined in chapter 3 of this PhD thesis is the rise of the ‘founder-funder class’. is refers to a cohort of former founders and operators, who have transitioned to become prolic angel investors or have started their own venture rms. Oen times, these venture rms are founder-friendly early stage venture rms.

2.3.3 Dominance of the venture rm allocation and ‘death of the IPO’

As a result of the outlined changes in the general funding environment, the dominant allocation for technology startups is now through private capital providers (venture capital rms at the earlier stages, growth capital and crossover funds at the later stages), rather than through the public markets. ere are a number of theories or perspectives that can help in explaining this phenomenon.

54See Nicholas(2019) (In the same vein, comparing the costs of seing up PetFlow.com, a new version of Pets.com a decade later ‘it cost about $7 million to $10 million to set up the infrastructure for Pets.com, excluding inventory, PetFlow.com was established with only around $50,000. at precipitous drop in cost owed much to the diusion of the enabling technologyies developed by previous high levels of investments in internet startups. Innovations like Amazon Web Services, introduced in 2006, enable the outsourcing of of functions that Pets.com had to build in-house.’.). 55In 2017, for example, the spell-checking startup Grammarly, raised its rst round of outside venture capital, a $110m Series A, aer having existed for almost a decade before that Roof(2017a). 56e internet community and podcasting series Indiehackers.com, for example, chronicles the journey of hundreds of startups, mostly soware and SaaS companies, scaling to oen times millions of dollars in annual revenue without venture funding (as so-called ‘indiehackers’). 57Bebchuk and Kastiel(2017)(‘e desirability of a dual-class structure, which enables founders of public companies to retain a lock on control while holding a minority of the company’s equity capital, has long been the subject of a heated debate. […] Furthermore, there has been an upward trend in the adoption of dual- class stock since Google went public with a dual-class structure in 2004 and was followed by well-known tech companies, such as Facebook, Groupon, LinkedIn, Snap, Trip Advisor, and Zynga.’). 58See NVCA(2019) (reporting on ten funds that have raised billion dollar venture funds in 2018, including Sequoia Capital ( $8bn), Tiger Global Management ($3.75bn), Bessemer Venture Partners ($1.85bn), Norwest Venture Partners ($1.5bn), General Catalyst ($1.375bn), GGV Capital ($1.36bn), Newview Capital ($1.35bn), Lightspeed Venture Partners ($1.05bn), rive Capital ($1bn), Index Ventures ($1bn)).

85 Specialized fund perspective e empirical data presented in chapter 3 of this PhD thesis relating to ‘mega funds’ and ‘founder-funder funds’ demonstrates how the venture rm has increasingly specialized to beer serve startups across dierent stages of their lifecycle. In particular, the rise of ‘mega funds’, which can serve startups over multiple rounds with capital that would previously only be available through the public markets, appears to have been a major contributing factor to the startups’ ability to remain private for longer stretches.59 Acquisition perspective Shortly aer the JOBS Act was passed, as part of the Advisory Commiee on Small and Emerging Companies, a group of academics presented an alternative hypothesis on the decline of IPOs to the SEC.60 is argument, later published by X. Gao, Rier, and Zhu(2013), held that the decrease in IPO activity was due to ‘increased economies of scope’ and the ‘increased importance of speed to market’. e presented results indicate that startup protability has been declining for decades and that smaller companies ‘lack the resources to quickly take advantage of new technology’. us, larger tech companies can serve protable markets more quickly and eciently, oen under-pricing smaller startups. As a result, the argument holds that startups are frequently forced to bypass public market nancing and instead merge into bigger companies that can provide synergies and economies of scale. In the same vein, Bayar and Chemmanur(2012) nd that emerging rms operating in less concentrated industries are less likely to be acquired.61 Masulis and Nahata (2011) point out that acquisitions provide an eective alternative during times when the IPO window is closed.62 Securities regulation perspective Within the scope of this chapter, securities regulation is analyzed as a main contributing factor that determines the allocation through the venture rm or the market, respectively.63 However, in light of the alternative perspectives presented above, it should be understood that securities regulation is but one factor that contributes to the recent rise of the rm allocation.

2.4 Coase eorem of Securities Regulation

e Coase eorem of Securities Regulation (hereinaer the ‘eorem’) is a novel law and economics theory of securities regulation. Based on the foundational work of Ronald Coase and Guido Calabresi, it develops a novel perspective on securities regulation, which allows us to have a beer understanding of the role of security laws on the formation of rms and markets. e theory breaks securities regulation into three functional layers: disclosure, liquidity and diversication. Under the rst part of the theory, it analyses how security laws can lead to a dierential pricing between the market and the rm allocation. Under the second part, it re-conceptualizes the costs of the market, in particular securities regulation, as an externality and considers the optimal assignment of these costs at each functional layer.

2.4.1 e three functional layers

e eorem introduces the concept of the ‘three functional layers of securities regulation’, which are the main units of the regulatory analysis conducted. ese three layers follow a hierarchical and sequential logic. At the base layer, which is referred to as the disclosure and information layer, the issuer of a security, either required by securities regulation or voluntarily, discloses information to investors. Once the security has been issued, it is placed in the public market by

59See Waters and Foley(2015) (‘It’s a lile bit of a self-fullling prophecy: the only way to be part of the meteoric growth part of the curve is to be in the private market,’ says Dylan Smith, co-founder of Box, an online storage company that rode the private market for 10 years before going public this year.’). 60See X. Gao, Rier, and Zhu(2012) (‘e protability of small independent rms has declined relative to the value created as part of a larger organization that can quickly implement new technology and benet from economies of scope’). 61See Bayar and Chemmanur(2012) (‘us, regression results using alternative concentration-based competition measures also support the predic- tion of H2 that IPOs tend to be in less concentrated industries where product market competition is not dominated by“big player”public rms.’). 62See Masulis and Nahata(2011) (‘[...], while IPOs are generally viewed as the most protable VC exit, acquisitions can also be very protable, and can be the only protable exits in periods when the IPO market is weak or eectively closed.’). 63See Bayar and Chemmanur(2011) (‘ese trends indicate that the costs to private rms of going public rather being acquired have risen signif- icantly in recent years, a trend blamed by investment bankers and other practitioners on the recent spate of scandals involving analysts, which has reduced the number of analysts and therefore the post-IPO coverage of small rms, and the Sarbanes-Oxley Act of 2002, which, they argue, has increased the cost of complying with disclosure and governance regulations aer an IPO.’).

86 an underwriter and actively traded on a securities exchange. is set of activities and functions is referred to as the investment and liquidity layer. Lastly, as investors assume idiosyncratic risk by investing into the security of a single issuer, modern portfolio theory dictates that they should diversify across multiple issuers. is diversication at of the buy-side is referred to as the diversication layer.

2.4.1.1 Disclosure and information layer

Economically speaking, a security always involves a ow of funds from:

• a surplus agent (investor, with net assets); to

• a decit agent (securities issuer, with net liabilities).

Within the scope of the startup equity seing of this chapter, surplus agents refer to individuals or institutional investors. On the other hand, decit agents refer to startup rms. e eorem denes a disclosure as a one-sided, unilateral ow of information from the decit agent to the surplus agent. In the securities market seing, the disclosure layer refers to disclosures mandated by the SEC and produced by the issuer. In a venture rm seing, the disclosure layer governs the disclosures made between startups and the venture rm and between the venture rm and its investors (LPs). However, the disclosure and information layer is not constrained to disclosures, but is dened more broadly, going beyond the traditional focus of mandatory disclosures. In particular, it covers the entire information and data ow between (i) surplus agents, (ii) decit agents and (iii) third-party data providers.

2.4.1.2 Investment and liquidity layer

Whereas the disclosure and information layer governs the ow of information, the investment and liquidity layer governs the ow of funds between the two. e investment and liquidity layer can be further broken down into two separate sub-functions:

• primary market activities; and

• secondary market activities.

Primary market activities relate to the initial ow of funds from surplus agents to decit agents. With respect to startups, this is typically associated with the underwriting of a startup’s equity. Secondary market activities relate to the secondary ow of funds between surplus agents, which are typically facilitated by nancial exchanges and market makers. Under the venture rm allocation, ‘primary activities’ comprise the initial investment in a startup within the venture rm structure, while ‘secondary activities’ consist of holding startup equity in the VC fund structure until an exit is realized.

2.4.1.3 Diversication layer

Whereas both the disclosure and the liquidity layer look at the individual startup issuer, the diversication layer is concerned with the pooling of economic exposure across multiple issuers. e essence of diversication is to shi economic exposure away from a single issuer towards multiple issuers. In equity markets, diversication traditionally takes place through pooling vehicles, such as ETFs or mutual funds, which aggregate funds from investors and depositors and spread the economic exposure over multiple companies.

2.4.2 First part of the eorem

In his seminal 1937 paper ‘e Nature of the Firm’, Coase asks the question: why do rms exist? Similarly, the rst part of the eorem asks, why do we see certain economic transactions allocated through the rm and others through the markets?

87 Coase oers a number of reasons why rms may come into existence. e main argument put forward is that the parties encounter dierent costs whether they are operating through the rm or the market. In chapter 1 of this thesis, the distinction between baseline and regulatory costs under Coase is made. Under the rst part of the eorem, the focus lies on regulatory costs, in particular the costs of securities regulation and alternative rm-specic regimes. Within the scope of this startup-focused chapter, the alternative rm-specic regulation is through exemptions from security laws. Coase describes what is here referred to as ‘regulatory costs’ as follows:64

Within the rst part of the eorem, the chapter introduces a novel perspective on securities regulation which makes these laws an integral part of explaining why certain transactions are allocated through the rm or the market. us, it views the rm and the market as being governed by competing legal regimes, which impose dierent prices on economic transactions. In this regard, securities regulation determines the price of the market, while rm-specic regulation (in this chapter exemptions under Reg D from security laws) determine the regulatory costs of the alternative (venture) rm seing. Based, in part, on these regulatory prices, economic actors choose to allocate through the market or the rm. us, the observed allocation mechanism can be regarded as a representation of rational preferences of economic actors, given the existing legal seing.

2.4.3 Second part of the eorem

Under the rst part of the eorem, the costs of securities regulation and venture capital regulation are considered endogenous transaction costs. is is an adequate level of analysis, as the main focus there lies on comparing the costs of market regulation to the costs of rm-specic regulation (Reg D exemptions from securities regulation in this chapter). us, it does not require a level of granularity beyond that of the aggregate costs that are compared to the alternative regulatory regimes. is is dierent for the second part of the eorem established in chapter 1 of the thesis, which hones in on the costs of the market allocation. e core contribution of the second part is that it re-conceptualizes the costs of the market, including security laws, as externalities. By regarding these costs as externalities, the eorem allows us to analyze them under the traditional Coasean seing of ‘e Problem of Social Costs’.65 us, at its core, the second layer of the eorem is an application of the classical Coasean seing to the realm of securities regulation. In a theoretical, friction-less seing, we can consider the classical ‘Coasean’ frictionless environment where costs can be allocated in a highly ecient manner among the parties aected by the externality. To recount, the original Coase eorem can be split into two main propositions:

• the eciency proposition; and

• the invariance proposition.

e eciency proposition states that in the absence of transaction costs, parties can overcome ineciencies otherwise caused by externalities. In other words, in a frictionless seing, the costs involved with the negative externality, such as the regulatory costs of transacting through the market can be optimally allocated between the parties involved. us, in the context of securities regulation, the issuer and the investor can engage in Coasean bargaining and optimally allocate the regulatory costs. e invariance proposition goes one step further and states that in a frictionless seing, the initial assignment of legal entitlements or obligations does not aect the eciency of the nal allocation of resources. In the context of securities regulation this means that in a world without transaction costs, the assignment of rights and obligations by securities regulation to either the issuer or the investor does not aect the eciency of the nal allocation.

64See Coase(1937). 65See Coase(1960).

88 2.5 First part of the eorem

As outlined above, the rst part of the eorem views the rm and the market as being governed by competing legal regimes, which impose dierent prices on economic transactions. In this regard, securities regulation determines the price of the market, while exemptions to securities regulation determine the price of the venture rm allocation. Based, in part, on these empirical regulatory ‘prices’, economic actors choose to allocate through the rm or the market. us, the observed allocation mechanism can be regarded a representation of rational preferences of economic actors, given the existing legal regime.

2.5.1 Disclosure and information layer

e disclosure and information layer is concerned with the ow of information between startups and investors. When startups are nanced through the public markets, information ows directly between the startup and investors through the mandatory disclosure regime of existing security laws. is makes intuitive sense, as investors enter into a direct contractual investor relationship with the startup, which exposes them to the risks and returns of the startup in full. On the other hand, where startups are nanced through the venture capital structure, the ow of information is compartmentalized between (i) the startup and the venture rm and (ii) the venture rm and its investors. Under the rst layer of the eorem, the chapter compares the dierent disclosure and information costs incurred by startups when raising money through the public markets under U.S. security laws with the costs of raising funding from venture capital rms under the existing Reg D exemptions.

2.5.1.1 Costs of the market

If shares are oered through the public markets, the qualication of startup equity as ‘securities’ under existing U.S. security laws can trigger substantial mandatory disclosure obligations, both initially and on an ongoing basis. In particular, where late stage startups complete a traditional initial public oering (IPO) under the Securities Act 1933, a Form S-1 ling is required initially and Forms 10-K, 10-Q and 8-K need to be led on an ongoing basis thereaer. Where early stage startups complete a novel oering under Reg CF or Reg A+ under the JOBS Act, they are required to le Forms C and 1-A initially and Forms C-AR and 1-K/1-SA on an ongoing basis. To comply with such regulatory requirements, startups need to engage the services of sophisticated market-enabling rms, namely corporate law and accounting rms. is results in both direct compliance costs and in indirect costs by diverting the founder’s aention and potentially disclosing valuable nancial information to competitors in nascent business segments. As a result, disclosure obligations can deter startups at all stages from accessing the public markets.

2.5.1.1.1 Traditional initial public oering (IPO)

From the moment that technology startups enter the public markets, they are subjected to a rigid mandatory disclosure regime set forth by security laws and the SEC. Compliance with these disclosure obligations requires substantial nancial resources and is likely to divert management’s aention. e going public process typically begins with the startup registering an S-1 form66 with the SEC. On the face of it, this is an 8-page document, outlining the dierent items that need to be included in the public ling. However, the S-1 is in many ways just the ‘tip of the iceberg’. Many of the disclosure costs arise from very detailed statutory and non-statutory disclosure requirements and guidelines set out by the SEC:

• Regulation S-X:67 sets out the principal accounting regulation, specifying the form and content of the nancial statements to be included in SEC lings.

• Regulation S-K:68 sets forth the SEC’s disclosure requirements of the non-nancial statement portion of lings. 66SEC, Form S-1, hps://www.sec.gov/les/forms-1.pdf 6717 C.F.R. § 210. 6817 C.F.R. § 229.

89 • Financial Reporting Manual (‘FRM’):69 this manual contains interpretations by the sta of the Division of Corporation Finance regarding a wide range of detailed nancial reporting maers.

• Financial Reporting Releases (‘FRR’):70 communicates the SEC’s position on accounting and auditing principles and practices.

• Sta Accounting Bulletins (‘SABs’):71 these are a reection of the SEC’s views and interpretations regarding accounting-related disclosure practices.

• Compliance and Disclosure Interpretations (‘C&D’):72 these comprise the SEC’s interpretations of the rules and regulations on the use of non-GAAP nancial measures and aim to guide issuers when they are reporting on nancial gures that fall outside of the U.S. Generally Accepted Accounting Principles (GAAP).

• Industry guides:73 these ‘industry guides’ published by the SEC are intended to assist issuers, by providing in- struction sets on disclosure compliance for particular industries, including oil and gas, mining, banking, insurance and real estate.

• Regulation S-T:74 governs the preparation and submission of documents led through the SEC’s EDGAR system.

To comply with these many obligations and detailed guidances, startups oen need to engage an entire ‘going public team’ for their initial public oering (IPO), consisting of expensive eets of securities lawyers, public accounting rms and public relations specialists. In the post-Dotcom era, additional disclosure costs have been introduced through the Sarbanes-Oxley Act75, which has added another set of mandatory risk, accounting and governance disclosure obligations for startups seeking to enter the public markets. However, these initial disclosures are really just the rst gate crossed in a series of ongoing disclosure obligations. Aer the startup has transitioned from the private rm allocation to the public market, it needs to le multiple forms on a periodical basis, including forms 10-K,76 10-Q77 and 8-K.78 In the aermath of the Dotcom era, these ongoing disclosure obligations have become substantially costlier and burdensome. In particular, Regulation Fair Disclosure,79 which was promulgated by the SEC in August 2000, has made it impossible for startups to selectively disclose information to investors. In contrast to the venture rm allocation, where the startup founder can informally update its large investors, every investor update in the public market needs to be led with the SEC and passed through expensive information intermediaries. The tech industries’ struggles with the SEC’s disclosure obligations Over the past decade, the tech industry has openly struggled with the stringent disclosure obligations of the public markets. Before going public, many founders of late stage unicorn startups have voiced their concerns over the public markets. While some have been rather vague in this regard, such as Uber’s co-founder Travis Klanick,80 others have specically addressed their concerns with the mandatory disclosure obligations associated with an IPO. Alex Karp, the co-founder of data analytics unicorn Palantir, for example, has reported his fears that the company could loose its competitive edge through an IPO and that employees could loose their focus.81 In a similar vein, Jay Yarow, the co-founder of

69SEC, Financial Reporting Manual, hps://www.sec.gov/corpn/cf-manual 70SEC, Financial Reporting Information Center: Regulatory Pronouncements, hps://www.sec.gov/divisions/enforce/friregulatory.shtml 71SEC, Selected Sta Accounting Bulletins, hps://www.sec.gov/interps/account.shtml 72SEC, Non-GAAP Financial Measures, hps://www.sec.gov/divisions/corpn/guidance/nongaapinterp.htm 73SEC, Industry Guides, hps://www.sec.gov/about/forms/industryguides.html. 7417 C.F.R. § 232. 7515 U.S.C. § 7201 et seq. 76SEC, Form 10-K, hps://www.sec.gov/les/form10-k.pdf 77SEC, Form 10-Q, hps://www.sec.gov/les/form10-q.pdf 78SEC, Form 8-K, hps://www.sec.gov/les/form8-k.pdf 7917 C.F.R. § 243. 80See D’Onfro(2015) (‘It took Facebook a long time to go public, but once they did, Zuck has become a huge proponent–is it misery enjoys company?’). 81ompson(2014) (‘Another reason the company has trumped competitors is because it refuses to go public. Karp said that once companies go public they begin to lose their competitive edge and can’t stay ahead of the game. “e minute companies go public, they are less competitive. … You need a lot of creative, wacky people that maybe Wall Street won’t understand. ey might say the wrong thing all the way through an interview,” Karp said. “You really want your people to be focused on solving the problem not on cashing in.”’).

90 SurveyMonkey, which eventually went public in 2018 aer 19 years as a private company, has long pointed to competitive concerns and quarterly reporting obligations as a chief reason for staying private.82 ese competitive reservations by startup founders have been widely recognized in the academic literature, which has described information spillover eects as a potential source of freeriding by competitors83 and as a main reason for information underproduction in the absence of mandatory disclosure obligations.84 Arguably the most vocal critique, however, has come from Marc Andreessen, the co-founder of Netscape and general partner at venture capital rm Andreessen Horowitz (a16z):

‘You also had a relatively benign regulatory environment, pre-Sarbanes-Oxley. [..] Basically, that all started to change aer 2000. A whole set of “closing the barn door aer the horse had run out” kind of things happened. Sarbanes-Oxley happened. e irony of Sarbanes-Oxley was that it was intended to prevent more Enrons and Worldcoms but it ended up being a gigantic tax on small companies. e compliance and reporting requirements are extremely burdensome for a small company. It requires eets of lawyers and accountants who come in and do years of work. It’s this idea that if you control everything down to the n-th detail, nothing will go wrong. It’s this bizarre, bureaucratic, top-down mentality that if only we could make everything predictable, then everything would be magic, everything would be wonderful. It has the opposite eect. It’s biased enormously toward companies that are big enough to hire eets of lawyers and accountants, biased against companies that are very young and for whom there’s still a lot of variability. […] When there’s a problem, the answer is presumed to be more regulation — even when the regulation was the problem in the rst place.’85

e critique of Marc Andreessen reects the overproduction aw86 of mandatory disclosure obligations, which has oen been pointed to in the securities regulation literature, as well as the scholarly critique of the anti-competitive eects87 that may result from this overproduction aw. Furthermore, many technology startups that have gone public have recently struggled with complying with both initial and ongoing disclosure obligations. For example, aer the high-prole IPO of social networking startup Snap in 2017, which operates the ‘Snapchat’ app, both the SEC and the DoJ were investigating the company for potentially misleading investors .88 Another instance of a tech company’s struggles with the public market has been the infamous tweet of Tesla founder Elon Musk, who tweeted that he would take Tesla private and that ‘funding was secured’ (although

82Yarow(2013) (‘In general you don’t want competitors to understand your business, outside of telling people your revenue and protability numbers. Part of the reason we’re not going public is that we don’t want to measure our results on a quarterly basis. Part if that is you don’t want to release numbers on a quarterly basis and then having people say, ”ey grew 30% this quarter so they should grow 30% next quarter” and those kind of things. If you’re private, you’d rather just keep all that information for yourself. ere’s not a whole lot of advantage for a company to be public.’). 83de Fontenay(2017) (‘Private companies today can raise large amounts of capital while disclosing less than their public company counterparts in part by freeriding on the enormous volume of public side information, which makes private company valuation vastly easier and more accurate. e cloud storage company Dropbox, which remains a private company despite a ten billion dollar valuation, surely benets to some degree from the nancial and material contract disclosures of its public company competitor, Box.’); Palmiter(1999) (‘[P]ublic disclosure, ostensibly meant for investors,can harm the issuer’s business when used by competitors, particularly privately-held competitors that do not make reciprocal public disclosures’). 84A. Schwartz(2019) (‘e idea is that le to their own devices, companies might not provide as much disclosure as diversied investors would like, due to collective-action problems. For example, a company might rationally decide not to disclose a certain piece of information, even if investors would want to know it because doing so would aid a competitor.’). 85See T. Lee(2014). 86See Haeberle(2018) (outlining the overproduction problem ‘Even if one believes, as we do, that there are instances when compelling disclosure has value for investors or society as a whole, there are several reasons to be concerned that government mandates will call for companies to disclose information when the social benets of the disclosure are outweighed by its production costs.’); Ben-Shahar and Schneider(2014) (detailing the overproduction problem more generally across multiple domains of the laws). 87Easterbrook and Fischel(1984) (‘Existing rules give larger issuers an edge, because many of the costs of disclosure are the same regardless of the size of the rm or the oering. us larger or older rms face lower otation costs per dollar than do smaller issuers.’); J. Schwartz(2012) (‘Moreover, while an increased regulatory burden falls on all rms, the costs tend to be felt most acutely by emerging ones. ese rms tend to be smaller, making the costs loom proportionally larger, and they have not been around long enough to routinize the process.’); Afshar and Rose(2007) (‘Sarbanes-Oxley critics have also pointed to the fact that compliance costs for small public companies appear to be disproportionately aected by the Act. […] is is because the costs of complying with securities laws and Sarbanes-Oxley have an element of xed cost that does not vary proportionally with rm size.’); Ben-Shahar and Schneider(2011) (‘Second, mandated disclosure can have anticompetitive eects. Disclosure costs are substantially “xed costs”; many of them do not vary with the scope of activity or with the frequency of disclosures. ese xed costs – collecting information, draing forms, training employees – are roughly the same for large and small disclosers. is gives larger disclosers an advantage: their burden of disclosure per ”unit” is smaller. is, in turn, hurts small companies trying to enter and compete in the market.’). 88See H. Murphy(2019) (‘In the ling, Snap said it had responded to “subpoenas and requests for information” from the Department of Justice and the Securities and Exchanges Commission, who it believed were “investigating issues related to allegations asserted in our federal securities class action about our IPO disclosures”.’).

91 he did not actually plan on doing so and funding was not secured), which eventually led to a $20m personal ne by the SEC.89 While the above examples may be considered anecdotal, they do reect a general industry sentiment and coincides with the secular trend of startups staying private longer.90

2.5.1.1.2 JOBS Act oerings

In 2012, the Jumpstart Our Business Startups (JOBS) Act91 was explicitly introduced to encourage capital formation for small businesses, in particular technology startups, by reducing disclosure costs under existing security laws. In particular, three alternative disclosure regimes were introduced by the JOBS Act:

• Emerging Growth Company (EGC) status

• Regulation A+ oerings

• Regulation CF oerings

As will be outlined below, these are scaled down versions of the fully-edged SEC disclosure regime. us, they have not materially changed the existing logic of the SEC’s mandatory disclosure requirements, but have rather reduced the scope. In this sense, these reforms can be understood as ‘less of the same’ measures.

Emerging Growth Company (EGC) Title I of the JOBS Act,92 also known as the ‘IPO on-ramp’, was designed to make the traditional IPO process more aractive to startups, which qualify as so-called ‘emerging growth companies’ (EGC). To qualify as an EGC,93 a startup must have total gross revenues of less than $1bn during its most recently completed scal year and must not have issued more than $1bn in non-convertible debt over the past three years. e benets of qualifying as an EGC include the following reductions in mandatory disclosure requirements:

• Historic nancial statements: EGCs are allowed to include only two (instead of three) years of audited nancial statements in their initial public oering (IPO) registration statements;94

• Executive compensation disclosure requirements: EGCs can rely on the reduced executive compensation disclosure requirements for ‘smaller reporting companies’ under item 402 of Regulation S-K;95

• Deferred compliance with accounting standards: EGCs can defer compliance with any new or revised nancial accounting standards, as long as they do not qualify as ‘issuers’ under Section 2(a) of the Sarbanes-Oxley Act; and

• Exemption from Sarbanes-Oxley: EGCs are exempt from the Sarbanes-Oxley Act Section 404(b) auditor aes- tation96 on management’s assessment of its internal controls.

Summary of ECG status provisions Dambra, Field, and Gustafson(2015) have estimated that the reduced disclosure requirements for emerging growth

89Complaint, SEC v. Elon Musk, (S.D.N.Y. 2018) (No. 1:18-cv-8865). 90See (Rier, 2019) (detailing a rise of the a median age at IPO from 5 years to 11 years since the Dotcom bubble). 91Jumpstart Our Business Startups (JOBS) Act, Pub. L. No. 112-106, §§301–05, 126 Stat. 306, 315–23 (2012) (codied in scaered sections of 15 U.S.C.). 92Public Law 112–106, 126 Stat. 306 (2012). 93Section101(a) of the JOBS Act amended Section 2(a) of the Securities Act [15 U.S.C.77b(a)] and Section3(a) of the Exchange Act [15 U.S.C. 78c(a)]. 94Rule 3-02 of Regulation S-X otherwise requires the ling of three years of audited statements of income and cash ows statements. 95A ‘smaller reporting company’ is dened in Rule 405 under the Securities Act[17 C.F.R. 230.405], Rule12b-2of the Exchange Act [17 C.F.R. 240.12b- 2], and item 10(f)(1) of Regulation S-K [17 C.F.R. 229.10(f)(1)] to mean an issuer that had a public oat of less than $75m. 9615 U.S.C. 7262(b).

92 companies (EGCs) under the JOBS Act have led to 21 additional IPOs annually, a 25% increase over pre-JOBS levels.97 However, it should be noted that even under the reduced disclosure requirements, EGCs still face substantial disclosure costs in aggregate.

Reg A+ oerings Reg A+ oerings have been introduced as a novel take on Regulation A oerings, which have existed for a long time prior to the enactment of the JOBS Act. Before the JOBS Act, issuers raising under Regulation A were able to raise $5m for any 12-month period.98 Oerings made under Regulation A required issuers to le Form 1-A with the SEC,99 which is still required under the amended regulations. Although the disclosure obligations under Form 1-A are substantial, they are substantially reduced compared to that of traditional public oerings. For example, Regulation A+ Tier 1 and Tier 2 issuers must le balance sheets and other nancial statements as of the two most recently completed scal years. ey are not subject to Sarbanes-Oxley100 reporting requirements unless the company elects to become a ‘full reporting issuer’. Under the JOBS Act, the amended Regulation A+ provides two oering tiers:

• Tier 1 oerings ranging from $0-$20m: Tier 1 oerings are very similar to previous Regulation A oerings, with the major dierence being that the cap has been increased from $5m to $20m. Aside from the requirement to le a Form 1-Z ‘exit report’ aer the oering ends, Tier 1 oerings have no ongoing reporting obligations.

• Tier 2 oerings ranging from $0-$50m: is new tier has been the major change under Regulation A+. It has raised the oering threshold and introduced additional disclosure requirements. In particular, tier 2 lers must le audited nancial statements on an ongoing basis. However, they are exempt from all state Blue Sky laws and registration requirements. Crucially, oerings made under tier 2 can also be made to non-accredited retail investors.

Summary Reg A+ oerings under the second tier have been described as ‘mini-IPOs’. However, while the disclosure requirements remain substantial, oerings are capped at $50m. With many later stage rounds by venture capital rms reaching this size, it is questionable whether the reduction in disclosure requirements alone will be enough to induce startups and founders to choose this oering mode. To this date, there has not been an example of a notable ‘breakout’ technology company having raised funding under Reg A+.

Reg CF Oerings Title III of the JOBS Act, the Capital Raising Online While Deterring Fraud and Unethical Non-Disclosure Act of 2012 (“CROWDFUND Act” ),101 aims to facilitate a novel, internet-based capital raising process commonly referred to as equity crowdfunding. e CROWDFUND Act gives startups access to capital by permiing these companies to sell securities to both accredited and non-accredited investors without registering securities or completing the full disclosure requirements usually required for public oerings. In particular, startups can raise up to $1,070,000 through crowdfunding oerings in any 12-month period.102 Instead of ling an S-1, startups conducting a Reg CF oering have to le Form C through the SEC’s EDGAR system.103 e nancial statement requirements are based on the amount oered and sold in reliance on Reg CF within the

97See Dambra et al.(2015) (‘We nd a signicant increase in US IPO activity since the passage of the JOBS Act in April 2012, especially amongst small rms. Although IPO volume remains well below its pre-2001 levels, from April 2013 to March 2014, IPO volume and the proportion of small rm issuers was the largest since 2000.[…] us, our evidence suggests that the JOBS Act has increased IPO volume by 21 IPOs per year since its passage, which represents a 25% increase over 2001–2011 levels, although the short time period and sustained bull market since the passage of JOBS makes these estimates preliminary.’). 9817 C.F.R. § 230.251(b) (2014), amended by 17 C.F.R. § 230.251(a) (2017). 99SEC, Form 1-A, hps://www.sec.gov/les/form1-a.pdf 10015 U.S.C. § 7201 et seq. 10117 C.F.R. § 227. 10215 U.S.C. § 77d(a)(6)(A). 103SEC, Form C, hps://www.sec.gov/about/forms/formc.pdf

93 preceding 12-month period:104

• Issuers oering $107,000 or less: in this lowest bracket, startups can le nancial statements and federal income tax returns, which are certied by the principal executive ocer of the startup only.

• Issuers oering between $107,000 and $535,000: in this mid-tier bracket, the requirements are slightly higher, with nancial statements having to be reviewed by an independent public accountant.

• Issuers oering more than $535,000: in the last bracket, nancial statements, which are reviewed by an independent public accountant are still admissible, unless nancial statements are available that have been audited by an independent auditor.

A startup that has issued securities in a Reg CF oering is required to provide an annual report through Form C-AR on an ongoing basis.105

Summary While Regulation CF requires substantially fewer disclosures than traditional public oerings and Reg A+ oerings, these exemptions are quickly exhausted as the amount of funding sought under these rules gets larger. In particular, the information intermediary costs of public accountants and auditors are gradually ‘phased in’, meaning that there is a gradual rise of regulatory costs as the size of the oering increases. Given the overall low funding thresholds, capping Reg CF oerings at c. $1m per year, which is the size of a small private Seed round, it appears questionable that high quality startups will raise funding through the crowdfunding route, where a failure is public and the potential oering size is insubstantial. In particular, given that it subjects the startup not only to initial public disclosure costs, but ongoing disclosure requirements along the way.

Summary of JOBS Act measures As we have seen above, the JOBS Act measure to address the problem of prohibitively high public market disclosure costs are substantial and manifold. However, they essentially solve the same problem with the same solution. All measures rely on the same information intermediaries, in particular securities lawyers and accounting/auditing rms. All measures require the same traditional ling of forms with the SEC, with no substantial innovation in terms of removing friction at the disclosure layer. In addition, the caps under Reg A+ and Reg CF, make it unlikely that high quality startups with enough demand from venture rms will seek funding via this route.

2.5.1.2 Costs of the rm

When investments in technology startups are allocated through the venture rm structure, both the startup and the venture capital rm avoid the rigidities of the public disclosure obligations through exemptions from security laws, in particular those provided by Regulation D. Exemption at both the startup and venture rm level allows them to operate in the ‘shadows of securities regulation’ at substantially lower costs at the disclosure and information layer. However, this does not mean that there is no ow of information between surplus and decit agents. To the contrary, at the portfolio company level, there are frequent updates and bilateral disclosures made by the startups to the VC rm and, at the fund level, between the venture fund’s general partners (GPs) and their limited partners (LPs). However, as will be further outlined below, this is a private, bilateral exchange of information. Oen times, information is exchanged on the basis of contractual and non-contractual commitment devices, which (to the most part) do not rely on SEC mandated disclosure obligations. Given the private bilateral nature, disclosures are less formalized and do not pass through the expensive information intermediaries typically encountered in the public markets, such as securities lawyers and public accounting rms. Disclosures can also include a much wider set of business metrics and management considerations.

104SEC, Regulation Crowdfunding: A Small Entity Compliance Guide for Issuers, hps://www.sec.gov/info/smallbus/secg/rccomplianceguide- 051316.htm 10517 C.F.R. § 227.203.

94 As a result, the disclosures made under this regime may oen times provide more relevant information to investors (both at the VC rm and the LP level). When it comes to assessing the viability and growth potential of the startup’s business or the fund performance, they may hold more recent and indicative information than what would be mandated by security laws in the public markets. In particular, at the portfolio company level, founders can share concerns and ideas about the business more liberally and receive candid feedback from investors outside the scrutiny of the public. Venture fund managers, on the other hand, can provide limited partners with more granular, non-nancial insights into the performance and management plans of the portfolio companies, if they can make such disclosures in a less formal seing than through the public performance reports led with the SEC.

2.5.1.2.1 Venture capital rm

As will be outlined in more detail below, venture capital rms mostly operate in the shadows of securities regulation by way of an elaborate system of exemptions.106 Strict compliance with these exemptions is required at all time, if the venture rm wants to avoid becoming subject to SEC registration requirements and public disclosure obligations. Nev- ertheless, in the absence of security laws’ mandatory disclosure obligations, venture rms typically keep their limited partners (LP) closely apprised with respect to the latest performance of the fund’s portfolio companies. However, such performance disclosures are made exclusively to fund investors. Venture rms have historically been rather secretive and gone to great length to keep this information from being disseminated publicly. An illustrative example of this is an episode back in 2003, where the venerable venture capital rm Sequoia Capital took drastic action against one of their LPs, the University of Michigan endowment fund, which was forced by way of administrative law to disclose portfolio performance data. As a result of a Freedom of Information Act (FOIA) request made to the University of Michigan, the university’s endowment fund was forced to disclose the performance of its venture capital investments. Reportedly, Sequoia Capital immediately removed the endowment from the LP list of Sequoia Capital XI, the fund which Sequoia Capital was raising at the time and for which the University of Michigan had been given allocation. In addition, Sequoia Capital also requested the endowment to divest all its existing investments in already-commied earlier Sequoia fund vintages in the secondary market.107 Disclosure industry practice e relationship between venture capital funds and their limited partners is a long-term relationship, characterized by repeat interactions. Oen times venture capital funds raise their follow-on funds largely (or sometimes exclusively) from their existing LP base.108 Economically speaking, the interaction between limited partners and the venture fund can be understood as a repeated principal-agent game, where the principal reviews the actions of the agent periodically and rewards or punishes the agent by investing in follow-on funds or withholding funds respectively.109 us, much like for startups one intermediation layer below, it is important for venture capital rms to keep limited partners ‘in the loop’ at all times. For decades, it has thus been a standard practice of venture capital funds to provide investors (LPs) with periodic reports on the value and progress of portfolio companies, including an annual meeting with the fund managers (GPs) and selected management teams of portfolio companies.110

106See Kaplan and Lerner(2016) (‘Unlike mutual funds, venture capitalists are typically exempt from the Investment Company Act of Act of 1940, and typically do not disclose much information to the United States Securities and Exchange Commission or other regulators.’). 107See Hurdle(2005) (‘Although the University had invested in six Sequoia funds since 1992, in July 2003 Sequoia expelled it from the Sequoia Capital XI fund and asked it to leave several other funds. Citing concerns about “people who hope to prot from the sale of data about the venture capital business and newspapers interested in publishing articles about [the] business,” Sequoia terminated its relationship with the University.). 108See, for example, Primack(2011a) (reporting on the fundraise of Greylock’s fund XIII ‘Venture capital rm Greylock Partners yesterday announced that it has expanded its thirteenth fund to $1 billion, up from the $575 million it originally raised in late 2009.[…] All of the new commitments came from existing LPs.’); Primack(2011b) (reporting on the fundraising of Khosla Venture‘s $1.05bn fund IV which was ‘was oversubscribed, with over 90 percent of commitments coming from existing LPs. Considering that former cornerstone backer CalPERS decided not to re-up, that means a lot of increased commitments’); Bessemer Venture Partners(2011) (Bessemer reporting that the majority of the $1.6bn fund VIII was raised from existing LPs ‘Alongside existing BVP limited partners, Bessemer welcomed a select number of leading university endowments, corporations and family oces into BVP VIII as new LPs’). 109See Radner(1985) (showing that equilibria in repeated principal agent games can be achieved through a family of relatively simple strategy-pairs, referred to as ‘review strategies’. ‘Roughly speaking, in a review strategy the principal evaluates the cumulative performance of the agent since the last review. If the review results in a satisfactory evaluation, a new review phase is begun; if not, the players enter a penalty phase, aer which a new review phase is begun.’). 110See Sahlman(1990) (‘All venture-capital rms surveyed agree to provide the limited partners with periodic reports on the value and progress of portfolio companies, including an annual meeting with the general partners and selected portfolio-company management teams.’).

95 In the absence of SEC-mandated disclosure obligations, quarterly nancial disclosures made by venture rms to its limited partners are provided for on a contractual basis under the fund’s limited partnership agreement (LPA).111 Most central to these disclosures is the performance of portfolio companies, as measured by the internal rate of return (IRR)112 and the total value to paid-in multiple (TVPI).113 As Sahlman(1990) notes, assigning values to illiquid early stage startups is subject to managerial discretion, which is why the conservative practice is to write down ‘losers’ quickly and to ‘mark up’ valuations only when a startup raises its next (externally led) round at a higher valuation.114 However, much additional ‘avor’ is added in meetings or calls between the general partner and the limited partners. Importantly, these disclosures typically do not pass through the same set of expensive information intermediaries, which they would if the rms would report publicly to the SEC. Many VC rms have a specialized internal fund accountant and do not require a lawyer for regular portfolio updates. is gives the venture fund allocation a major cost advantage at the disclosure and information layer. Venture capital fund regulation Like other private equity rms, venture capital rms rely on a number of exemptions to avoid having to register with the SEC,115 most notably Rule 506(b)116 of Regulation D.117 In addition, they rely on venture capital-specic exemptions, such as rule 201(I) of the Investment Advisors Act of 1940.118 Notably, Rule 506(b) of the Regulation D exemption is by no way limited to the allocation of venture capital funds. Trillions of dollars are raised every year under this exemption, across a wide range of asset classes, including hedge funds and private equity funds.119 In fact, as will be outlined below, the VC funds’ portfolio companies also heavily rely on this exemption when raising money at the startup company level. In 2017, of the total of c. $1.8 trillion raised through c. 51k individual fund and non-fund oerings, venture capital allocation under Rule 506(b) of Regulation D accounted only for 3’913 individual oerings and c. $135.5bn raised.120 Regulation 506(b) of Regulation D uses a wide range of connecting factors which need to be met under federal securities regulation by venture rms if they want to avoid public disclosure obligations under federal securities regulations. In particular, these connecting factors include:

• transaction contracting criteria: ‘no general solicitation’ rules that venture funds need to adhere to when raising their funds;

• transaction party criteria: limitations on the types of investors that can be accepted by venture funds;

• rm-level exemption criteria: exemption from a qualication as an SEC-regulated investment company. e criteria are outlined in more detail below. Crucially, the rst two criteria apply similarly at the venture rm level and at the startup level. Transaction contracting criteria For transactions to qualify under Rule 506(b) of Regulation D, the venture capital fund must not engage in ‘general solicitation’ or ‘general advertisement’. While the terms are not dened in the statute, Rule 502(2) of Regulation D prohibits express advertisements, such as ads published in newspapers, magazines or other media, or any seminar or meeting where the aendees have been invited by way of general solicitation.121 Further to that, through the ‘no action 111See Spindler(2009) (‘Most private-equity limited partnership agreements call for some sort of regular disclosure to investors, such as aggregate annual and quarterly nancials, and these are generally required to be in accordance with Generally Accepted Accounting Principles (GAAP).’). 112See Phalippou and Goschalg(2009) (‘IRRs are frequently used as performance measures for private equity funds.’). 113See Phalippou and Goschalg(2009) (‘e widespread belief of good past performance of private equity mentioned in the Appendix is oen based on so-called multiples. e rst of these is the ”total value over paid-in capital” (TVPI); it is dened as the sum of all cash distributions plus the latest NAV, divided by the sum of all takedowns.’). 114See Sahlman(1990) (‘Because most investments are made in private companies with highly uncertain prospects, assigning values is very dicult. Oen the partners agree to recognize losses quickly and to write up the value of an investment only if there is a signicant arms-length transaction at a higher value. If no such transactions have occurred and no loss seems likely, cost is used as a basis for reporting.’). 115See Spindler(2009) (‘What makes private equity “private”? e very essence of private equity is exemption from the public securities laws’). 11617 C.F.R. § 230.506(b). 11717 C.F.R. § 230.500. 118See section 12(g) and Rule 12g-1; see also 15 U.S.C. § 781(g). 119See Bauguess et al.(2018) (‘In 2017, there were 37,785 Regulation D oerings reported on Form D lings, accounting for more than $1.8 trillion raised in new capital.’). 120See Bauguess et al.(2018). 12117 C.F.R. § 230.502(2).

96 leer practice’ of the Commission, there exists more specic guidance on what is covered by these terms. For example, where an investor has established a pre-existing,122 substantive123 relationship with the issuer, this will not constitute a ‘general solicitation’ in the eyes of the Commission. As this example demonstrates, the connecting factors used by security laws can be rather subtle contracting behaviors and mechanisms. Venture capital funds must thus thread carefully when raising funds, because exemptions are oen subject to fragile limitations. Transaction party criteria Further to the transaction contracting criteria, Rule 506(b) of Regulation D sets out transaction party-specic criteria as a connecting factor.124 For venture rms to fall within the Reg D exemption, investors can either be institutional investors or individuals, but must not include more than 35 non-accredited investors. Where the transaction party is a legal entity, it must either be a specialized rm125 and have assets in excess of $5m to be considered an accredited investor or be a legal entity126 in which all equity owners are accredited investors. As further explained below, venture capital rms do not classify as accredited investors by way of qualication as investment companies registered under the Investment Company Act 1940 (‘Investment Company Act’), but they are instead typically exempted by way of operating as a partnership that is fully owned by accredited investors. With respect to individual investors, securities regulation requires an individual to have either an annual income of at least $200,000 per year ($300,000 with their spouse)127 or a net worth of at least $1m.128 In summary, the connecting factor used by securities regulation in these instances rely on both institutional qualications or the monetary resources of the individual investors. Firm-level exemption criteria As noted above, venture capital rms do not qualify as investment companies under the Investment Company Act. To avoid qualication as such, they typically rely on the ‘3(c)(7) exemption’.129 is exemption can be understood as a sui generis accredited investor exemption for the venture capital rm: instead of relying on the term ‘accredited investor’ as a connecting factor under Rule 501(a) of Regulation D, it relies on the notion of the ‘qualied purchaser’.130 In short, a qualied purchaser under this set of rules can be described as an accredited investor with a higher nancial asset threshold, in particular $5m instead of $1m in the case of an individual. In addition to the exemption under the Investment Company Act, venture capital funds typically131 also seek exemption under Section 203(l) of the Investment Advisers Act of 1940 (‘Advisers Act’), also known as the ‘venture capital exemption’.132 Residual SEC reporting requirements By complying with the Reg D exemption requirements above, venture capital funds avoid the scrutiny of both the public market and the SEC. However, there do remain some residual reporting requirements for them with the SEC. In particular, despite the exemption under both the Investment Company Act and the Adviser Act, venture funds are still considered an ‘exempt reporting advisor’ and as such are required to provide an abbreviated Form ADV to the SEC. Summary e above shows in great detail how there exist a considerable number of connecting factors at the level of the venture rms that, if not carefully avoided, would require registration and public disclosure obligations. Unless close aention is given by fund managers to the many ‘pitfalls’ posed by security laws, these may quickly end up subjecting

122See, E.F. Huon & Company, SEC No-Action Leer (Dec. 3, 1985) (“In determining what constitutes a general solicitation the [SEC Sta] has underscored the existence and substance of prior relationships between the issuer or its agents and those being solicited” ). 123See, Citizen VC, Inc., SEC No-Action Leer (Aug. 6, 2015). (holding that “the quality of the relationship between the issuer (or its agent) and an investor” is a critical factor). 12417 C.F.R. § 230.506(b)(2). 12517 C.F.R. § 230.501(a)(1)-(3) (including banks, investment companies, retirement plans and charitable organizations, among others). 12617 C.F.R. § 230.501(a)(8). 12717 C.F.R. § 230.501(a)(6). 12817 C.F.R. § 230.501(a)(5). 12915 U.S.C. § 80a–3(c)(7). 13015 U.S.C. § 80a–2(a)(51)(A). 131Contrary to this general industry practice, the venture capital rm Andreessen Horowitz has recently announced that it will abandon this exemption. See Konrad(2019) (More aggressively, they tell Forbes that they are registering their entire rm — a costly move requiring reviews of all 150 people — as a nancial advisor, renouncing Andreessen Horowitz’s status as a venture capital rm entirely.). 13217 C.F.R. § 275.203(l)-1.

97 them to SEC supervision. In summary, operating in the ‘shadow of securities regulation’, both as a private rm and a private fund investor, can feel like walking over a complex mineeld for the involved agents given the many touchpoints of securities regulation.

2.5.1.2.2 Startup rms

Similar to venture capital funds, startups also typically seek exemption under securities regulation under Reg D at the portfolio company level. Despite the absence of formal disclosure obligations, a situation in which startups would leave their investors ‘in the dark’ is rarely observed. To the contrary, much like VC rms keep their LPs in the loop, startups have a keen interest to keep their venture investors apprised and updated. Disclosure industry practice Startups have an incentive to keep their investors well-informed over time. Since venture funding is typically provided as staged nancing,133 linked to certain milestones, it is important for startups to keep their venture investors informed and closely aligned at all times. Staged nancing is an important nancing mechanism for venture capitalists, as entrepreneurs may not have an ecient ‘stopping rule’. In other words, they may not quit a failing project, unless external funding is externally withdrawn.134 Unlike public market disclosures, which are oen limited to periodic disclosures, the ow of information with venture investors can oen be a continuous back-and-forth ow of information. Outside of board meetings, many information exchanges between the entrepreneur and the venture capitalists are not formalized and can take the form of emails, phone calls and increasingly even text messages.135 Such disclosures can dier between the dierent funding stages. Seed round At the seed round, startups are typically nanced through convertible notes, and more recently through variants, such as the ‘SAFE’ (Simple Agreement for Future Equity),136 draed by startup accelerator Y Combinator, or the ‘KISS’ (Keep It Simple Security),137 draed by the startup accelerator 500 Startups.138 Since these are convertible debt contracts, they do not give investors shareholder rights before conversion. Explanations in the economics literature for convertible debt are typically centered on ex ante information asymmetries.139 In the context of venture funding, it has been argued that convertible notes are used to resolve information asymmetries about the future actions of the entrepreneur.140 Others have argued that convertible debt is used in venture capital to deal with the double-sided moral hazard problem with respect to the managerial contributions of both the entrepreneur and the venture capitalist.141 A last stream of the nance literature has looked at the role of convertible debt as a way to mitigate distributional conicts in an exit event.142

133See Klausner and Litvak(2001) (‘Most important among these contract terms is the staged nature of the venture capital investment. Venture capitalists invest relatively small amounts at a rm’s early stage of development and then add capital incrementally aer observing a rm’s progress in relation to its initial projections.’). 134See Admati and Peiderer(1994) (‘If capital for the project is provided by outside investors, but the continuation decision is made by the beer informed entrepreneur, then it is easy to see that the entrepreneur has an incentive to continue projects even when it is optimal to abandon them. is occurs because the entrepreneur is not puing up the money for the continuation but does stand to gain if the option to continue pays o.’). 135See, for example, Harry Stebbings (Producer)(2020) (Vinod Khosla, founder of Sun Microsystems and founder Khosla Ventures, reecting on his communication channels with founder [at minute 22:10](‘I personally try to do this [communication] one-on-one with founders, I don’t go to a lot of board meetings.’). 136See Y Combinator(2013) (‘Y Combinator introduced the SAFE (simple agreement for future equity) in late 2013, and since then, it has been used by almost all YC startups and countless non-YC startups as the main instrument for early-stage fundraising.’). 137See 500 Startups(2014) (‘To keep convertible equity nancings quick and simple, 500 Startups has created the KISS legal docs. e KISS docs are short and sweet “open source” documents draed aer multiple discussions with a number of Silicon Valley law rms and early-stage investors.’). 138See Coyle and Green(2014) (‘In late 2013, an aorney at the startup accelerator Y Combinator proposed a “simple agreement for future equity” (“SAFE”) as an alternative to the convertible note.’). 139See, for example, Stein(1992) (relying on ex ante asymmetries between issuer and investors, allowing the issuer to covertly increase equity funding ‘is paper argues that corporations may use convertible bonds as an indirect way to get equity into their capital structures when adverse-selection problems make a conventional stock issue unaractive’.). 140See Cornelli and Yosha(2003) (‘At the time of initial venture capital nancing, the entrepreneur and the nancier are oen equally informed regarding the project’s chances of success, and the true quality is gradually revealed to both. e main conict of interest is the asymmetry of information about future actions of the entrepreneur.’). 141See Repullo and Suarez(2004) (focusing on the advisory role of the venture capitalist (‘is paper characterizes the optimal securities for venture capital nance in an environment with multiple investment stages and double-sided moral hazard in the relationship between entrepreneurs and venture capitalists.); Schmidt(2003) (also assuming a double sided moral hazard problem and viewing convertible debt as a way to encourage ecient investment ‘Convertible securities can be used to endogenously allocate cash-ow rights as a function of the state of the world and the entrepreneur’s eort. is property can be used to induce the entrepreneur and the venture capitalist to invest eciently into the project.’). 142See Hellmann(2006) (highlighting the fact that VCs receive dierent payos whether an exit occurs by IPO or acquisition ‘e optimal contract

98 From a disclosure perspective, convertible note instruments can also be seen as a way for smaller investors to ‘force’ the company to provide the investor with company updates. Since convertible notes with a xed maturity need to be extended in the absence of conversion, the startup is required to ask investors for such an extension, at which point the seed investor may ask for a company update in exchange.143 is is particularly important for angels and smaller funds, which cannot force disclosure by ‘withdrawing’ signal in follow-on rounds. Priced rounds (Series A+) Over the last decades, the Series A has oen become the rst round of institutional venture capital nancing, whereas Seed rounds have increasingly been lled by angels. At the Series A, unlike at the Seed round, preferred stock, rather than convertible debt is sold to investors. is makes the Series A the rst ‘priced round’ and gives investors formal shareholder rights, including rights to take board seats.144 From that point onward, the board meeting becomes the formal mode of communication.145 However, startups typically keep their venture capital investors informed between board meetings on an ad hoc basis as maers arise.146 With every additional nancing round, further board seats are given to venture investors, gradually shiing more control and oversight to investors.147 Arguably this further reduces the role of the board as a forum for disclosures, as investors are increasingly co-managing the company. Private startups under securities laws As mentioned already above, startups rely on the same exemptions from registering with the SEC under Rule 506(b)148 of Regulation D149 when raising their funding rounds. Without having to go into full detail again, many of the same connecting factors under Regulation 506(b) of Regulation D apply to startups. In particular, these connecting factors include the rst two mentioned above for the VC rms, namely:

• transaction contracting criteria: in particular ‘no general solicitation’ rules that startups need to adhere to;

• transaction party criteria: in particular limitations on the types of investors that can be accepted.

2.5.1.3 Comparative pricing

From the above, it appears that the legal and regulatory system places substantially dierent prices in terms of disclosure costs on investment transactions that occur privately through the venture capital rm structure, guided by exemptions from security laws, compared with public market transactions subject to securities regulations. If we tried to empirically compare the baseline costs at the disclosure layer, we could compare the legal and compliance costs associated with an S-1 ling to that of a private nancing round. e SEC has found that the upfront compliance costs for an initial public oering are c. $2.5m, with ongoing annual compliance costs of $1.5m.150 As a result, these compliance costs have traditionally priced smaller oerings completely out of the market.151 In contrast, startups oen execute convertible note nancings without any legal representation and the costs of legal representation gives the venture capitalist more cash ow rights in acquisitions than IPOs. is explains the use of convertible preferred equity, including automatic conversion at IPO.’); E. Gordon(2014) (developing an incomplete contracting framework that helps to explain the predominance of convertible debt in venture nancing ‘We demonstrate how the parties to venture capital arrangements can design the rm’s capital structure to mitigate the distributional conicts associated with a future sale of the rm’). 143See Jason Calacanis (Producer)(2019) (recounting the angel investor perspective at minute 40:01 ‘What was nice about convertibles is they had a term, two years, if they expired you could do an extension and that created a nice check-in point, because when I was doing my early angel investing I had that as a leverage with founders to say ‘Oh what’s going on with the company’. So if they went dark, they would email me and be like ‘Oh hey, I need to extend this note and I should catch with you about the businesses’ And I was like ‘Yeah, I emailed you three times, but I haven’t heard from you in a year’.’). 144See Barry, Muscarella, Peavy, and Vetsuypens(1990) (‘Venture capitalists oen participate directly in management; typically one or more serve on the company’s board of directors.’). 145See Rosenstein(1988) (‘For rms funded by venture capital organizations, the board of directors is a signicant interface between the venture capitalists and the internal management group.’). 146See Harry Stebbings (Producer)(2020) (Vinod Khosla (mentioned above) describing the role of board meetings in VC-backed startups as typically more advisory [at minute 21:31](‘In private company board meetings it is not as much duciary, as it is in public companies, it’s much more advisory.’). 147See Smith(2005) (analyzing a sample of 367 venture-backed companies completing IPOs between 1997 and 2002 ‘Because venture capitalists typically gain additional board seats with each round of investment, over time the board composition provisions of venture-backed companies tend to move from ”entrepreneur control” or ”contingent control” to ”investor control.”’). 14817 C.F.R. § 230.506(b). 14917 C.F.R. § 230.500. 150See SEC(2015) (‘Two surveys concluded that the average initial compliance cost associated with conducting an initial public oering is $2.5 million, followed by an ongoing compliance cost for issuers, once public, of $1.5 million per year.’). 151See SEC(2015) (‘Hence, for an issuer seeking to raise less than $1 million, a registered oering may not be economically feasible.’).

99 in priced rounds typically range between $30 and $50 thousand dollars.152 In addition, there exist vast dierences in terms of the speed of execution, with nancing rounds through the venture rm oen closing in a maer of days, while a going public process is typically a year-long process at the minimum. It is principally for these dierential costs at the disclosure and information layer that security laws and the mandatory disclosure requirements are oen thought to ‘price’ startups out of the public markets. e eect is that many founders will try to avoid the public markets for as long as possible. If one assumes an equal supply of capital by venture rms and the public markets, founders are actively disincentivized by the high compliance and information costs at the disclosure layer to seek public market funding and instead rationally prefer the ease of transacting with venture capital rms in the ‘shadows of security laws’.

2.5.2 Investment and liquidity layer

Whereas the disclosure and information layer governs the ow of information between the startup and the investor, the investment and liquidity layer governs the ow of funds between the two. Under the rst part of the eorem, the chapter compares the dierent investment and liquidity costs incurred by startups when raising money through the public markets under the supervision of the SEC with the costs of raising funding from venture capital rms under the existing Reg D exemptions. e investment and liquidity layer can be further broken up into primary market activities, which govern the primary ow of funds from investors to the startup, and secondary market activities, which govern intra-investor liquidity aer startup shares have been placed in the market. In practice, primary market activities typically involve an underwriter rm, which provides the startup with upfront nancing with the intention of re-selling the exposure as securities into the equity markets. Secondary market activities on the other hand typically involve market makers, regulated nancial exchanges or alternative OTC platforms through which shares can be traded amongst investors. Under the venture capital allocation, the ow of funds looks very dierent. Venture capital rms raise funds on a blind pool basis from investors. us, they do not pre-specify the startup companies that will be nanced with the funds collected from limited partners. is pre-nancing mechanism gives the venture fund an advantage in terms of speed of execution when they have identied an aractive startup for investment. On the other hand, once they have made an investment, they typically hold that position for multiple years. From the perspective of the startup, this makes the allocation through the rm structure ‘calmer’ than through the public markets. Startups are not exposed to the constant rigidities of the price mechanism under the secondary market. Instead, they can time when they want to re-price their startup’s valuation by raising the next round of nancing. As will be shown in this part, the fundamental dierences in the architecture of public markets and the venture rm results in signicantly higher economic and regulatory costs associated with operating as a public company.

2.5.2.1 Costs of the market

2.5.2.1.1 Primary market

As is the case for most securities markets, where an investor and an issuer transact through public equity markets, there is traditionally no direct ow of funds from the investor to the issuer. Rather, the initial economic transaction goes through an intermediary, an underwriter rm, which purchases (or ‘underwrites’) securities from the issuer. e underwriter then re-sells these securities to investors. is initial purchase is what the ‘investment leg’ of the investment and liquidity layer refers to. e current process of equity underwriting can be seen as a transitory (banking) rm allocation structure. is transitory banking-like allocation relieves both information asymmetries and liquidity concerns, as the underwriting acts as a reputational signal to the market and at the same time provides immediate liquidity to credit issuers. Over the

152See Jason Calacanis (Producer)(2019) (talking about YCombinator SAFEs and convertible notes at minute 39:14 ‘ey are both designed to be ve thousand dollars or less to execute, as opposed to a [price] round of funding which is $30k, $40k or $50k if you’re using one of the top [law] rms and that money comes out of the founder’s shares’).

100 last decades, as information asymmetries have eroded, equity underwriting fees have substantially declined. In fact, as will be further discussed in the second part of the eorem, some startup issuers have recently managed to successfully circumvent the traditional underwriting process altogether through direct listings.153 It is crucial to understand the nature of the underwriting business as a transitory (banking) rm allocation, as this means that investment banks, rather than serving as mere distribution channels, take on principal risk.154 In other words, underwriting activities are explicitly not a pure form market allocation. Traditional IPOs Traditionally, when technology startups go public through an initial public oering (IPO), they engage an investment bank on a rm commitment155 basis to underwrite newly issued shares, which are to be placed in the public markets. In the presence of signicant information frictions between surplus and decit agents, the role of the underwriter is to alleviate some of these frictions. is is achieved, both by acting as an information broker, but also by way of puing ‘skin in the game’ through the bank’s balance sheet commitment. us, at the investment and liquidity layer, information asymmetries are alleviated through a combination of both (i) data dissemination and data gathering measures and (ii) the signal related to the underwriting (i.e. transitory purchase) of securities by a reputable investment bank.156 With respect to the data dissemination and data gathering measures: this typically takes place in the context of a so-called ‘road show’.157 Within the scope of such a ‘road show’, both the issuer and lead underwriter will share nancial and business details with surplus agents158 and obtain indications of interest and price information in return (the so-called book building process). With respect to the costs of going public at the investment and liquidity layer, there are two major cost elements that startups are subject to, if they want to access the public markets:

• Underwriting fee: Traditionally, the issuers remunerate the investment bank for underwriting their IPO through an underwriting fee, also referred to as the ‘gross spread’. Historically, this ‘explicit fee’ has oscillated closely around the average range of 7 percent of the total oering amount.159

• Underpricing: In addition to the underwriting fee, there is an ‘implicit fee’ through the underpricing of newly issued securities. Underpricing means that the underwrien price is lower than what is considered to be the fair market value aer the book building process. IPO underpricing is hard to quantify, but typically the dierence between the oering price and the closing price at the rst day of trading day is used as a proxy.160 e range of IPO underpricing has varied widely in the past,161 with some of the most recent tech IPOs exhibiting rst day

153See Nickerson(2019) (‘In April 2018, music streaming giant Spotify disrupted the traditional initial public oering model and became a publicly traded company through a novel process known as a direct listing. Eschewing standard Wall Street practice, Spotify did not raise new money through the oering and instead simply made its existing shares available for purchase by the public. Spotify worked throughout 2017 and 2018 alongside legal counsel and investment banks and in communication with the Securities and Exchange Commission to facilitate the unorthodox approach.Major technology companies are now adopting a similar approach’). 154See Mandelker and Raviv(1977) (in the same vein, describing underwriting as an insurance function (‘Underwriting is the insurance function of bearing the risks of adverse price uctuations during the period in which a new issue of securities is being distributed. In practice, underwriting is oen combined with the distribution process but, in fact, the two are separate activities’). 155As opposed to a best-eorts underwriting, where the investment bank does not purchase the shares outright but only promises its best eorts in distribution. See Bower(1989) (‘In both types of oerings, the investment banker distributes the new shares, but in a rm-commitment agreement he or she performs the additional function of insuring the proceeds.’); Mandelker and Raviv(1977) (‘A “rm commitment” contract is one by which the underwriter purchases the entire issue outright and thus assures the issuer of receiving a xed amount of funds. If the issue does not sell well, the underwriter, and not the company, takes a loss.’). 156See Bower(1989) (Describing underwriting as a signaling device itself ‘In addition, a rm-commitment agreement uses the reputational capital of the investment banker to certify the value of the issue to a greater degree than in a best-eorts oering’). 157See Sjostrom(2001) (‘With an underwrien IPO, this involves responding to underwriter due diligence requests, aending draing sessions and participating in the road show’.). 158Typically, these decit agents are limited to buy-side institutional investors, such as pension funds, mutual fund managers, life insurances or endowments. 159See H.-C. Chen and Rier(2002) (‘In the period from 1995 to 1998, for the 1,111 IPOs raising between $20 and $80 million in the United States, more than 90 percent of issuers paid gross spreads of exactly seven percent.’). 160See Hurt(2005) (‘Generally, the IPO share price usually rises above the oering price during the rst day of trading. is increase may be modest or almost incomprehensibly large. Assuming that the resulting price is the ”market price,” many commentators then refer to the IPO oering as being ”underpriced”.’). 161See Derrien and Womack(2003) (recounting the IPO of Broadcast.com, which appeared to be heavily underpriced and thus popped by 277 percent in the rst day of trading); Baker(2000) (recounting the VA Linux Systems, Inc. IPO which was priced at $30 per share and popped to $239 per share on the rst day of trading).

101 ‘IPO pops’ of up to 80 percent.162 Rier(2020) provides a list of 272 IPOs for the time frame between 1975 and early 2020, for which the share price has doubled in the rst day of trading.163

e investment layer can be the most expensive functional layer for startups entering the public markets. Bill Gurley, a venture capitalist at Benchmark, is known in the industry as a vocal critique of the established underwriting practices. He argues that the way in which market oerings are currently structured and run, results in a substantial wealth transfer from startups and founders to (institutional) IPO investors.164 It should be noted, however, that this is not primarily a shortcoming of security laws (regulatory costs), but rather of startups failing to negotiate higher oering prices with underwriters (baseline costs). In these negotiations, startup founders are systematically disadvantaged as investment banks are repeat players in primary markets, whereas startup founders are typically involved in not more than one IPO throughout their professional career.165 Regulation of traditional IPOs e SEC regulates underwriting activities in the primary market through the market-enabling rms, namely by supervising investment banks as broker-dealers.166 IPO underwriting practices are further regulated by the Financial Industry Regulatory Authority (FINRA), a private corporation that acts as a self-regulatory organization (SRO).167 For example, FINRA rule 5130 sets out that underwriting rms may not purchase a new issue in which the underwriter has a benecial interest, except for ‘sticky securities’,168 securities which the underwriter is unable to sell to the public.169 JOBS Act oerings e primary market for oerings made under the JOBS Act, in particular Reg A+ and Reg CF oerings,170 looks very dierent. Securities Act Section 4A(a)(1)171 requires platforms processing crowdfunding transaction under the JOBS Act to register with the SEC, either as a traditional broker-dealer172 or as a registered.funding portal173 Fundraising for these JOBS Act categories typically does not rely on the underwriter model, instead issuers access the capital markets ‘directly’ through an online funding portal. us, startups do not go through the ‘transitory rm allocation’, which has been traditionally associated with investment bank underwriting. In this sense, Reg A+ and Reg CF oerings are logically and structurally already very close to a pure market allocation. However, this does not mean that the underwriting model in these markets is completely absent and that it could not evolve over time. A rst empirical analysis of the SEC indicates that most Reg A+ oerings are indeed not underwrien, with only 13% of Tier 1 oerings and 23% of Tier 2 oerings seeing underwriter involvement.174 Such direct market allocation comes with signicant uncertainty, risks

162See Kate(2019) (reporting on the 81% rst day pop of Zoom ‘Shares of Zoom (Nasdaq: ZM) began trading at $65 a pop ursday morning aer the video conferencing unicorn priced its shares at $36 apiece Wednesday, above its anticipated range.’). 163See Rier(2020) (highlighting the outlier ‘e ten biggest rst-day percentage increases are: the Va Linux 12/09/99 697.50%, Globe.com 11/13/98 606%, Foundry Networks 9/28/99 525%, Webmethods 2/11/00 507.50%, Free Markets 12/10/99 483.33%, Cobalt Networks 11/05/99 482%, Market- Watch.com 1/15/99 474%, Akamai Technologies 10/29/99 458%, Cacheow 11/19/99 426.56% and Sycamore Networks 10/22/99 386%.’). 164See Basak and McBride(2019) (‘e venture capitalist estimated that more than $6bn in wealth has been transferred from companies to IPO investors from recent stock-market debuts that jumped soon aer the oering. “e buy side has been trained for free giveaways. ey’re more entitled than a millennial,” he said. “It’s a very long-term issue that has negatively impacted our industry.”’). 165See Patrick O’Shaughnessy (Producer)(2019b) (Venture capitalist Bill Gurley speaking to that eect at minute 4:32 ‘e reason why I think this happens, and the reason why this has been so systematic and happened over and over again, is you have a massive frequency mismatch. A Silicon Valley entrepreneur, founder, CEO is most likely to do one IPO in their lifetime. Actually, they have to be very successful and then they get to do one. e number of people who touch two is very small. So, if you look at the other people involved in the process, the investment bank and the buy side, the mutual funds buying the issuance. e others do 20 to 40 a year versus one in a lifetime.’). 166Securities Exchange Act of 1934 section 3(a)(4)(A), 15 U.S.C.A. § 78c. 167See Tuch(2014) (As broker-dealers, investment bankers must register with the Financial Industry Regulatory Authority (”FINRA”) and comply with its rules, including the requirement to ”observe high standards of commercial honor and just and equitable principles of trade.” As the self- regulatory body for broker- dealers, FINRA functions as the equivalent of the self-regulatory bodies governing other professionals, such as lawyers and accountants.’). 168See Winnike and Nordquist(1993) (‘In some cases, an oering will prove to be less aractive to investors than anticipated. If the lack enough of serious demand is obvious, the oering may be postponed or abandoned. In some instances, however, the participants will have elected to go forward before they are fully aware of, or notwithstanding, an inadequate demand. is lack of demand may be manifested in an incomplete “book” of orders […] is type of situation is called a “sticky oering” […] When an oering turns sticky and the underwriter is forced to purchase shares for its own investment). 169See FINRA MANUAL R. 5130 (2020) (‘Nothing in this Rule shall prohibit an underwriter, pursuant to an underwriting agreement, from placing a portion of a public oering in its investment account when it is unable to sell that portion to the public.’). 170JOBS Act oerings under the reduced disclosure obligations for companies with ECG status go through the traditional underwrien IPO process described above. 17115 U.S.C. 77d(a)(6). 172Securities Exchange Act of 1934 section 3(a)(4)(A), 15 U.S.C.A. § 78c. 17317 C.F.R. § 227.400. 174See Knyazeva(2016) (‘e majority of oerings were conducted on a best-eorts, self-underwrien basis, consistent with the small oering size

102 and marketing costs for the startup and its founders. Startups raise Reg A+ and Reg CF oerings through a few equity crowdfunding platforms that have since established themselves around the new regulations: Wefunder, StartEngine and Seedinvest. In addition to the formal SEC lings, an oering typically requires the rm to make a promotional video and engage in online Q&A sessions with potential investors through the platform. All of this while facing a lot of uncertainty, about whether enough investors will eventually ‘bite’. Startups specify a minimum fundraising target ex ante, but they do not know whether that fundraising target will be met. If it is not reached, or uptake is very limited, there is a risk that this can also have negative repercussions on private fundraising, as failure to raise from the crowd may provide a negative signal.

2.5.2.1.2 Secondary market

In many ways, the secondary public market can be considered the quintessential market allocation. rough the price mechanism of secondary market trades between investors, there is a constant pricing and re-pricing of public companies. For startups entering the public markets, this is a major change to the way in which they operate. Under the venture rm allocation, startup founders can largely escape the rigidity of the price mechanism, as there is typically no re- pricing between the funding rounds. In fact, by timing the follow-on round fundraising, founders can actively time the re-pricing of their companies. ey can strategically time it such that it falls within a period of positive growth and revenue traction. is is dierent, once a company has gone public and the market constantly scrutinizes and re-prices the rm by way of secondary trades. Traditional oerings Traditionally, when startups go public, they list on one of the two SEC-regulated national exchanges: the Nasdaq or the New York Stock Exchange (NYSE). To be listed on one of these exchanges, startups are required to pay an annual listing fee. Furthermore, issuers typically engage a market maker to provide secondary market liquidity aer the IPO. In practice, this is oen the investment bank taking the startup public. Again, the startup is required to pay annual fees for the services of market makers in the secondary market. In the past, secondary equity market liquidity was dominated by trades through the Nasdaq and the NYSE. However, over the past decades, fueled by technological changes, equities markets have seen increasing fragmentation and at the same time consolidation, as new players have entered the industry and incumbents have linked up existing liquidity pools. Today, while the primary exchanges still play a major role, a large part of the order ow is eectively routed through electronic communications networks (ECN), alternative trading systems (ATS), and more opaque crossing networks known as ‘dark pools’.175 However, despite the rise of alternative trading systems and liquidity providers in the secondary market, startups going public typically still prefer a traditional listing on a primary exchange. Regulation Under security laws, the primary exchanges, such as the NYSE and the Nasdaq, are regulated as national securities exchanges.176 On the other hand, the providers of alternative secondary market mechanisms, including ECNs, ATS, and internal crossing through dark pools are typically regulated as broker-dealer rms.177 e tech industries’ struggles with the secondary market mechanism In the past few years, technology startup founders have oen reported that they are struggling with the scrutiny of secondary markets. Oen times, these struggles have reached a climax during equity analyst calls, where startup founders have been asked probing questions on the strategy and growth trajectory of their rms. In an analyst call in 2018, for example, Tesla co-founder Elon Musk clashed with an equity analyst inquiring about the company’s sales fore- and the small size of a typical issuer.’). 175See Eng, Frank, and Lyn(2013) (‘Over the past three decades, the equities markets all over the world have seen periods of fragmentation and consolidation as new players enter the industry, or two or more entities combine to unify liquidity pools. Despite major industry mergers, traders currently have more venues to send order ow than ever before. ese include the primary exchanges, electronic communications networks (ECN), alternative trading systems (ATS), and opaque crossing networks which are more commonly known as ”dark pools”.’); Batista(2014) (‘Once a place ruled by humans, new advances in technology have transformed the stock market into a virtual no man’s land where most traders are now aided by super computers and advanced trading soware. Advances in technology and new Securities and Exchange Commission (”SEC”) rules allowing for greater access to the exchanges have given rise to high frequency trading (“HFT”).’). 176Securities Exchange Act of 1934 section 6, 15 U.S.C.A. § 78f. 177Securities Exchange Act of 1934 section 3(a)(4)(A), 15 U.S.C.A. § 78c.

103 casts and eventually asked the analyst to stop with the ‘boring bonehead questions’.178 Similarly, Snapchat founder Evan Spiegel clashed with an equity analyst in 2017, asking the analyst ‘to go to Google’ aer the analyst had unintentionally muered a critical remark into an unmuted microphone.179 JOBS Act oerings Just like the primary market, the secondary market for oerings made under the JOBS Act, in particular Reg A+ and Reg CF oerings,180 looks very dierent from ‘regular’ public oerings. As a general rule, investors should not expect any secondary market liquidity for these oerings. For Reg CF oerings, a certain level of illiquidity is even mandated through explicit securities regulations: investors in startup shares under Reg CF cannot sell their shares for a one-year period, except where they sell the shares (i) back to the issuing company, (ii) accredited investors, (iii) as part of an ordinary SEC-registered oering or (iv) to a family member.181 However, even aer this lock-up period expires, liquidity of these securities is poor, given that the funding portals do not currently oer secondary markets. is, could change in the future, as one of the large crowdfunding platforms, StartEngine, has already registered as a broker-dealer in 2019 and is planning to oer a secondary market, StartEngine Secondary, in the near future.182 On the other hand, Reg A+ securities are not restricted and can be re-sold freely by investors. In particular, Reg A+ oerings made under the second tier are increasingly listed on OTC secondary markets, such as the OTCQX, OTCQB and the Pink Open Market.

2.5.2.2 Costs of the rm

When startups are nanced through the venture capital rm structure, things look very dierent to the public market allocation. Generally speaking, things seem much ‘calmer’ from the outside, as much of the ‘action’ that is played out publicly in the markets, takes place behind closed doors in the shadows of securities laws. Rather than ringing the NYSE opening bell, startup founders sign late stage funding rounds in aorney oces. Rather than waiting for the ‘lock up’ period to lapse before they can sell their shares in the public markets, they sometimes sell a considerable portion of their founder shares already in the late stage, growth capital rounds.

2.5.2.2.1 Primary activities

Under the market allocation, the primary market is dominated by the underwriter model, whereby an investment bank acts as a transitory rm allocation and certication agent for the startup. In contrast, under the venture fund allocation, primary activities are multi-layered. ere are two levels of interaction with investors: at the level of the venture rm and at the level of the startup. At both layers, there exist dierent metrics, investors and timelines. Venture fund Under the public market allocation, startup issuers and their underwriters market an equity oering for a specic rm to investors. Under the venture capital allocation, the primary activities look very dierent in this respect. Venture capital rms raise funds on a blind pool basis from investors. us, they do not pre-specify the exact identity of the startup rms that will be nanced with the limited partner’s funds. Instead of specifying the future investments, venture funds oen raise new funds with the track record of their existing funds and their respective portfolio companies. From interviews conducted within the scope this PhD thesis, it has emerged that this may provide an incentive for many VCs to quickly ‘mark up’ deals in their existing portfolio, sometimes by way of encouraging founders to quickly raise the next round, led by another VC rm at a higher valuation (so-called ‘up rounds’).

178See Cox(2018) (‘Tesla’s 1Q18 analyst conference call was arguably the most unusual call I have experienced in 20 years on the sell-side,” Adam Jonas, equity analyst at Morgan Stanley, said in a note to clients. “Many investors we spoke with post the call agree.’). 179See Anita(2017) (‘e conversation came aer Snap’s fell short of analyst expectations, with revenue and user growth disappointing for the second consecutive quarter.’). 180JOBS Act oerings under the reduced disclosure obligations for companies with ECG status are listed on the regular national securities exchanges. 18117 C.F.R. § 227.501. 182StartEngine, What StartEngine’s Broker-Dealer Means for Investors, hps://www.startengine.com/blog/what-startengines-broker-dealer-means- for-investors/

104 Venture fund vehicles have an average term of roughly ten years. is is oen split in a 3-year ‘investment period’, where the initial portfolio is constructed, with another 7 years, where follow-on investments are made from the fund’s reserve capital, and a ‘harvesting period’ at the end of the fund’s lifecycle, where startup investments are realized. Typically, a venture fund raises a new fund aer the rst three to ve years of the fund’s lifetime, i.e. aer the prior fund’s ‘investment period’ has ended. Well before all proceeds from an existing fund are ‘harvested’, a the venture rm goes back to its LPs to ensure that the rm can continue its deployment pace.183 In other words, in venture capital, the investment and distribution periods can overlap over multiple fund vintages. In other words, the venture rm allocation can be understood as an implicit pre-raise on the basis of past performance. Compared to an IPO, where a concrete startup issuer is looking for nancing in the spot market, an investment in a venture fund is an investment in startups that have oen not even been conceived at the point of the investment. Limited partners in VC funds thus invest in the general partners’ reputation, network and ability to source future high quality deal ow. e pre-raise feature ensures a commied pool of funds and speed-of-execution. Fund managers are incentivized to scour the market for potential deals. Once they have found a suitable target, they can deploy funds more quickly and eciently than if they had to go out and raise money on a deal-by-deal basis (e.g. through specically set up SPVs, which emerging managers oen do). It is important to note that portfolio startups are oen completely shielded from the fundraising process that occurs at the level of the venture fund. While sometimes, new limited partners may ask for reference calls with portfolio company founders during their due diligence, the interaction between the venture fund’s limited partners and the startups remains very limited. From the perspective of the startup, this is highly benecial, as it means that founders are eectively negotiating with an already commied, pre-nanced pool of capital, when they are raising capital from venture funds. Regulation With respect to securities regulation, the existing exemptions discussed under the disclosure layer apply in this context as well. In particular, with the exception of the abbreviated Form ADV, venture funds are mostly exempt from the mandatory disclosure obligations with the SEC. is is crucial, given that there may be a lot of individual nancial transactions occurring at the fund level over time. In particular, the limited partner commitments to a venture fund are typically not paid upfront. Instead, for every startup investment, the fund makes a ‘capital call’ to its investors in the amount required by the new investment. If venture funds were not exempt from ling requirements under the Reg D exemptions, it is likely that each of these capital calls would trigger a disclosure or registration requirement. Startups At the level of the startup, ‘primary activities’ refers to the fundraising of new capital from venture rms and growth capital rms, oen staged over multiple funding round. e primary market activities under the public market allocation require that the startup communicates with a large crowd of investors – ‘the world at large’. is is not only a stressful, but also time-consuming and costly endeavor for small companies. On the other hand, where startups raise money through the venture rm structure, they typically only have to negotiate with a few individuals leading the deal at the venture rm. ese fundraising discussions may sometimes start well in advance of the actual raise, such that the understanding of the business from the investor side has already matured at the time of the investment decision. e startup founder may actively time the raise around a period in which the company has hit a particular milestone or achieved a certain level of traction in the market. Venture funds may indicate such particular milestones in earlier meetings, such that startup founders can work towards them. Under the public market allocation, underwriting fees and underpricing in the primary market typically make up the bulk of the costs of going public. In contrast, where startups raise money privately under the Regulation D exemption from venture capital rms, they can stay clear of these direct and indirect costs. e intermediation costs of the venture rm, consisting of management and performance fees, are borne by the VC rm’s limited partners (LPs), rather than the nanced startups. Furthermore, these venture capital ‘fees’ are completely hidden from the startup founders, as LP-GP

183See Sahlman(1990) (‘In each new fund, the capital is invested in new ventures during the rst three to ve years of the fund. ereaer few if any investments are made in companies not already in the portfolio, and the goal is to begin converting existing investments to cash. As investments yield cash or marketable securities, distributions are made to the partners rather than reinvested in new ventures.’).

105 negotiations have already materialized at the point the startup is raising capital. us, startup funding negotiations can revolve around the company valuation only. Founders can negotiate concrete pre-money/post-money terms from the rst meeting, without having to worry about market uptake and how much money they are ‘leaving on the table’ through IPO underpricing. e exemptions under security laws discussed under the disclosure layer apply here as well. e ability of startup founders to engage in ‘primary activities’ at multiple points over the lifetime of a startup, namely at dierent funding rounds, can be seen as a clear advantage that the venture funding model holds over the public market allocation. In particular, it allows the founder to time the pricing and re-pricing of the rm, such that it coincides with high growth periods where the company can report on positive milestones and market signals.

2.5.2.2.2 Secondary activities

Under the venture rm allocation, ‘illiquidity’ has always been the dominating principle aer the initial investment. When a venture rm decides to back a startup, this is typically a multi-year commitment. If everything goes to plan, the venture rm holds on to the initial investment until an exit is realized many years later. In addition, over multiple follow- on nancing rounds, the venture rm typically re-invests capital by taking up its pro-rata rights share and sometimes even more. is makes the allocation through the venture rm a rather quiet and cozy place for startups, especially compared to the public markets. Whereas public markets constantly price and re-price a company’s shares, private startups nanced by VC rms can largely avoid the rigidities of the price mechanism. By deciding when to raise their next round of nancing, startup founders can time the next valuation and have it coincide with growth traction and major company milestones. However, as will be shown below, this principle of illiquidity has been somewhat watered down and challenged over the past decade, especially at the portfolio company level through changes to the ‘500 shareholder rule’ made by the JOBS Act. Venture fund Secondary activities at the level of the venture capital rm are rather limited, as limited partners commit their capital for the full period of the fund. Subject to limitations in the LPA, LPs can sell their interests in venture funds before the lifetime of the fund has expired through so-called LP secondaries. Nadauld, Sensoy, Vorkink, and Weisbach (2019) report that the secondary markets in private equity limited partnership (LP) interests are relatively thin. is is because there exist contractual selling restrictions in the partnership agreements, few buyers and sellers and high information asymmetry.184 In either case, secondary market activity at the LP level does not aect the underlying portfolio companies and their interaction of the startup with the GP. Startups Traditionally, there existed very lile secondary market liquidity for startups under the venture rm allocation. As investments by the VC rms were by their nature illiquid, they provided much quieter or soer capital compared to public market nancings. In particular, the decade-long holding period of VC investments allows the funded startups to take managerial decisions with longer time horizons. While founders are subject to active oversight and reporting requirements towards their venture investor, they are still able to correct, pivot and experiment in a private ‘playground’. is stands in contrast to a public market allocation, as founders are shielded from the constant market scrutiny, which provides a major benet of staying private longer. In many cases, and increasingly so over the past decade, founder- friendly venture capitalists even are actively contribute to the success of the startup’s operations by making key business and hiring introductions through their network. Secondary private markets Over the past decade, the liquidity situation under the venture rm allocation has changed substantially. In particular, as the JOBS Act has removed the ‘500 shareholder rule’, which acted as a de facto selling restriction, an active private secondaries market in startup shares has evolved. is makes the allocation under the venture rm structure substantially less quiet. However, it also comes at substantial benets to founders in terms of geing earlier liquidity. 500-shareholder rule 184See Nadauld et al.(2019) (‘Transactions costs in this market are high for reasons suggested by market microstructure theory: that is, it is a relatively thin market with few buyers and sellers in which asymmetric information is likely to be high.’).

106 For decades, late stage startups were forced into going public by ‘an obscure provision of the Securities Exchange Act’, as the New York Times has termed Section 12(g) of the Securities Exchange Act prior to Google’s IPO.185 ereby, once a startup reached over 500 shareholders of record (the ‘500-shareholder rule’), they had to register with the SEC on an ongoing basis. It is a generally accepted fact that the 500-shareholder rule triggered the initial public oerings (IPO) of major technology rms including Apple, Google, and Facebook.186 Prior to its IPO in 2004, Google was protable nearly from the beginning, able to nance its ongoing operations, growth, and acquisitions without recourse to the public markets, not having raised any outside venture capital since 2000. However, in 2004 it bumped up against the 500-shareholder limit and had to le for its IPO. Similarly, Facebook, like Google, managed to largely self-fund for many years from incoming ad revenues. However, aer being private for eight years, many Facebook employees had eventually vested their shares and the company soon found itself at the brink of the 500-shareholder threshold.187 In 2012, the JOBS Act188 raised the threshold to 2’000 shareholders of record and provided that many employee shareholders would not be counted towards this total. Eect of the changes to the ‘500-shareholder rule’ e second-order eects of quadrupling the threshold to 2’000 have been signicant: an empirical study by the SEC(2012) found that only 13% of Section 12(g) registrants would have been required to register with the Commission pursuant to the amended threshold.189 First and foremost, raising the threshold has allowed startups to stay private longer under the venture rm allocation structure.190 However, the eects on secondary market transactions of privately held shares have arguably been even more profound. In particular, given that employees are no longer counted towards the threshold, startups can aord to have substantially more shareholders on their cap table. At the early stage of the startup, this has helped to fuel the rise of much larger angel rounds with smaller individual check sizes. At the later stages of a startup’s lifecycle, this has fostered the emergence of a private market in startup shares, the so-called ‘secondaries’ market. While such secondary transactions already existed prior to the regulatory change, they were typically restricted to larger institutional buyers and sellers. For example, Accel Partners sold about 15 percent of its investment in Facebook, valued over $500 million via a secondaries transaction prior to the Facebook IPO.191 Since the change to the 500 shareholder rule, secondaries markets have exploded in volume and importance for startups. Increasingly, these transactions have been routed through specialized online platforms, with SecondMarket and Sharepost being the rst movers in the space.192 In 2015, SecondMarket was acquired by Nasdaq, allowing the SEC-registered national securities exchange to now be also active in this (still) niche private markets segment.193 More recently, the late stage startup Carta has raised a $300m Series E in the summer of 2019 to move into this segment by building a ‘private stock market for companies’.194

185See Marko(n.d.). 186See Sjostrom(2011) (‘Facebook has never been in a hurry to go public, especially because it has been able to privately raise plenty of capital. Widespread speculation, however, has the company ling to go public in April 2012. is is because Facebook has well over $10 million in total assets and recently disclosed that it plans to surpass 499 shareholders this year. December 31 is the last day of Facebook’s scal year, so assuming it has 500 or more shareholders on that date, it will be required to le a Form 10 by April 29, 2012. […] Google faced a similar situation in 2003. It had total assets of well over $10,000,000 and more than 500 shareholders on December 31, 2003, the close of its scal year. us, it ended up ling a Form 10 on April 29, 2004, the same day it led its IPO registration statement. As Google noted in its registration statement: “By law, certain private companies must report as if they were public companies. e deadline imposed by this requirement accelerated our decision [to go public].’); Rodrigues(2015) (questioning the overall relevancy of the ‘500 shareholder rule’, but conrming it in the cases of Apple, Google and Facebook). 187See Solomon(2011) (‘Facebook will almost certainly have to go public during this time whether it wants to or not’). 188JOBS Act § 201(a); 17 C.F.R. § 240.12g-1(b)(1). 189See SEC(2012) (‘In calendar year 2011, there were approximately 2,983 companies with a class of securities registered under Section 12(g). Shareholder of record data was available for 2,524 of these companies. Of these 2,524 companies, only 318 companies had more than 2,000 shareholders of record.’). 190See Coee(2013) (‘By amending §12(g)(1) of the Securities Exchange Act to raise the mandatory threshold for “reporting company” status to 2,000 shareholders of record, the JOBS Act has also ensured that all companies that are not yet a “reporting company” will have considerable discretion and a debatable choice about whether to become one.’). 191See Ramsinghani(2018) (‘Early investors are especially keen on the legitimate transition/exit as a way to cash in some of their earnings. Accel Partners sold about 15 percent of its stake in Facebook, then valued over $500 million via secondary markets. While still a fraction of what is traded in public exchanges such as the New York Stock Exchange and NASDAQ, private exchanges have brought a much needed third exit option to venture practitioners.’) 192Ibrahim(2012) (‘In 2009, VC secondary markets got a signicant boost. Two electronic marketplaces, SharesPost and SecondMarket, launched as platforms for intermediating secondary market transactions.’). 193See Roof(2015) (‘NASDAQ has agreed to buy SecondMarket Solutions to combine forces with the NASDAQ Private Market. e group will facilitate the exchange of shares for private companies […] As startups stay private longer, there is an increased need to provide liquidity for shareholders’). 194See Loizos(2019) (‘Carta has so many pieces in place that in a call on Friday, founder and CEO Henry Ward told us Carta is taking what may be its biggest step yet and becoming the rst real “private stock market for companies.” Its massive new funding round is “about act three,” he said.

107 e rise of secondary markets has been welcomed by startup founders and employees, who can get earlier liquidity through such transactions. rough nal public oerings (FPOs), founders can oen get substantial liquidity at late stage nancing rounds, similar to what would have previously been realized in an IPO. However, on the other hand, the active secondaries market subjects private startups to a price mechanism not unlike that of the public market.

2.5.2.3 Comparative pricing

From the analysis above, it appears that existing security laws impose substantial direct intermediation and indirect liquidity costs on the allocation through the public market. While the SEC has in the past limited its cost analysis of IPOs to the 7% underwriting fee,195 the more expensive part of going public oen turns out to be associated with the systematic underpricing of new issuances. In particular, under the existing underwriting practice, startup founders are oen forced to ‘leave money on the table’ in what has been referred to as a ‘multi-million dollar marketing event’.196 As will be discussed further under the second part, this has led to a backlash of startup founders, who are increasingly opting to go public through ‘direct listings’ instead. Furthermore, the rigid scrutiny and constant price mechanism within secondary markets has made it cumbersome for founders to operate public companies. On the other hand, the venture fund allocation, operating in the ‘shadows of securities regulation’ through Reg D exemptions, appears to be much beer tailored to the needs of startup founders. By pre-raising fund structures on a blind pool basis, venture funds can work with commied pools of capital through which they can quickly deploy funds, whenever a suitable target startup is identied. Startups, on the other hand, can time the pricing and re-pricing of their company, by strategically raising in high growth periods, where signicant milestones have been achieved. In the secondary markets, the venture rm allocation, which has traditionally been characterized by utmost illiquidity, now oers startup founders earlier liquidity through ‘secondaries’, in part as a result of the JOBS Act. Empirically, the costs of the investment and liquidity layer under the venture rm allocation are a fraction of the 2% in management fee and the 20% performance fee that venture capital funds typically charge their LPs.

2.5.3 Diversication layer

e diversication layer is key to a comprehensive understanding of both the benets and costs of the (venture) rm and the (public) market allocation, respectively. Most investors get exposure to equities through the diversication layer. us, rather than investing in single stocks and selecting individual companies, they are invested in the markets through fund vehicles or retirement plans. e essence of diversication is a shi of economic exposure away from a single source of risk towards multiple sources. e concept of diversication has been covered extensively in the nance literature, most prominently through the capital asset pricing model (CAPM) and the ecient market hypothesis (EMH). Against the backdrop of these dominant streams in the nance literature, the general notion is that investors are generally best served if they can invest in a fully diversied ‘market portfolio’. As we will see below, market-enabling rms in the public markets have rened and almost perfected diversication over the past decades. In particular, exchange-traded funds (ETFs), by oering diversication through ‘indexation’, manage to closely approach Markowitz’s stylized ‘market portfolio’. From the perspective of the public market investors, such diversication provides tangible benets in terms of idiosyncratic risk exposure and liquidity. However, from the perspective of a startup entering the public markets, having investors with innitesimal exposure does not necessarily provide much value and/or align interests with investors. On the other hand, venture capital rms are notoriously ill-diversied. Instead of diversifying over as many startup investments as possible, they intentionally take concentrated bets. In fact, these bets even get more concentrated over

“Now that you have this network of companies and investors all on one platform and the ability to transfer securities, you can build liquidity on top of it.”’). 195See SEC(2015) (‘Moreover, issuers conducting registered oerings also usually pay underwriter fees, which are, on average, approximately 7% of the proceeds for initial public oerings,’). 196See Patrick O’Shaughnessy (Producer)(2019b) (Venture capitalist Bill Gurley referring to underpricing and the IPO pop as a multi-million dollar marketing event at minute 23:32 ‘e notion that paying $500m for a short-term markting event ies in the face of long-term thinking.’).

108 the lifetime of the fund, as the VC rm ‘doubles down’ on the winners. It is a standard practice of venture funds to put aside ‘reserve capital’ for such follow-on, pro rata investments. From the perspective of the startup, this provides a very welcome alignment of interest between the startup founders and the venture rm. On the other hand, for investors in venture funds, this leads to suboptimal diversication at the VC fund level. As a result, venture fund investors (LPs) typically only allocate a rather small portion of the entire portfolio to the venture asset class.

2.5.3.1 Costs of the market

Diversication in public equity markets can be provided by a range of dierent market-enabling rms that manage diversication vehicles. ese institutions vary signicantly in terms of diversication, transparency and regulation. As will be outlined below, these public diversication vehicles are optimized from the perspective of individual investors. However, from the perspective of the startup issuer, they may not oer the perfect feature set.

2.5.3.1.1 Equity mutual funds and ETFs

Mutual funds and exchange-traded funds (ETFs) oer highly diversied investment exposure to public stock markets. By pooling capital with many investors, these diversication vehicles allow the individual investor to own a fraction of a larger, well-diversied portfolio. ETF and mutual funds are subject to comprehensive regulations, which are set forth in the Investment Company Act197 and the associated SEC rules.198 e compliance costs of these regulations are extensive. In addition to regulating equity issuances at the single issuer level, this introduces another rm layer with an additional set of regulations for the issuance of ‘fund shares’. us, fund vehicles typically need to reach a critical mass in order to operate eciently. Private startup investments by public market investors As outlined in more detail in chapter 1 of this thesis, the principle of ‘redeemability’ is deeply ingrained in the Investment Companies Act and, through multiple statutory provisions,199 eectively limits the ability of mutual funds to make substantial investments in private startups. us, for open-ended investment vehicles, the underlying assets need to be highly liquid for the most part. In the past decade,200 some of the larger open-end mutual fund providers, including rms like BlackRock, Fidelity, T.Rowe Price and Wellington, have started to invest in late stage, privately held technology startups.201 However, in the context of the entire mutual fund portfolios, these investments can only ever amount to a small percentage point given the short-term redemption obligations towards their investors. us, as a general rule, public diversication vehicles do not oer any substantial exposure to private startup investments. While founders may have incentives to tap into such crossover liquidity pools, these mutual crossover funds follow a passive investment strategy, tailored to investors seeking broad market exposure, rather than concentrated, high conviction equity strategies.

2.5.3.1.2 Pension funds

In contrast to mutual funds and ETFs, pension funds traditionally diversify savings across across all public and private asset classes. As part of their equity exposure, they invest and diversify across a wide range of public equities, as well

19715 U.S.C. §§ 80a-1 to 80a-64. 19817 C.F.R. §§ 270.0-1 to 270.60a-1. 199For example, Section 22(e) gives investors in open-ended funds a right to demand prompt redemption and compels such funds to make payment on the investor’s redemption request within seven days of receiving the request. See 15 U.S.C. § 80a–22 (e)). 200See Kwon, Lowry, and Qian(2020) (‘Between 1995 and 2000, less than 15 mutual funds had investments in private rms, compared to 96 in 2014. ere is a particularly large jump in 2011, which coincided with the economic recovery following the nancial crisis, suggesting increased demands for capital by companies, combined with a weak IPO market.’). 201See Schaefer(2015) (‘For the average investor, there are is a crop of easily-accessible mutual funds that oer a taste of exposure to hot tech startups while still being largely composed of more liquid public stocks.’); Wigglesworth(2020) (‘Much of Mr Ellenbogen’s success at T Rowe was thanks to a pioneering move to invest early into private, unlisted companies such as Twier, Grubhub and Warby Parker, something that had historically been o-limits to mutual funds. e practice is somewhat contentious. Stakes in private companies are hard to ooad, which entails risks for mutual funds that give investors the option of pulling money out whenever they want.’); Coren(2018) (‘As private markets have matured, the amount of capital has risen alongside the quality of investors. Pension funds and other asset managers began looking to startups for potential returns.e trend started with T. Rowe Price’s investments in Twier in 2009 and Workday in 2010 (whose valuations were $1 billion at $2 billion, respectively, just a fraction of today’s) is now mainstream. Other nancial rms like Fidelity and Morgan Stanley are pouring billions into growth-stage companies. Kulkarni says SharesPost tracks 85 mutual funds with shares in more than 200 private companies.’).

109 as illiquid private equity investments. Within the divide between public markets and venture rms, pension funds take a special, hybrid role, as they are both, major providers of capital to venture capital rms202, as well as active buy-side investors in public equity markets. From a regulatory perspective, pension funds operate outside the scope of security laws. ey are explicitly excluded from the denition of an investment company by section 3(c)(11) of the Investment Companies Act.203 Instead, they are regulated by specic rules and agencies, in particular the Department of Labor (DOL) under the Employee Retirement Income Security Act (ERISA).204 Given that pension funds themselves invest in public market diversication vehicles, including mutual funds and ETFs, this adds yet another intermediation layer to the diversication stack, raising the overall compliance costs even further. At the extreme, where a pension fund invests in an equity ETF, there are compliance costs (i) at the single equity issuer level, (ii) the ETF level and (iii) the pension fund levels. us, an equity transaction routed through such a ‘full stack’ diversication layer is subject to three dierent layers of securities regulation and supervision. Venture capital and startup investments Pension plans can be divided into dened-contribution (DC) and dened-benet (DB) pension plans. With respect to venture capital and startup investments, dened benet plans have a clear advantage in terms of diversication, as they can invest in both public and private assets. As a result, they are among the largest group of limited partners (LPs) in venture capital funds and sometimes also invest directly into late stage technology startups (increasingly so through co-investments oered by private fund managers). Pension funds typically invest only a small portion of the overall pension fund into the illiquid venture asset class.205 CalPERS, for example, manages the savings of California’s public employees through a dened benet scheme, with a total of $357bn of assets under management. ereof, $35bn are allocated to private equity and only $1bn of that is allocated to venture funds.206 However, over the past decades there has been a macro-level shi away from the allocation through DB plans to DC plans.207 Today, dened-contribution plans are the primary private savings vehicle.208 In terms of venture funds and the startup asset class, this means that many retirement savers no longer get any meaningful exposure. is is because dened-contribution plans, such as the most common 401(k) plans, only oer the investors a menu of mutual funds or similar public market investment vehicles which employees can choose from.209 From the above, it again appears that pension funds are best-tailored to passive retirement savers, which aim to hold the market portfolio and reduce idiosyncratic risk. It is not, however, tailored towards the interests of startups and founders.

202See Chemla(2004) (‘Pension funds have long been a major provider of capital to private equity funds. In 2001, over 50% of U.S. venture capital investments came from pension funds.’). 20315 U.S.C. § 80a–3(c)(11). 204Where the plan is sponsored by private sector employees the pension plans are regulated by the U.S. Department of Labor (DOL) under the Employee Retirement Income Security Act (ERISA), 29 U.S.C. §§ 1001-1461, or, where it involves a dened benet pension fund, by the Pension Benet Guaranty Corporation under Title IV of the ERISA, 29 U.S.C. §§ 1301-1311. Where the plan is sponsored by public sector employees, it is regulated by the Oce of Personnel Management (OPM) under the Civil Service Retirement Act (CSRA), 5 U.S.C. §§ 8331-8351. 205See Brophy and Guthner(1988) (‘See Institutional investors supply the bulk of the funds which are used by venture capital investment rms in nancing emerging growth companies. ese investors typically place their funds in a number of venture capital rms, thus achieving diversication across a range of investment philosophy, geography, management, industry, investment life cycle stage and type of security. Essentially, each institutional investor manages a “fund of funds,” aempting through the principles of portfolio theory to reduce the risk of participating in the venture capital business while retaining the up-side potential which was the original source of araction to the business.’). 206See Diamond(1984) (‘CalPERS doesn’t invest in major private companies that are yet to go public, such as Uber, like some other public pension plans have done. It does have a private equity portfolio worth about $26 billion, but most of it is devoted to buyout funds. Venture funds make up just $1 billion of the system’s private equity portfolio.’). 207See Zingales(2009) (‘In 1975, the value of privately held pension assets represented only 18 % of the gross domestic product (GDP) and 70% was represented by dened benet plans, which did not directly expose workers to nancial market risk; today, pension assets represent 60% of the GDP, 70% of which is in dened contribution plans and thus exposed to nancial market risk.’). 208See Ayres and Curtis(2015) (‘Participant-directed dened-contribution retirement plans are now the primary private savings vehicle for most Americans’ retirement. Dened contribution plans hold more than $4.4 trillion of workers’ retirement savings. e bulk of assets in these accounts is invested in professionally managed nancial products-mutual funds and similar structures-in which investors pool funds and pay a percentage of invested assets for professional portfolio management services.’) 209See R. Gilson and Kraakman(1984) (‘[…] of all recent developments in nancial economics, the ecient capital markets hypothesis (”ECMH”) has achieved the widest acceptance by the legal culture …the ECMH is now the context in which serious discussion of the regulation of nancial markets takes place.’).

110 2.5.3.1.3 Diversication under security laws

While the SEC may actively promote investor diversication,210 it does not actively force investors to diversify. us, investors in public markets can either invest in single issuer securities or in a diversication vehicle. However, while not explicitly requiring diversication, security laws set out certain investment restrictions and thresholds that are implicitly geared towards investor diversication.

• Accredited investor denition: With respect to the accredited investor denition, securities regulation requires an individual to have either an annual income of at least $200,000 per year ($300,000 with their spouse)211 or a net worth of at least $1 million.212 If these asset levels are fullled, individuals are free to invest in both startups and venture funds. e underlying assumption seems to be that these asset levels provide a cushion for individual investors such that, on a portfolio basis, they can ‘aord to lose’ some money.

• Crowdfunding investment caps: Similarly, under the JOBS Act Reg CF oering, annual investments under the regulation are capped at the greater of $2,000 or 5 percent of the annual income or net worth of such investor, if the annual income or the net worth of the investor is less than $100,000,213 or alternatively, 10 percent of the annual income or net worth of such investor, as applicable, not to exceed a maximum aggregate amount sold of $100,000, if either the annual income or net worth of the investor is equal to or more than $100,000.214

As such, the design of security laws seems to imply that investments in illiquid startups can be made by anyone, even retail investors, as long as they are made on an adequately diversied basis.

2.5.3.2 Costs of the rm allocation

Diversication under the rm allocation looks markedly dierent from diversication practices in the public markets. While diversication across the entire market is one of the principal objectives for public market diversication vehicles, the venture rm is known to expressly take highly concentrated bets. Venture funds typically make only between 15 to 30 initial investments in startups. VC funds’ diversication strategies are part of the private placement memorandums (PPM) that rms and GPs use when raising funds from potential limited partners.215 It has been argued in the literature that it does not make economic sense for venture rms to seek portfolio diversication at the fund level, given the high xed costs of venture rms to gain expertise in specic technology verticals.216 Importantly, over the lifetime of the fund, the venture rm becomes even less diversied. Norton and Tenenbaum report that about one of every 15 portfolio companies results in returns that are ten or more times above the venture capitalist’s initial investment costs (¿10x gMOIC, gross multiple of invested capital); the value of these few winners comprise about half of the ending value of the investor’s portfolio and almost two thirds of the fund’s performance.217 One reason for this increase in concentration is that funds invest in follow-on rounds through what is known as ‘reserve capital’.218 Fred Wilson, general partner at Union Squared Ventures (USV), describes the process as follows:219

210See SEC(2009) (‘e Magic of Diversication. e practice of spreading money among dierent investments to reduce risk is known as diversication. By picking the right group of investments, you may be able to limit your losses and reduce the uctuations of investment returns without sacricing too much potential gain.’). 21117 C.F.R. § 230.501(a)(6). 21217 C.F.R. § 230.501(a)(5). 21315 U.S.C. § 77d(a)(6)(B)(i). 21415 U.S.C. § 77d(a)(6)(B)(ii). 215See Buchner, Mohamed, and Schwienbacher(2017) (‘In practice, diversication strategies are part of the private placement memorandum that VC fund managers use to secure capital commitments of limited partners.’). 216See Norton and Tenenbaum(1993) (‘e assumption of complete diversication is not appropriate according to models that assume that some investors have cost advantages over others. From this perspective, venture capital rms will use their expertise to specialize in certain technical and product areas. Due to their information advantage in certain technologies or markets, and the high xed costs of gaining expertise in other technical and product areas, it does not make economic sense for venture capitalists to seek portfolio diversication.’). 217See Norton and Tenenbaum(1993) (‘About one of 15 investments results in returns that are ten or more times the venture capitalist’s investment; the value of these few winners comprise about 49.4% of the ending value of the investor’s portfolio and 61.4% of the investor’s prots.’) 218See Kupor(2019) (‘VCs tend to invest capital in startups earlier in the life of a fund, they also generally set aside “reserves,” expected monies that they anticipate they might invest in a startup over the course of its next several nancing rounds’). 219See Wilson(2017) (‘Reserves is the term VCs use to describe funds they “reserve” for follow-on nancings of their portfolio companies.’).

111 ‘One very important thing that separates a strong VC rm from all other sources of capital is that the best VC rms reserve capital for follow-on nancings for their portfolio companies and can be counted on to participate in subsequent nancing rounds. is is not true for angel investors, seed funds, growth funds, and strategic investors. [... ] Most top VCs will choose to take their “pro rata share” of follow-on rounds. at means they will invest enough capital to avoid being diluted by the follow-on nancing round. If a VC owns 15% of your company, they most likely are going to want to take 15% of follow-on rounds. at means that you can’t raise your next round from your VC investors, but you can count on them for a material part of the round.’

is high concentration in VC portfolios has been criticized by industry insiders in the past, such as Dave McClure, the founder of the ‘500 Startups’ accelerator program:220

‘Most VC funds are far too concentrated in a small number (<20–40) of companies. e industry would be beer served by doubling or tripling the average # of investments in a portfolio, particularly for early-stage investors where startup arition is even greater. If unicorns happen only 1–2% of the time, it logically follows that portfolio size should include a minimum of 50–100+ companies in order to have a reasonable shot at capturing these elusive and mythical creatures.’

However, from the perspective of the startup founder, this concentrated exposure of venture rms is quite welcome, as it ensures the commitment of the venture rm to the particular startup. As the venture funds puts in more and more capital into a particular startup, the interests of founders and the venture rm get more closely aligned over multiple nancing rounds. From the perspective of founders, this lack of diversication thus provides a clear benet of venture funding, as it both aligns incentives and ensures follow-on nancing capital (although oen an external lead is required). VC diversication under security laws As mentioned above, venture funds largely operate outside the realm of existing security laws, in particular they benet from the Reg D exemption. As such, there are no direct limitations placed on venture rms with respect to diversication under existing security laws. Instead, through covenants in the venture partnership agreements between limited partners and general partners, investors typically limit portfolio concentration on a contractual basis.221 However, the exemption under Section 203(l) of the Investment Advisers Act of 1940 (“Advisers Act”), also known as the venture capital exemption can be seen as an implicit diversication limit.222 In this set of exemption rules, security laws outline what they regard as a venture capital fund. Notably, the main limitation for the venture capital rm is set out in the second condition, which provides that:223

‘Immediately aer the acquisition of any asset, other than qualifying investments or short-term holdings, [the fund] holds no more than 20 percent of the of the fund’s aggregate capital contributions and uncalled commied capital in assets (other than short-term holdings) that are not qualifying investments.’

By creating these two baskets of qualifying investments (80 percent) and non-qualifying investments (up to 20 percent), the SEC limits the scope of investments a venture capital rm can make. First, the Advisers Act denes qualifying investments as equity investments and thereby excludes credit investments. However, the denition of ‘equity’ is a rather broad one, also including convertible debt. e laer is relevant, as convertible debt notes are the typical instrument used in earlier nancing rounds (pre-Seed and Seed rounds). Secondly, it requires that the investment must be made directly into a company and not acquired from a third party. In particular, this limits the fund’s ability to engage

220See McClure(2015) (‘[…] the basic argument remains: portfolios too highly-concentrated in small of companies risk missing out on ANY unicorns whatsoever.’). 221See Gompers and Lerner(1996) (analyzing 140 venture capital limited partnership agreements and nding that covenants restricting the size of investments in any one rm are increasingly found in partnership agreements, growing from c. 30% in the late 70ies o c. 80% in the early 90ies), more broadly on LPA covenants restricting portfolio composition see Kupor(2019) (‘is may sound obvious, but the LPA will dene the areas of investment for the GP and any hard-line restrictions on that. For example, is this a life sciences fund or a general information technology fund? Are there restrictions on stage – i.e., can the GP invest in seed- versus early-stage venture companies versus late-stage companies?’). 22217 C.F.R. § 275.203(l)-1. 22317 C.F.R. § 275.203(l)-1(a)(2).

112 in secondary market transactions, so-called ‘secondaries’, which have become a more popular mode of investing in technology startups in the past decade. While this limitation on the nature of the investments seems rather small, one of the leading venture rms in Silicon Valley, Andreessen Horowitz (a16z) has recently decided to become a registered investment advisor to get more exibility in this respect.224 However, generally speaking, the regulatory limitations on venture fund’s diversication levels are rather minute and inconsequential.

2.5.3.3 Comparative pricing

Again, we have to ask ourselves, how the costs of the market compare to those of the venture rm allocation. Under the market allocation, diversication entails that a new intermediation layer is introduced with its own market-enabling set of rms and its own set of regulations. e baseline costs of this intermediation layer can range from c. 1-1.5% for actively managed funds, to 0.3-0.9% for passive funds.225 While public market allocation has achieved high levels of diversication, almost reaching the theoretical limit of Markowitz’s ‘market portfolio’, this diversication layer clearly comes with its own set of regulatory costs under existing security laws. In particular, this diversication layer seems to be mainly tailored to the needs of investors, rather than issuers. On the other hand, the venture rm allocation oers low diversication levels. With the exception of a few broad restrictions imposed by the Investment Company Act, venture rms are known to take concentrated bets. ese bets get even more concentrated over time, oen times making the entire fate of a fund dependent on one or two investments, which should return both the fund, plus a positive return. From the perspective of the startup founders, this concentrated portfolio structure manages to align the interests between the VCs and the founders. Empirically, the costs of diversication under the venture rm allocation can be considered as a hard-to-quantify fraction of the 2/20 management fee and performance fee (carried interest) of venture rms.

2.6 Second part of the eorem

While the rst analytical step of the eorem focuses on the dierential regulatory pricing of the rm versus the market allocation, the second analytical step of the eorem hones in on the costs of the market. In particular, it re-conceptualizes the costs of the market as a negative externality. e costs include both baseline costs, namely the costs naturally occurring in the transaction, and regulatory costs, in particular the regulatory costs imposed on market transactions through securities regulation. is re-conceptualization allows us to analyze securities regulation under the seminal law and economics literature of Roland Coase and Guido Calabresi. In particular, under Coase’s seminal paper ‘Problem of Social Costs’, it is held that in the absence of any transactional frictions, the initial assignment of rights and obligations does not aect the eciency of the nal allocation.226 In the tort context, the Coase eorem thus holds that, whether the polluter or the neighbor is held liable for the costs of the negative externality does not aect the eciency of the nal allocation in a frictionless regime. In the context of equity markets, this means that as transactional costs approach zero, the legal assignments of rights and obligations to either the investor or the issuer under security laws has diminishing eects on the eciency of the nal outcome. For example, where relevant data about the issuer become freely and openly available at the disclosure layer, whether security laws mandate disclosures by the issuer or pass responsibility on investors to gather information does not aect the eciency of the nal allocation. Similarly, as the costs of diversication and maintaining an active primary and secondary market approach zero, the relevance of the implicit or explicit legal assignment of these costs by security laws diminishes.

224Contrary to this general industry practice, the venture capital rm Andreessen Horowitz has recently announced that it will abandon this exemption. See Konrad(2019) (More aggressively, they tell Forbes that they are registering their entire rm — a costly move requiring reviews of all 150 people — as a nancial advisor, renouncing Andreessen Horowitz’s status as a venture capital rm entirely.). 225See Guercio and Reuter(2014) (detailing expense ratio of 0.99-1.57% for actively managed funds and 0.37-0.86% for index funds). 226See Coase(1960) (‘It is necessary to know whether the damaging business is liable or not for damage caused since without the establishment of this initial delimitation of rights there can be no market transactions to transfer and recombine them. But the ultimate result (which maximises the value of production) is independent of the legal position if the pricing system is assumed to work without cost.’).

113 Since the frictionless regime of the Coase eorem is a stylized scenario, which does not necessarily reect economic reality, our concrete analysis of the equity market cost ‘externality’ below is guided by the work of Guido Calabresi, who holds in the seminal book ‘e Costs of Accidents’ that an optimization should (i) minimize the sum of the costs of the externality, including the costs of regulating the externality and (ii) an assignment of the costs of the externality to the most ecient cost-avoider.

2.6.1 Disclosure and information layer

2.6.1.1 Misinformation externality

At the disclosure and information layer, the relevant negative externality is misinformation. As outline in more detail in chapter 1, the misinformation externality results from the absence of information production or through false and misleading information production. us, information production costs constitute the relevant transaction costs related to this externality. e key insight of the second part of the eorem is that in the absence of transaction costs related to the market externalities, the legal assignment of rights and obligations under security laws does not aect the eciency of the nal allocation. e second part of this eorem thus holds that with transaction costs (information production costs) approaching zero, whether security laws assign (i) mandatory disclosure obligations on the issuer or (ii) data gathering obligations on the investor, does not aect the eciency of the nal allocation. In a positive transaction cost seing, however, the initial assignment of rights and obligations by the law does maer. In this respect, an optimal regime is one that minimizes the costs of the market and assigns them to the least cost avoider, the party that is best positioned to reduce the costs of the market. us, in the context of the disclosure layer, the objective is to reduce disclosure and information costs and assign these costs to the party that is best positioned to reduce them. In the below chapters, a (hypothetical) optimal regime is explored, which disaggregates the monolithic disclosure costs, such that it allows for a more nuanced assignment of costs to the least cost avoider.

2.6.1.2 Optimal regime

Since most startups fail, investing in startups at the early stage means looking for the outliers. is is why there is a lot of scepticism, even among professional investors, when it comes to the value of startup data and a data-driven investment approach.227 While a purely data-driven investment approach may not capture the many idiosyncrasies that need to be considered at the very early stage (pre-Seed and Seed), once a startup has seled into a given business model, progress and milestones can be measured eectively along a common set of metrics.228 In designing an optimal disclosure regime, one needs to reect on the heterogeneous business models,229 the available information sources, aggregation levels and investor types. In this section, an optimal disclosure and information regime under the eorem is proposed, which applies the blueprint of an optimal regime proposed in chapter 1 of this thesis. In particular, it proposes to ‘disentangle’ or ‘unbundle’ the production of raw data and information (ground truth data) from data processing and analysis:

• Ground truth data layer: refers to electronic, raw and unprocessed data sources, which the surplus agents can credibly rely on when making the investment decision. Such data sources can relate to nancial or alternative

227In my interview with Amplify Partners’ Partner Sarah Cantanzaro conducted as part of this PhD thesis, when asked about using data to guide investments she answered (at minute 9:59) ‘Given the limitation of startup data, I actually don’t think we can automate investing. I don’t think you can use data and machine intelligence to pick the right companies.’ Interview available under hps://channel.sandhillroad.io. 228In this vein see Patrick O’Shaughnessy (Producer)(2019a) (Alex Mial, founder of one of the largest angel group, FundersClub, reecting on this at minute 10:16 ‘One of the challenges of venture, especially early stage venture, one could very credibly say: ok, so if your whole job is to identify disruptive companies, ‘no one thinks this is possible companies’, how could you possibly have data that’s relevant, right? So that’s a very strong argument. And actually that’s the consensus argument. And what we’ve realized is, ok, that’s probably true I actually believe that also. But what if it’s simultaneously true that there’s certain business models in venture and tech startups that have been used over and over again where you actually potentially could have good data.’). 229See Patrick O’Shaughnessy (Producer)(2019a) (at minute 10:53 ‘And so we’ve thought about that and so for instance in enterprise, so companies selling to businesses there’s the SaaS model, soware-as-a-service subscription model. And so on the consumer side, you have consumer marketplaces, think Airbnb or Uber. And so what we’ve realized and we’ve funded hundreds of companies, we’ve seen literally thousands, and again and again certain business models are repeating.’).

114 business data.

• Data processing and analysis layer: data processing refers to the preparation of raw data for the end user. In particular, this can entail the aggregation of ground truth data entries into consolidated information sets. In contrast, data analysis refers to the use of processed data as an input for a wider range of calculations and model- based inferences, oen in combination with external data sets.

e goal is to conceptualize a hypothetical, cost-eective mode for dierent startup segments and verticals to disseminate relevant information to investors. Under the current design of security laws, disclosures oen rely on highly aggregated data that is reviewed and aested for by costly information or nancial intermediaries, such as accountants or securities lawyers, who sit between investors and startups. e emphasis thus lies on the data processing and analysis layer, where the bulk of costs are incurred in practice. What is ultimately presented to investors has oen gone through multiple rounds of human processing, review and aggregation. However, we know that such human processing is slow, biased and costly. Furthermore, these costs are shouldered primarily by issuers. us, the approach presented here explores how a clear separation of the data processing and analysis layer from the ground truth data layer would allow for a more nuanced cost assignment.

2.6.1.3 Optimal regime: ground truth data layer

Technology startups, being by denition small, early stage companies, are in many ways more ‘trivial’ than larger and more mature companies. ey have fewer employees and customers, lower revenues and fewer cost items. Unlike large multinational conglomerates, they do not have layers of management, complex corporate structures and multinational operations. is is what makes them innately beer suited for a disclosure regime that places greater emphasis on ground truth data disclosures. Before the advent of the computer and the internet, company records necessarily had to be stored locally on paper records. Large ling cabinets were the centerpiece of any corporate headquarter. e ability to make corporate ground truth data publicly accessible at low costs simply did not exist, given the technical hurdles of dissemination. However, signicant technological advances over the last decades, in particular the internet and cloud infrastructure, have increased a company’s ability to capture, process, and disseminate such ground truth information. Similarly, the market’s demand for real-time corporate disclosures has signicantly increased. Furthermore, technology startups tend to maintain the majority of their data fully digital, through a number of cloud-based services. Under the predominant rm allocation, this allows venture capital investors to quickly assess the viability of a startup investment by observing a range of service provider dashboards, such as the:

• Accounting so ware dashboards: for the assessment of a startup’s burn and runway (e.g. using ickbooks).

• Payment processor dashboards: for the assessment of a startup’s monthly recurring revenue (MRR) traction, cohort churn and lifetime customer value (LTV) (e.g. using Stripe).

• Server analytics dashboards: for the assessment of a startup’s user engagement and costs of customer acquisition (CAC) (e.g. using Google Analytics or Mixpanel).

• Version control dashboards: for the assessment of a startup’s product development and progress (e.g. using Github or Gitlab).

In other words, all the relevant data points are at the founder’s ngertips. e ground truth data is digitally stored and it can be both disseminated and independently veried at a fraction of its historical costs. As a result, making ground truth data pipelines a core piece of a modern mandatory disclosure regime seems both technically feasible and politically overdue. In an ideal seing, as already envisioned by former SEC Commissioner Wallman(1997) in the late 90ies, expensive and complex regulatory lings with the SEC could be replaced by linking a company’s cloud applications to the SEC’s electronic EDGAR system by way of a public API.

115 2.6.1.3.1 Traditional nancial disclosures

Under the current disclosure regime, startups going public primarily rely on aggregated nancial data, in particular on consolidated nancial reporting. In chapter 1 of this thesis, an optimal regime of a seamless ground truth data ow is presented in the nancial reporting context. In particular, based on the proposition of Wallman(1997), the option of disaggregated nancial data disclosures is explored. In such a ground truth disclosure regime, companies would be able to comply with their basic disclosure obligations, simply by linking a set of ground truth data sources to a regulatory API specied by the SEC. Such a disaggregated nancial disclosure regime would have a higher likelihood of success, particular in the sphere of technology startups, as these digitally native rms already rely on cloud-based electronic bookkeeping and performance tracking solutions. In particular, many Silicon Valley startups use Intuit’s ickbooks solution. is cloud-based accounting soware allows startups to digitally record their booking entries and produce the relevant nancial statements: balance sheet, income statement and cash ow statement. e soware solution is widely used to prepare reports for shareholders, creditors and tax lings. At the extreme, startups exposing their ground truth data would give investors access to a startup’s entire bookkeeping records through a publicly accessible investor API, linking e.g. to the startup’s ickbooks records. Investors would be given access to all accounting information in its entirety, with full granularity, in real-time and without any third- party accounting ‘cosmetics’. Under this full transparency scenario, investors would be able to review every booking entry for every corporate expense and income. However, such a full transparency regime would be daunting, not only for startup founders and their employees, but also for other company stakeholder, including vendors and customers. Surely, a requirement to have companies provide the market with full access to their accounting data would receive substantial pushback from startups and their stakeholders. us, such an ‘open organisation’ regime could be only introduced on a voluntary basis at rst, much like in the case of the SEC’s XBRL initiative.230 Under such a voluntary opt-in regime, startups could decide whether to provide traditional disclosures prepared with the help of lawyers and accountants, or alternatively provide access to ground truth data pipelines. e fact that at least a small sub-segment of startups may adapt to such a disclosure regime on a voluntary basis, is demonstrated by the ‘open startups’ initiative of the data analytics company Baremetrics. Under this initiative, a number of technology startups already today, chose to provide highly granular revenue data, all in the open and on a fully voluntary basis, with real-time, transaction-level revenue data, down to the individual $10 monthly subscription tickets.231 Alternatively, to induce startups to make more digital disclosures, a less permissive public investor API or data schemata could be set out by the SEC. In particular a data regime, which would only disclose data at given intervals and at pre-specied higher levels of granularity. Under such a solution, it would still be a fundamentally passive mode of disclosure, where the startup does not have to actively prepare and le data. Rather, the startups would link their existing cloud solution to the regulatory API interface.

Disclosure Data Data provider Financial Data ‘Raw’ accounting data ickbooks Revenue Paypal

2.6.1.3.2 Alternative data points for dierent startup industry verticals

Traditional nancial disclosures unarguably constitute the most relevant information when it comes to assessing mature companies with stable cash ows. Wallman(1997) has pointed to a wide range of non-nancial data sources and forward-looking information, including patent or customer cohort data.232 ese alternative data sources are partic-

230Until 2009, participation in the XBRL initiative was voluntary. See XBRL Voluntary Financial Reporting Program on the EDGAR System, Securities Act Release No. 8529, Exchange Act Release No. 51,129, Investment Company Act Release No. 26,747, 70 Fed. Reg. 6556 (Feb. 8, 2005) (codied at 17 C.F.R. parts 228-229, 232, 240, 249, 270). 231See Baremetrics(2020) (‘Welcome to the land of the brave. ese wonderful companies are embracing transparency and openness by sharing their metrics with everyone.’). 232See Wallman(1997) (‘In addition, and in particular, this shi would further our ability to convey, more easily, data that is increasingly viewed as critical to an understanding of knowledge-based companies. Such information might include nonnancial or forward-looking information, as well as

116 ularly important for startups. For many early stage startups, nancial metrics do not provide enough signal, as the company is still at a pre-product or pre-revenue stage. us, venture investors oen need to look for alternative metrics, when it comes to assessing value and growth potential. In other words, the crucial metrics that drive traction and growth are not found in the accounting soware, but rather in alternative dashboards. Under the existing securities regulations, these alternative metrics are typically discussed in the Management’s Discussion and Analysis (MD&A) section within the SEC lings, such as most notably the S-1 ling. Unlike for nancial disclosures, the management has substantially more discretion with respect to these metrics. e management can decide which specic business metrics to report and at which intervals. Given the crucial importance of these alternative metrics for the underlying business, an interesting idea to consider from a regulatory perspective would be to give early stage startups mandatory disclosure options that focus primarily on these metrics. In lieu of traditional nancial disclosures, which the startup may be more reluctant to provide, they could access the market by disclosing certain alternative growth metrics only. Surely, there would have to be clear limits on the amount that can be raised before traditional nancial disclosures would be required. Also, the catalogue of eligible alternative metrics would have to be pre-dened. However, such an alternative disclosure regime may induce founders to seek public market funding earlier, at a stage where the startup’s nancial metrics would not yet reect company’s traction and growth potential. Consumer startups Consumer startups come in many shapes and forms: from marketplaces, to direct-to-consumer (DTC) startups, to (social) media startups. e relevant metrics can vary widely depending on the vertical. For example, in the advertising- based social media and content vertical, metrics that reect user engagement, such as initial app downloads, daily or monthly active users (DAU or MAU) are generally considered to be the most relevant. ese metrics can be measured through user engagement analytics platforms, such as App Annie,233 Google Analytics, Alexa, Mixpanel or Similar Web.234 Reective of the importance of these metrics is that when public social networks, such as Facebook, Twier or Snap publish their quarterly results, the main aention of equity analysts typically lies on these user engagement metrics.235 is is because these metrics determine future ad revenues and are thus considered to be leading, rather than trailing indicators. In contrast, for consumer marketplace startups, metrics related to both the supply and demand side of the marketplace are generally considered to be the most important metrics.236 For the home-sharing marketplace startup Airbnb and the ride-sharing marketplace startup Uber, for example, the relevant metrics early on were the number of homes listed and the number of drivers available on the platform, as this determines marketplace liquidity on the supply side. Soware-as-a-service (SaaS) On the other hand, for the soware as a service (SaaS) startup business model, where soware is delivered through monthly recurring soware licenses, the focus typically lies on a dierent set of metrics.237 In particular, some of the information such as the number of patents obtained or their value or revenues generated relative to research and development expenditures, or the number of repeat customers among businesses in a particular industry.’). 233See Wales(2020) (venture capital investor at e.ventures, Brendan Wales, providing an App Annie screenshot that has been critical for the early identication of social network TikTok’s traction). 234See Hackernoon(2018) (Content startup ‘Hackernoon’ providing screenshots of their Google Analytics dashboard, their Alexa rank and Similar Web ranking in the course of their equity crowdfunding campaign ‘200,000+ daily visitors, 8,000,000+ monthly page views, our trac is ranked in the top 5K of all websites worldwide and in the top 3k of all US websites by Alexa.’). 235See Jiang(2019) (reporting that user engagement and retention metrics are the key focus of equity analysts covering publicly listed social media company Facebook ‘Facebook’s strong user metrics impressed analysts across Wall Street, prompting them to raise their price targets.’). 236See Jordan, Jin, Coolican, and Chen(2020) (venture capital investors at Andreessen Horowitz listing a number of relevant marketplace metrics used in their investment decision making, including the match rate, market depth, time to match, fragmentation of supply and demand, take rate, unit economics, prevalence of multi-tenanting, switching or multi-homing costs, user retention cohorts, core action retention cohorts, dollar retention and paid user retention cohorts, retention by location/geography and power user curves); Brasoveanu(2016) (venture capital investors at Accel listing a number of relevant marketplace metrics used in his investment decision making, including gross merchandise value (GMV), net revenue, gross margin, contribution margin, growth, market share, marketplace liquidity, average order value (AOV), user engagement, net promoter score (NPS), cohort retention, sector and geographic concentration, customer acquisition costs, channel scalability, life-time value (LTV) and burn rate); Patrick O’Shaughnessy (Producer)(2019a) (at minute 11:47 ‘And then on the consumer marketplace side, you know metrics like what’s the percent of organic acquisition vs. paid acquisition, LTV (customer lifetime value), CAC (customer acquisition cost), cohort retention.’). 237See Patrick O’Shaughnessy (Producer)(2019a) (at minute 11:11 ‘So breaking apart say like SaaS. What are example metrics or parameters that a SaaS company might have. ese are things like ACV, annual contract value, or retention or churn, did the customers stay with the company long term or do they leave. Or sales eciency, how ecient are the sales teams.’).

117 most relevant metrics of this vertical are the annual contract value (AVC), the costs of customer acquisition (CAC), customer lifetime value (LTV) and customer churn over time.238 ese metrics are typically analyzed over dierent customer cohorts. Since they typically relate to revenue and subscription contracts, they can be monitored through ground truth payment data, captured by the most commonly used SaaS payment processors, including Stripe, PayPal, Chargebee or Braintree (part of PayPal). Developer tools and commercial open source soware (COSS) Developer tools and commercial open source soware constitutes another startup vertical that is highly metric- driven. Developer tool startups develop fundamentally proprietary soware solutions or components used by larger enterprise customers and other startups. e growth of these startups crucially depends, besides market uptake, on the active and continuous development of their core soware components. Commercial open source startups monetize an open source soware projects through services, features or hosting. For this PhD thesis, the author has talked to multiple venture capitalists actively investing in this segment of the market, including Patrick Chase from Redpoint Ventures, Bryan Out from Index Ventures, Lee Edwards from Root Ven- tures and Joseph Jacks from OSS Capital.239 A further set of conversations involved a number of successful founders of breakout technology startups, including Sid Sijbrandij, co-founder of Gitlab (pre-IPO COSS version control startup having raised c. $440m in venture funding), Mahew Forniciari, co-founder of Gremlin (Series B stage developer tool/chaos engineering startup, having raised c. $26m, with Index leading their Series A and Redpoint leading their Series B) and Joe Doliner, co-founder of Pachyderm (Series A stage COSS big data startup, having raised c. $12m, with Benchmark leading their Series A).240 rough these interviews, the author has familiarized himself with the dierent business models and relevant metrics that venture investors track in this space. For both closed-source and open-source projects, development metrics, such as the number of commits made to a project are highly relevant. For open-source projects, the ‘stars’ on Github and the downloads through the package manager NPM are oen used by venture capital rms to assess the community engagement and growth potential of these projects.241

Technology vertical Data Data provider Consumer Web trac Google Analytics Daily active users (DAU) Similar Web App downloads App Annie Developer tools & Code commits Github Commercial open Code pull requests Gitlab source soware (COSS) Github stars NPM Soware-as-a-Service (SaaS) Monthly recurring revenue (MRR) Stripe Customer Churn Braintree Paypal

238See Tunguz(2019) (Tomasz Tonguz, general partner at Redpoint Ventures, providing an extensive spreadsheet with the most common SaaS metrics used to evaluate the viability of a SaaS startup); Insight Partners(2018) (Venture rm Insight Partners listing Customer Acquisition Cost (CAC), Customer Lifetime Value (CLTV), Conversion Rate, Customer Churn, Revenue Churn, Annual Recurring Revenue (ARR), Monthly Recurring Revenue (MRR), Burn Rate, Cumulative Cash Flow and Customer Engagement Score as the ten most relevant SaaS metrics). 239Parts of these interviews are available under hps://channel.sandhillroad.io. 240Parts of these interviews are available under hps://channel.sandhillroad.io. 241See P. Levine and Li(2020) (in the same vein, venture investors Peter Levine and Jennifer Li from Andreessen Horowitz ‘Project-community t, where your open source project creates a community of developers who actively contribute to the open source code base. is can be measured by GitHub stars, commits, pull requests or contributor growth. Product-market t, where your open source soware is adopted by users. is is measured by downloads and usage.’).

118 2.6.1.3.3 Ground truth decision making under the venture rm structure

As the above examples from notable venture rms, including Andreessen Horowitz,242 Redpoint,243 Insight Partners,244 Accel245 and e.ventures246 have demonstrated, venture investors increasingly rely on (ground truth) metrics to drive their investment decisions. One example of such a purely ground truth metric-driven investment under the venture rm allocation is oered by Jeremy Liew, partner at the venture capital rm Lightspeed Capital. In a podcast interview, he has recounted his decision process with respect to his investment in Snapchat. For this, he had to rely primarily on third-party data provider Flurry, as he personally fell outside of the consumer app’s target demographic:247

‘ere was no way that I could’ve organically understood what that engagement process looked like. What I could do was open up the Flurry analytics dashboard with Evan and look at the numbers and see 50 percent month-on-month growth and see engagement and retention metrics. ere were multiples of what we might expect from other companies. Something was working. It didn’t maer that I didn’t understand it right away. It didn’t maer that my intuition was bad.’

However, reliance on ground truth data is not only privy to the investment decisions of venture capital rms. Maybe even more so, it is used by large strategics, in particular tech companies, to sni out competition early on and identify potential acquisition targets. One notable example is Facebook’s data-driven investment policy, with a notable incidence relating to the period prior to Facebook’s acquisition of the messenger platform Whatsapp for $19bn in 2014. In that pre-acquisition period, the social media company turned out to have monitored the user behavior of millions of app users through data obtained through a Virtual Private Network (VPN) application provider, Onavo, which Facebook had acquired earlier in the year of the acquisition.248 is allowed the social network operator to track app usage in a very granular manner and identify Whatsapp as a potential competitor early on.

2.6.1.4 Optimal regime: data processing and analysis layer

e optimal regime proposed in chapter 1 of this thesis further looks at a separate data processing and analysis layer. Data processing refers to the preparation of raw data for end users. In particular, this can entail the aggregation of ground truth data entries into consolidated information sets. Wallman(1997) refers to this aggregation function as ‘compiling’ in the context of nancial statement preparation.249 In the context of technology startups, we can think about accounting rms aggregating individual accounting entries and third-party data providers compiling alternative data sources into comprehensive reports. Data analysis refers to the process of using processed data as an input for a wider range of calculations and model-based inferences, oen in combination with external data sets. In the sphere of technology oerings, we can think of the work of sell-side equity analyst using already aggregated and processed nancial data to make buy and sell recommendations. With respect to technology startups, the data processing and analysis layer typically involves these functions:

• Data processing: Assuming that the ground truth data feed would consist of raw, disaggregated accounting data, then the data processing layer can be understood as the aggregation layer that pieces together the individual accounting entries to generate the nancial statements, namely balance sheet, income statement and cash ow statement. e disaggregated accounting data could be fed through the API of an industry-standard accounting soware provider, such as ickbooks in the US. By disentangling the ground truth data feed from consolidated

242See Jordan et al.(2020) and P. Levine and Li(2020). 243See Tunguz(2019). 244See Insight Partners(2018). 245See Brasoveanu(2016). 246See Wales(2020). 247See Swisher(2017). 248See Bell(2018) (‘And when it comes to WhatsApp, the data shows just how important the acquisition was to the company, and why it earned a $19 billion price tag.’). 249See Wallman(1997) (‘By “compiling” then, I mean making data useful by taking data bits that are not useful in their raw form, tracking and aggregating the data by categories over time periods, and presenting the results of the compilation in accordance with a standard language—in this instance GAAP - to make them usable.’).

119 nancial statements, market participants would be put in the position to make a truly independent company valuation. For example, the granular data would allow them to assess and re-assess tricky accounting decisions, such as expensing or capitalizing the purchase of particular assets.250 is would enable investors to make a truly independent assessment, challenge accounting choices made under U.S. GAAP and arrive at a proprietary, true and fair representation of the business.

• Data analysis: Data analysis typically entails the qualitative assessment of the investment merits of a business, resulting in the assignment of a ‘buy’, ‘sell’ or ‘hold’ recommendation for the shares of a company. In the public markets, this function is traditionally carried out by sell-side equity analysts. In the aermath of the Dotcom bubble, equity analysts covering emerging technology companies became subject to intense scrutiny, given the conicted nature of some of the recommendations issued.251 e process of assigning a ‘buy’ or ‘sell’ recommendation can range from qualitative assessments, to constructing nancial statement ratios, to running more sophisticated statistical pricing models. e research of stock market analysts is paid for by investors, with investment banks historically bundling this research with other services.252

2.6.1.5 Least cost avoider

A strict separation of ground truth data from data processing and analysis allows us to identify a least cost avoider for each and assign the costs between issuer and investors in a more nuanced way. Under the least cost avoider regime proposed in chapter 1 of this thesis, it is held that the costs of producing ground truth data is most eciently assigned to the decit agents, while the costs of data processing and analysis would ideally be borne by creditors.

• Ground truth data: With respect to traditional nancial data, it is rather straightforward to see that startups are indeed the least cost avoiders for disaggregated company-level nancial accounting data. As this data is directly processed by the issuer for ordinary operations and other regulatory lings (such as taxes), the issuer is clearly the least cost avoider when it comes to providing access to this data. Similarly, for alternative data, startups use such alternative data points provided by third-party data providers in the course of growing their business. us, it makes sense that they are the most ecient cost avoiders.

• Data processing and analysis: As we have seen above, data processing is typically performed by the issuer with the help of specialized accounting rms, which are engaged and compensated for by the issuer. Under an optimal regime, where ground truth accounting data is made widely available, one could imagine that the issuer only provides a ‘basic version’ of consolidated nancial statements. As investors have access to granular nancial data of the issuer through a robust ground truth data feed, one could thus hypothetically imagine that investors could pay for a customized accounting layer.253 In the present market structure, the costs of data analysis on the other hand are already fully borne by investors. For example, the ‘buy’ and ‘sell’ recommendations provided by investment bank sell-side equity analysts are bundled into other bank services and thereby fully paid for by investors. 250See Wallman(1997) (‘As any one standard compilation system, such as GAAP, or international accounting standards, becomes just one of many that users may employ, the need for aestation will be drawn to the underlying data itself, and the means for preparing the elements of the database that make it useful to users when accessing the data.’). 251See Fisch and Sale(2003) (‘On August 1, 2001, another class of investors led suit against six brokerage rms for issuing misleading favorable recommendations and research reports on a variety of Internet securities. 45 e extent to which these suits will be successful remains an open question. On one hand, in July 2001, Merrill Lynch paid $400,000 to sele a case by a former client, alleging that he was duped by a Merrill Internet analyst.’). 252See Coee(1984) (‘Typically, securities research is reduced to an analyst’s report that is circulated among prominent institutional investors in return for expected future commissions or other investment banking business.’). 253See Wallman(1997) (In the same vein ‘Under this new approach, companies providing the database will have complied with their disclosure requirements, and the soware of users would then take over to provide customized nancial reports.’).

120 2.6.2 Investment and liquidity layer

2.6.2.1 Illiquidity externality

At the investment and liquidity layer, the relevant negative externality is illiquidity. As outlined in more detail in chapter 1 of this thesis, the illquidity externality results from the absence of potential buyers and sellers of the issuer’s securities, willing to provide bid and ask quotes in the market. us, liquidity provision costs constitute the relevant transaction costs of this externality. e key insight of the second part of the eorem is that in the absence of transaction costs related to the market externalities, the legal assignment of rights and obligations under security laws does not aect the eciency of the nal allocation. e second part of the eorem holds that with liquidity provision costs approaching zero, whether security laws require (i) the issuers or (ii) the investors to compensate such intermediaries does not aect the eciency of the nal allocation. In a positive transaction cost seing, however, the assignment of these costs by the law (whether explicitly or implicitly) does maer. In this respect, an optimal regime is one that minimizes the costs of the market and assigns them to the least cost avoider, the party that is best positioned to reduce the costs of the market. us, in the context of the investment and liquidity layer, the objective is to reduce liquidity provision costs both in the primary and secondary markets and assign these costs to the party best positioned to reduce them. In the below chapter, a (hypothetical) optimal regime for both primary markets and secondary markets is sketched out that could minimize liquidity provision costs and allow for a more nuanced assignment of costs to the least cost avoider.

2.6.2.2 Optimal regime

Dening an optimal regime for primary equity markets at the investment and liquidity layer is a daunting task. Primary markets are dominated by the underwriter model, which is essentially an allocation through a specialized broker-dealer rm structure, essentially a transitory rm allocation. Furthermore, the existing secondary market allocation of equity markets oers high liquidity levels, which may not fully reect the preferences of startup founders. Identifying the optimal regime is thus not only complex, but also highly exploratory in nature. However, given that the investment and liquidity layer is arguably the most expensive functional market layer and constitutes the very essence of the market, it deserves close aention.

2.6.2.2.1 Primary market

For the primary market, two separate optimal regimes are proposed. e rst one, direct market access, addresses the shortcomings of the existing underwriter model, while the second one, parametric pre-nancing order books, aims to mirror the pre-nancing advantages of the venture rm structure. Direct market access ‘Direct market access’ refers to the process of startups accessing equity markets directly without engaging an underwriter. Sjostrom(2001) describes how, already in the late 90 ies, companies would conduct so-called ‘internet direct public oerings’ (DPOs) by selling shares securities directly to the public without an underwriter. As early as 1984, before the rise of the internet, ice cream maker Ben & Jerry’s conducted one of the rst direct public oerings, raising $750,000 in the process.254 Anand(2003) reports for the early 2000s that Home Depot allowed its shareholders to purchase its shares online, while General Motors has permied shareholders to increase their shareholding by reinvesting their dividends in additional GM stock. Similarly, as discussed under part one of the eorem, the novel nancing routes oered under the JOBS Act, in particular Reg A+ and Reg CF oerings, work (mostly) through a ow of funds between shareholders and startups. A key challenge of internet direct public oerings is to align investors for a given share issuance. e best place to do this is at a marketplace where investors and issuers already interact on a daily basis, in other words a primary exchange

254See Cortese(2013) (‘Ahmadi’s lawyer, however, suggested an alternative, something known as a direct public oering, or D.P.O.’).

121 rather than the issuers webpage or a crowdfunding portal’s website. In fact, as a recent phenomenon, so-called direct listings of late stage startup issuers have successfully managed to circumvent the traditional underwriting process. Direct listing In 2018, as the rst of its kind, Spotify ‘went public’ in a direct listing, namely a listing without rm commitment underwriters.255 Notably, in these oerings, companies currently sell existing shares only, in particular founder, employee and early investor shares. us, they do not raise new company funds through a direct listing. However, the listing is completed without a transitory rm allocation through an investment banking underwriter. e argument that was provided by the NYSE president to explain the success of such a direct listing was mainly based on the lack of information frictions that existed between the public market investors and the company:

‘I’m not quick to jump to ‘hey if this is a success it’s a revolutionary overhaul of the IPO process [... ] Spotify is unique. ey’re not the only ones, but they are unique in the sense that they do have unlimited access to capital, they do have a worldwide brand and 70 million users. ere aren’t a whole lot of companies in the world that t that prole for whom this type of transaction is right in the sweet spot […]’

Similar to the Spotify model of cuing out the costly investment bank, the tech company Slack has entered the public markets in 2019256 through a direct listing and other companies, such as Airbnb have been rumored to enter the public market without an underwriter in 2020. e argument the NYSE president reects a general concern that direct listings may be limited to consumer soware or marketplace rms that are well known to retail investors. Namely companies where a broad enough segment of retail investors use the products on a daily basis and the company thus ‘needs no introduction’. is is reminiscent of the situation of many late eighteenth- and early nineteenth-century corporations described by Henry Hansmann and Mariana Pargendler where the ‘the principal shareholders were also the rm’s principal customers’.257 is was because ‘local merchants and farmers were apparently the most eective source of capital’ at the time. In other words, the argument is that the required pre-marketing of the securities oering, related to the company and its equity story, will be limited due to the prior notoriety of the company and its products among public market participants. Direct oering So far, direct listings on the NYSE have been limited to the sales of existing shares of investors and employees. It is important to note at this point that the current success model of direct listings crucially relies on the existence of a well-capitalized venture capital industry. is industry capitalizes startups through the rm structure, making it unnecessary for the startups to raise additional money to the balance sheet at the point of entering the public markets. Slack reported in their S-1 that the company held $841m in cash reserves, which were raised through multiple venture funding rounds.258 As such, the public market is seen purely as an exit and liquidity route. us, the current direct listing model is in part a result of the dominance of the venture rm. However, an optimal regime would make capital markets an integral part of a startup’s fundraising, in particular by allowing them to access markets directly through the primary sales of new shares. In fact, the NYSE has led a proposal259 for a rule change with the SEC in late November 2019. However, the SEC has rejected this proposal shortly thereaer,260 making a direct oering of primary shares impossible under the existing

255See Nickerson(2019) (‘In April 2018, music streaminggiant Spotify disrupted the traditionalinitial public oering model and became a publicly traded company through a novel process known asa directlisting.EschewingstandardWall Streetpractice,Spotify did not raise new money through the oering and instead simply made its existing shares available for purchase by the public.’); Castillo(2018) (‘Spotify’s direct listing is dierent from IPOs because the company is both listing and oering shares at the same time without banks’ help.’). 256See, Form S-1 Registration Statement under the Securities Act of 1933 for Slack Technologies Inc, Securities and Exchange Comission, hps://www.sec.gov/Archives/edgar/data/ 1764925/000162828019004786/slacks-1.htm 257See Hansmann and Pargendler(2014) (‘In many corporations of the time, the principal shareholders were also the rm’s principal customers. ese customers were the owners of businesses -farmers, merchants, and manufacturers. And the corporations were commonly providing infrastructural goods and services that were critical for the success of those local businesses.’). 258See, Form S-1 Registration Statement under the Securities Act of 1933 for Slack Technologies Inc, Securities and Exchange Comission, hps://www.sec.gov/Archives/edgar/data/ 1764925/000162828019004786/slacks-1.htm 259NYSE, Proposal to amend Chapter One of the Listed Company Manual to modify the provisions relating to direct listings, hps://www.nyse.com/publicdocs/nyse/markets/nyse/rule-lings/lings/2019/SR-NYSE-2019-67.pdf 260See Osipvich(2019) (‘e Securities and Exchange Commission rejected a proposal from the New York Stock Exchange to create a new type of direct listing that would let companies raise capital. e agency’s move throws a wrench into NYSE’s plans to create a new alternative to traditional initial public oerings.’).

122 stock exchange rules of the NYSE. Pre-nancing vehicles Under pre-nancing, the matching of present startup capital supply with future capital demand is understood. is goes even one step further than providing direct market access in the spot market through a direct listing, allowing agents to allocate startup equity over time. Pre-nancing is a key advantage of the venture rm allocation. Under the venture rm allocation, this is what limited partners do when they commit funds to a venture rm. e venture rm ‘raises funds’ from LPs in advance on a blind pool basis and, once a suitable startup investment has been identied, the fund can almost instantly deploy the funds. In other words, there exists an implicit pre-raise. is ensures a commied pool of funds and speed-of-execution. Forward-seling parametric order book Given the advantages of pre-raise vehicles, under the optimal regime, it is therefore suggested to provide for a directly accessible, forward seling, parametric order book for startups to mimic this pre-nancing property of the venture rm in the equity markets. ese forward markets could specify generic startup metrics on a parametric basis, such as the metrics discussed under the disclosure section. Investors would buy shares in these startups on a forward, blind pool basis, much like LPs in VC funds are typically nancing future startups. Parametric basis refers to pre-specied nancial or alternative metrics for startups. ese could include nancial statement gures and ratios, such as startup revenue, gross prots, or alternative growth metrics, such daily active users, customer acquisition cost (CAC) or customer lifetime value (LTV). In an optimal open order book infrastructure, startups could access these pre-nancing markets directly, originating startup equity directly against specic metric pools. Blank check or special purpose acquisition corporations (SPAC) While pre-nancing vehicles in the manner suggested by above do not exist at the present time, there have recently been aempts to use blank check companies, also known as special purpose acquisition corporations (SPAC), in the realm of late stage startup nancing. In particular, these are pre-nancing vehicles on a single deal basis, as SPACs can typically only execute on one major acquisition of a target company. In particular, in 2017, the venture capital rm Social Capital completed an initial public oering (IPO) for a $600 million holding company through which it intended to ‘take startups public without an IPO’.261 e founder of Social Capital, Chamath Palihapitiya, holds the opinion that the US IPO process is fundamentally broken.262 Against this background, this rst holding company was an aempt at creating a market-based solution for curing the present decits of securities regulation through what he referred to at the time as an ‘IPO 2.0’. e idea was to provide the public market investors with access to private startups typically nanced through the venture capital rm structure, while at the same time relieving these startup rms from the costs encountered at the disclosure layer during the traditional going public process. Rather, all regulatory costs would be consolidated and ‘pre-complied with’ at the holding level. At rst, this experiment seemed to have failed in reaching its original mission: instead of taking a late stage technology startup private, Chamath’s rst SPAC ended up acquiring Virgin Galactic from Richard Branson’s Virgin Group. However, in the meantime, Chamath has raised multiple successor SPACs, which have indeed acquired late stage venture-backed startups, incl. Opendoor, Clover Health and SoFi. In addition, Chamath has become the ‘Pied Piper’ for a broad SPAC movement, which in 2020 saw a sudden spike of 248 SPAC IPOs, raising $83.3bn in the process. Optimal regulation Under the optimal regime discussed above, a market allocation of startup equity would be enabled without traditional investment bank underwriting. While this poses both regulatory and technical challenges, it would move the market microstructure in primary startup equity markets closer to a stylized frictionless ‘Coasean’ seing. To sum up the above solutions, the optimal regulation regime would rstly enable direct market access avenues in the spot markets. From a securities law perspective, the current direct listing model seems to provide a good model. However,

261See Roof(2017c) (‘Silicon Valley venture rm Social Capital just completed the rst step in its mission to take startups public without an IPO.e team has listed something called a special purchase acquisition company, known as a SPAC. More common outside of the tech industry, these blank- check companies are specically designed to buy private companies and bring them public without going through the IPO process.’). 262See Kokalitcheva(2017) (‘this whole [IPO] process sucks’).

123 it is currently only possible to list secondary shares. As outlined above, a fully direct market through a primary direct listing will require further changes to NYSE listing rule, which have not yet been approved by the SEC. Furthermore, given that under the direct market access model, rms would need to spend more time and resources self-marketing the oering, the rules with respect to the communication between issuer and investors would need to be further updated. is would bear similarirty to the Securities Oering Reform in 2005, which made alternative ‘wrien communication’ forms, such as video conferences admissable.263 Secondly, the pre-nancing parametric order book market would require specic exemptions under existing security laws. In particular, given the nature of pre-nancing, this is because the identity of the startups is not specied at the time exposure is sold. Instead, only certain metric-based startup parameters would be pre-specied, which would require a sui generis regulation under existing security laws.

2.6.2.2.2 Secondary market

Given the rise of many alternative liquidity pools through which order ow in equity markets is routed today, namely electronic communication networks (ECNs), alternative trading systems (ATS) and ‘dark pools’, it seems tempting to suggest a frictionless high frequency liquidity regime for secondary markets. However, from the analysis provided under the rst part of the eorem, it appears that more liquidity is not what startups require. Instead, quite the opposite seems to be the case. Founders of late stage technology startups have long complained about the short-term perspective that comes with being a public company.264 Startups need the ability to correct, pivot and experiment in a quieter ‘playground’ that shields them from the rigidities of the constant price mechanism. Long Term Stock Exchange (LTSE) An interesting case study with respect to this ‘secondary market puzzle’ of startup nancing is provided by the Long Term Stock Exchange, which has also been termed the ‘Silicon Valley stock exchange’. e exchange is the brainchild of Eric Ries, who has famously started the lean startup movement with his book ‘e Lean Startup”.265 In this book, he has set out a methodology for building and iterating on technology products in a capital ecient manner. e Long Term Stock Exchange is backed in part by Marc Andreessen, one of Silicon Valley’s most vocal critic of public markets (see section 2.5.1.1.1). With the exchange, Ries strives to enable late stage startups to operate in a quieter public market environment. As Eric Ries puts it, the goal is to allow management ‘to spend more of their energy focusing on serving customers, less on the kind of distractions that cause a lot of value to be destroyed in today’s markets’.266 e LTSE has received SEC approval267 as the 14th SEC-registered national securities exchange in summer 2019 and has raised c. $50m of new funding in August 2019.268 e core features through which the LTSE wants to achieve the goal of building a quieter secondary market is through novel stock market rules, which allow for the following:269

• Tenured shareholder voting power: shareholder’s votes will be proportionately weighted by the length of time the shares have been held;

• Long-term management incentives: by linking executive pay to long-term business performance;

• Additional two-way disclosure and information requirements: which would inform startups about the identity of their long-term shareholders and, on the other hand, inform investors about the investments the company is making.

263Final Rule: Securities Oering Reform, Release No. 33-8591 (July 19, 2005) (“Adopting Release” ). 264See Waters and Foley(2015) (Ken Lin, the co-founder of Credit Karma, which never went public and was eventually acquire by Intuit in Q1 2020 for $7.1bn “ere is a lot more you can do as a private company,” says Mr Lin, echoing the views of a generation of tech entrepreneurs who have learnt to shun the public stock market for as long as they can. […] “In public markets, you can lose the long-term view to focus on the short term,” says Mr Lin. And with investors lining up to pour cash into companies like Credit Karma, which has sucked in nearly $370m so far, the pressure to conform to Wall Street is o. “You can still raise the dollars in relatively short order, and still have the benets of being a private company.”). 265See Ries(2011). 266See Kevin(2019). 267See SEC(2019b) 268See SEC(2019c). 269See Kevin(2019).

124 e reception of these proposals have been mixed so far, with the Council of Institutional Investors arguing, for example, that the LTSE’s voting mechanism could hurt shareholders by giving too much power to founders.270 Enabling a quieter secondary market While the proposals of the LTSE oer a welcome novel approach, it remains questionable whether they will be able to achieve the intended goal of building a quieter secondary market. In particular, it is likely, as Eric Ries openly admits, that rms will initially dual-list on other exchanges as dominant incumbent exchanges, such as the NYSE and the Nasdaq, oer access to larger liquidity pools. Such dual listings will limit the eects of the LTSE in terms of providing ‘calmer capital’. It thus appears that solving the ‘secondary market puzzle’ of startup nancing could be more optimally solved by other means. At the extreme, secondary market liquidity could be fully restricted for certain timeframes, as is the case for the rst year of Reg CF oerings. However, as full resale restriction may seem overly prohibitive, another model could be to have a model of ‘phased in’ liquidity, whereby shares can initially only be sold in semi-annually intervals, and over time, these intervals are shortened as the startup matures. Optimal regulation From the above, it appears that solving the secondary market problem of startups is a rather complex one, requiring further research. While the LTSE model of tenured shareholder voting, which incentives shareholders to hold startup shares for a longer timeframe, seems like an interesting market-based approach, it is questionable whether listing rules are the best legal mechanism for solving the problem. e alternative proposed herein, namely to impose resale restrictions and to ‘phase in’ liquidity over time through specialized startup oerings in the model of the JOBS Act, would be a more invasive option.

2.6.2.3 Least cost avoider

Primary market Under the underwriter model, the startup going public typically pays an underwriting fee. rough a rm commitment underwriting, the issuer receives price certainty for a set underwriting fee and an underpricing discount. e costs incurred through both the underwriting fee and the underpricing can be substantial, sometimes a high percentage of the total issuance volume. In the optimal models discussed above, in particular the direct market access and pre-nancing models, there would be no such fees, instead there would be access fees for the auction or order book infrastructures. Under the existing direct listing model, startup issuers face a lot of uncertainty prior to the oering. e same could be expected for the direct market access model. It is also not clear whether the startup issuer is indeed the least cost avoider for this price uncertainty. As mentioned above, under the underwriter model, the startup essentially pays for price certainty. In the proposed direct market access model and the pre-nancing model, the price uncertainty could potentially be passed to investors. In particular, under the direct access model, issuers could dene a minimum price acceptable to them through an auction mechanism to that eect. is would in many ways be similar to the minimum fundraising target set under the existing JOBS Act oerings under Reg A+ and Reg CF. On the other hand, pre-nancing order books naturally ‘pre-price’ the issuance. Secondary market Traditionally, the costs of regulated public secondary markets, comprising exchange listing and market maker fees, are borne by equity issuers. In equity markets, such secondary market costs can be prohibitively large for startups. Given that the main beneciaries of secondary market liquidity are indeed investors and not startups, it appears that these costs would optimally be assigned to investors instead. Given that a large part of the order ow in equity markets is today routed through OTC liquidity pools outside of regulated national securities exchanges, it appears that obtaining a primary listing on either Nasdaq or the NYSE is increasingly a costly signal. Under an optimal secondary market regime, with a more fragmented number of liquidity platforms and secondary market liquidity providers, the costs could instead be assigned among a wider number of agents. In particular, in

270See Council of Institutional Investors(2019) (‘e structure is likely to disproportionately empower founders/managers who have substantial stakes from IPO, and who sometimes fall victim to myopia or conicted behavior that can destroy value.’).

125 electronic central limit order book markets, liquidity providers (limit orders) are compensated by liquidity takers (market orders), thus allocating the costs of the market eciently among the investors.

2.6.3 Diversication layer

2.6.3.1 Misallocation externality

At the diversication layer, the relevant negative externality is misallocation. As outlined in more detail in chaper 1 of thesis, the misallocation externality can be understood as excessive idiosyncratic risk exposure in the absence of suitable pooling partners. us, pooling costs constitute the relevant transaction costs related to this externality. e misallocation externality can be further divided into two types:

• Market-induced misallocation: this misallocation results from the structure and costs of the market, in particular from frictions at the disclosure and investment layer, which limit the exposure to non-public, private assets. In the sphere of technology rms, this misallocation may result from an underallocation to privately-held startups that defer their going public process.

• Firm-induced misallocation: this misallocation results from the dominance of the rm, which nances assets through the rm-structure that could otherwise be nanced through the market. In the sphere of startups, this relates to the venture rm. As a result, the investor’s equity portfolio is underweight with respect to venture- nanced startup rms.

e key insight of the second part of the eorem is that in the absence of transaction costs related to the market externalities, the legal assignment of rights and obligations under security laws does not aect the eciency of the nal allocation. e second part of this eorem thus holds that, with transaction costs (pooling costs) approaching zero, whether security laws require (i) the issuer (ii) or the investor to bear the costs of pooling, does not aect the eciency of the nal allocation. In a positive transaction cost seing, however, the assignment of these costs by the law, whether through explicit or implicit assignment, does aect the nal outcome. In this respect, an optimal regime is one that minimizes the costs of the market and assigns them to the least cost avoider, the party that is best positioned to reduce the costs of the market. In the context of the diversication layer, the objective is to reduce pooling costs and assign these costs to the party best positioned to reduce them. In the below chapter, such an optimal regime is sketched out with respect to both market-induced misallocation and rm-induced misallocation costs in the sphere of technology startups.

2.6.3.2 Optimal regime

So far, we have analyzed a number of key challenges related to the disclosure and investment layer when it comes to ‘luring’ technology startups back into the public markets. However, the diversication layer is quite interesting in this regard, as public investment vehicles could overcome many of the woes encountered at the other layers. By being able to give retail investors access to shares in private companies, they could in a sense leapfrog over many of the hurdles at the disclosure and investment layer and still end up with adequate exposure to the startup asset class. Like for xed-income markets, where illiquid private credit of smaller issuers is bundled into pooled securities, startup equity of emerging companies could be bundled into larger pools. Given that trading ‘thickens’ at the pool level, such vehicles may be able to oer higher levels of liquidity.271 While this may seem like a very elegant ‘quick x’ solution, it should be noted that it does not solve more fundamental problems encountered at the lower layers. In other words, public market access through the diversication layer is not a substitute for a ‘pure play’ market allocation.

271See Ben-David et al.(2018) (empirically analyzing and comparing the liquidity of ETFs and basket securities and nding that ‘Along all three dimensions, the average ETF is signicantly more liquid than its basket stocks. In particular, the bid-ask spread is lower by about 20 bps.).

126 2.6.3.2.1 Market-induced misallocation

Under this type of misallocation, the investor’s portfolio is underweight with respect to equity in privately held companies. In other words, the exists a delta between the invested equity assets, n, and the full universe of equity assets, N, which would be comprised in a mean-variance ecient market portfolio in the sense of Markowitz.272 Brophy and Guthner(1988) have empirically identied this market-induced misallocation by looking at a subsample of 12 publicly traded venture capital rms and nding that the venture capital funds demonstrated very low beta coecients and covariance of returns among portfolio components when compared with portfolios of mutual funds. In an ideal seing, one would have public diversication vehicles, which would invest in specic funding stages and industry verticals. us, one would have dedicated pooling vehicles for early stage (Seed to Series A), late stage (Series B to C) and growth stage startups (Series D+) or dierent industry verticals, such as consumer tech, soware-as-a-service (SaaS), cloud applications and developer tools. To enable such exposure at the diversication layer, security laws need to provide for eective diversication vehicles that allow investors to pool resources and allocate them in such private companies. As outlined in chapter 1 of this thesis, and the rst part of the eorem, open-ended fund structures, such as mutual funds and exchange-traded funds (ETFs) are subject to strict liquidity restrictions and redeemability requirements under existing security laws, which largely restricts them from investing in privately-held technology startups. While some of the large open-end mutual funds, so-called ‘crossover funds’,273 have started to invest in late-stage, privately held startups, they have done so only at very low levels in the context of their entire portfolios. Overall, open-ended fund structures thus appear ill-equipped to deal with the allocation of startup investments. In contrast, closed-end investment funds are not subject to the same set of rigid redeemability and liquidity requirements and can therefore aord to make substantial investments in privately held startups. Sharepost 100 Fund ere currently exists one notable ‘case study’ with respect to such public diversication vehicles: the ‘Sharepost 100 Fund’.274 is fund is a Delaware Trust registered under the Investment Company Act of 1940, as a ‘non-diversied’, closed-end, management investment company that is operated as an interval fund. e fund’s portfolio investments include many late stage Silicon Valley ‘unicorn’ startups, including Nextdoor, Lime, Palantir, SoFi, Robinhood and many others. It appears that the shares acquired by the fund structure are primary secondary sales of early employees of these startups. As an interval fund, the fund oers to repurchase the shares in the closed-end vehicle on a quarterly basis from investors. Optimal regulation As the Sharepost 100 example above demonstrates, existing security laws already allow for public closed-end diversication vehicles to invest in private technology startups in a diversied manner. However, both from a disclosure and a liquidity perspective, the existing closed-end fund structures are well tailored to technology startups. Firstly, with respect to disclosure, the quarterly fund disclosures require the closed-end funds to mark the startup shares to market. is may not be in the interest of either startup founders or venture funds, who do not want the public vehicles interference with private startup valuations. For example, one could imagine a scenario where a startup is in the process of raising a private ‘up round’, while the startup’s shares trade at a discount of the prior round. Because of this forced market transparency, such pooling vehicles will receive substantial pushback from founders and early stage investors. In particular, given that the ‘supply side’ of this particular fund’s private company share are the startup’s employees, rather than the startups themselves, it is questionable how scalable the model will be without the support of the startup company, in particular its founders and lead investors. To accommodate such legitimate concerns, the disclosure requirements could be adjusted, such that marking-to-market can track private valuations. Secondly, with respect to liquidity, it appears

272See Markowitz(1952) and Markowitz(1959). 273See Schaefer(2015) (‘For the average investor, there are is a crop of easily-accessible mutual funds that oer a taste of exposure to hot tech startups while still being largely composed of more liquid public stocks.’). 274See Sharepost(2019a) (‘e SharesPost 100 is a list of 100 private, operating, late-stage, growth companies, primarily in the technology sectors, selected and maintained by the Investment Adviser according to several criteria, including revenue growth, market potential, product stage, management team, investor composition and level of nancing and trading activity on alternative trading systems and other private secondary markets.’).

127 that quarterly re-purchases by the interval fund vehicle are currently the main mode for investors to get liquidity. Ide- ally, as such diversication vehicles would grow in sizer, there could be enough inter-investor liquidity to allow for an active secondary market at the fund level. From a regulatory standpoint, this could mean that such closed-end funds are being restricted from operating as interval funds, once they surpass a certain fund size.

2.6.3.2.2 Firm-induced misallocation

Firm-induced misallocation arises from the dominance of the venture rm when it comes to nancing startups, in particular due to the preference of founders for private venture nancing. e problem here is the ‘access’ to investment exposure, rather than the concentration of exposure. Access to high quality deal ow is a practical hurdle, which is substantial in the sphere of early stage technology startups. ‘Access’ can further be divided into (i) the ability to identify high-quality investment opportunities and (ii) the ability to gain an allocation in a nancing round and thus a spot on the cap table of such high-quality startups. In many ways, establishing and executing on this ‘access’ demarcates the essence of a venture fund’s economic activity. Venture capitalists are known to to spend much of their resources trying to identify future breakout companies, and once identied competing ercely for an allocation in highly competitive rounds, oen courting promising founders and companies well before they seek funding275 and as ‘lead investors’ in a round oen pushing out smaller investors, in particular angels.276 To gain access to high quality venture deal ow, an optimal regime is sketched out, which provides for public market diversication vehicles, placed at strategic entry points next to incumbent gatekeepers or at the startup level. Again, the Sharepost 100 Fund example above is illustrative, as the fund structure is strategically positioned adjacent to the ‘Sharepost’ secondary market for private startup shares, where early employees of breakout startups are actively invited to tender their shares.277 Venture capital rm An optimal point of entry for public diversication vehicles would be at the level of the existing venture capital industry. In particular, one could imagine large public fund-of-fund vehicles that co-invest alongside existing venture funds. Whenever a venture rm would raise a new private fund through the traditional limited partnership structure, security laws could provide an option (or obligation) for the venture rm to oer a public sidecar vehicle that co- invests alongside it. is sidecar vehicle could be structured as a closed-end fund-of-fund (FoF) vehicle, which would be accessible to a broad base of retail investors.278 e regulatory footing of such a sidecar vehicle could be provided for under a carve-out under the Investment Company Act 1940. From the investors perspective, the benets would be that the specialized nature of venture rms would ensure that the public sidecar vehicles would receive early exposure to the most promising startup deal ow. From the perspective of the venture industry, there may also be a distinct advantage of such public sidecars. By having access to public markets, some early venture funds279 may be able to raise larger funds than they could ordinarily raise in the private sphere.280

275See McBride(2014) (Describing how Sequoia’s GP Jim Goetz was actively pursuing the WhatsApp deal ‘Founders say that Sequoia, based on Silicon Valley’s legendary Sand Hill Road, is adept at competing with other venture capitalists when a startup is seeking funds. It also excels at courting promising companies that believe they don’t need cash and persuading them to take it - like WhatsApp, whose founders weren’t actively seeking funding. “e notion of them marching up and down Sand Hill with a Powerpoint deck is comical,” said Goetz, referring to WhatsApp founders Jan Koum and Brian Acton. He had cultivated them since 2010 before closing Sequoia’s rst investment the following year.’); Grith(2020) (with respect to one of a more recent competitive round in cloud-based collaboration tool Notion where multiple leading venture funds have been rumoured to have courted founders for a long time‘In a moment of uncertainty, Mr. Kothari said, “nancing is a signal of stability, which is important to us.” So they called Sarah Cannon, an investor at Index Ventures who had been courting Notion for over a year.’). 276See Calacanis(2017) (‘As an angel investor, you will sometimes have an opportunity to invest in a Series A, but not oen. Why? Because VCs are greedy and have huge sacks of chips. When they gure out that a startup deserves $5 million or $10 million in funding and a $12 million or $25 million valuation, they are going to slurp up every share they can nd. In fact, sometimes the “lead investor” on a Series A will have the right to approve the other investors! If you’re geing into a Series A alongside a powerful VC, you either have a great relationship with the founders, who are demanding you get an allocation, or you provide massive value and the VC thinks that you working in their company will increase everyone’s share value.’). 277See Sharepost(2019b) (‘Sell your private company shares with help from our Private Securities Specialists — licensed brokers with years of experience.’). 278See Brophy and Guthner(1988) (outlining the benets of a fund-of-fund structure in the venture class ‘ese results demonstrate to investors the magnitude of the dierences in risk adjusted total return between publicly traded venture capital funds and growth oriented mutual funds on an individual fund basis. ey also demonstrate to investors the power of the “fund of funds” approach to institutional involvement in the venture capital business.’). 279Although not the top tier rms, which are oen oversubscribed. 280See Brophy and Guthner(1988) (in the same vein ‘Because such an approach produces beer risk adjusted investment results for the institutional

128 However, this being said, there may also be some pushback from the venture industry. Firstly, it would depreciate the value of the private fund oering, as the public co-investment sidecar funds would provide for passi paru exposure and thereby dilute private fund LPs. Also, given that such a dual-fund structure would require venture rms to manage both the agship private funds and co-manage public sidecar fund vehicles, there may be pressure on management and performance fees. In particular, for top tier early stage funds, such as Benchmark,281 which intentionally do not want to grow their funds and average check size, such dual structures may be perceived as depreciating the exclusivity of their agship fund oering. Startups Another potential point of entry would be at the level of the startup. Here, one could specify that whenever a startup raises venture funding from one venture rm of a pre-specied catalogue of select venture rms, the startup has the option (or alternatively obligation) to receive funding from a public diversication vehicle. From the perspective of a founder this may be an interesting way to increase the size of a traditional venture funding round.282 However, such a solution is likely to receive a lot of pushback from the venture industry, as many VC funds have minimum ownership demands and the existence of competition by public diversication vehicles might thus interfere with their investment strategy. Optimal regulation I have outlined above the blueprint for strategically placing diversication vehicles at dierent strategic entry points, either at the venture rm or the startup level. Neither one of these two solutions seem perfect though: if placed at the venture rm, diversication vehicles face an additional intermediation layer, if placed at the startup level, diversication vehicles have to compete heads on with the venture rm. Yet, compared to the case in which diversication vehicles would invest directly, the above solutions appear more likely to provide such diversication vehicles with access to high quality startups.

2.6.3.3 Least cost avoider

Besides exploring the optimal diversication regime, the eorem developed in chapter 1 of this thesis looks to identify the least cost avoider. In other words, we try to establish which party to the market transaction, the investor, the issuer or an intermediary, can minimize the ‘misallocation externality’ most eciently. Put more concretely, we analyze whether retail investors should pay for investment pooling services, as they currently do, or whether these costs could (and should) be passed on to startups or venture rms instead? e analysis of the least cost avoider aims to determine which side of the marketplace should optimally bear the costs, such that securities regulation can reect this and optimally assign the costs to the respective party. For this purpose, we can again dististinguish between market-induced and rm-induced misallocation.

2.6.3.3.1 Market-induced misallocation

As held in chapter 1 of this thesis, surplus agents are the ultimate economic beneciaries of optimal diversication, as they receive the direct economic rents from owning a mean-variance ecient portfolio. As such, they are directly incentivized to drive allocation to the optimum. It thus seems logical that they should be the least cost avoider when it comes to the market-induced misallocation. In the sphere of startup equity, where their portfolio is underweight with respect to VC-backed startups, they can always ‘vote with their feet’ by reallocating funds to pooling vehicles that provide them with more optimal exposure or demand the creation of such pooling vehicles from their investment investor, it seems to justify a greater ow of capital into the business from more risk averse institutional investment sources. is may mean greater access to institutional funds for those seeking to form new venture capital funds.’). 281e rm has made a name of itself for keeping their fund sizes ‘small’, in the $400m to $500m rage, despite being oversubscribed by LPs. See Konrad(2015) (‘ And the rm has eschewed the temptation to raise ever larger funds, keeping its own below $500 million.’); Loizos(2020) (‘He will not be investing the rm’s 10th venture fund, which is reportedly targeting $425 million in capital commitments. […] Benchmark — which has always run a fairly small operation — has routinely groomed new investors as veterans of the rm have moved on. When Benchmark raised its last fund — another $425 million vehicle in 2018 — it parted ways with Mitch Lasky and Ma Cohler.’). 282See Brophy and Guthner(1988) (in the same vein ‘For entrepreneurs seeking venture capital funds for their young companies, it may also mean a lower potential cost of capital for the nancing of business venturing.’).

129 managers. Against this background, it appears to be the most ecient solution to assign the cost of such diversication to creditors. However, as suggested in chapter 1, a full assessment requires us to consider the alternative of assigning these costs to the credit issuers, in particular through either (i) issuer-sponsored diversication vehicles or (ii) internal diversication. In the area of startup equity, however, neither of the two options seem very promising. Firstly, with respect to issuer- sponsored diversication vehicles, the ‘index addition eect’ that may incentivize issuers to carry the costs of pooling vehicles may lead to adverse selection in the sphere of technology startups. Similarly, internal diversication by issuers does not appear to be an alternative to investor diversication, given the small size of startups. However, with respect to internal diversication, the large public technology companies, including Google, Facebook and Microso,283 can be considered as diversied technology pooling vehicles, in particular as they keep acquiring and aqui-hiring smaller technology startups.

2.6.3.3.2 Firm-induced misallocation

Firm-induced misallocation results from the dominance of rm-based nancing for a specic segment of the market. In this respect, as suggested by the proposed regime above, either (i) venture rms or (ii) startups appear to be best positioned to reduce such misallocation. Given that their contractual relation is at the center of this misallocation, it appears that these two parties are also the lowest cost avoiders. In particular, they can be either incentivized or obliged to provide more investment access to market-based nancing providers. If they would be obliged, this would curtail their contractual freedom. However, the existing securities regulation regime has one key lever through which it may be able to force both venture capital funds and startups to accept such obligations: the Reg D exemptions from securities regulation, through which they can currently operate in the ‘shadows of security laws’. In particular, future qualication for this exemption could be made contingent on startups or venture rms granting access to public diversication vehicles in later rounds. Again, chapter 1 of this thesis asks us to also consider the alternative cost assignment. In particular, we should ask whether and how investors could minimize this type of misallocation and at what costs. In the context of startups, the costs of public market investors to compete with venture rm could be substantial. If we think again about the benets provided by specialized venture rms, in particular the signal value and advisory role of venture capital rms, then the public pooling vehicles would need to build up a similar reputation and support capabilities in order level the playing eld. Given the idiosyncratic nature of venture, such costs seem prohibitively high. e investment of public diversication vehicles into late stage startups is oen perceived to represent ‘commodity capital’ or ‘dumb money’.284 Against this backdrop, investors do not appear to be the least cost avoiders in this context.

2.7 Conclusion

is chapter aempts to conduct a holistic analysis of securities regulation in the sphere of Silicon Valley technology startups – a rapidly growing asset class with idiosyncratic investment properties. e chapter’s core contribution lies in proposing and applying a high-level analytical framework to this asset class, the Coase eorem of Securities Regulation, which provides a novel law and economics perspective on the regulation of startup nancings. In a rst step, the eorem is used to compare and contrast the allocation through venture capital rms with the public market allocation along the regulatory cost dimension and, in a second step, it allows us to identify specic policy levers that could foster and revive capital formation of startup equity through open markets. In a systems thinking approach to securities regulation, the chapter recognizes that any investigation into and reform proposal to existing security laws must permeate all three constituent layers of nancial markets: disclosure,

283See K. Jones(2019) (reporting that Google has made 236 acquisitions, Microso 225 acquisitions and Facebook 85). 284See Kwon et al.(2020) (not nding any empirical evidence for this ‘dumb money’ hypothesis ‘We nd lile evidence in support of the third posited factor, that mutual fund investments represent dumb money that is used to support higher valuations. Because all investors in the same round generally receive the same terms, informed investors should be less likely to invest alongside dumb money’).

130 liquidity and diversication. Across these dierent layers, the chapter nds that existing securities regulations and the microstructure of public markets that has evolved around these regulations, eectively ‘prices’ technology startups out of public markets and into the (venture) rm allocation. Put dierently, the regulatory costs imposed on startup nancing transactions clearly favor the rm over the market. In the context of U.S. security laws, the lenient Reg D exemptions for venture funds allow startups to be funded in the shadow of the market by rms that take concentrated bets in them, require fewer disclosures and oer less pricing pressure than the public markets. As a result, for most startup founders, go public is no longer the preferred exit route, as it appears far more aractive to scale operations privately through venture capital funding at the early stage and to later exit privately through an acquisition by a large tech conglomerate. In search of security law reforms, the chapter identies concrete policy tools along the dierent nancial market layers to (i) reduce friction to an optimum and to (ii) re-assign costs to the least cost avoider. At the disclosure layer, the chapter proposes to start ‘measuring what maers’ by focusing on passive data feeds through APIs of raw nancial and alternative data points that beer reect traction for specic tech verticals. In addition, startups can be relieved from disclosure costs by reassigning some of the costs of data aggregation and due diligence to investors. At the investment and liquidity layer, the chapter proposes to encourage a more open ecosystem of underwriters, market makers and nancial exchanges. e goal is to shi the focus from liquidity to open access, where a larger pool of market participants can make the market. is may reduce the total costs of intermediation and partially reassign the costs of liquidity to surplus agents. Lastly, at the diversication layer, the chapter proposes to create a novel category of regulated diversication vehicles. With fewer liquidity restrictions than existing fund structures, they may receive statutory access to startup deal ow. In summary, this chapter aempts to tackle a major societal problem of fostering technological innovation by private corporations through ecient capital formation and broad market-based access.

131 Chapter 3

Play-to-pay in Silicon Valley venture networks

132 3.1 Introduction

Over the past decades, venture capital (VC) nancing within the Silicon Valley ecosystems has been an important source of nancing for innovative companies. While the perimeter of this startup cluster is geographically limited,1 its reach goes far beyond regional and national borders. A disproportionate number of ‘unicorn’ high-tech startups, those with a $1bn valuation or above, have emerged from within this cluster. Equally important, the innovation output of this cluster has a macroeconomic dimension that is unparalleled, both in the United States and globally. Kaplan and Lerner(2010) have estimated that even though fewer than one sixth of 1% of companies receive venture nancing, roughly one-half of all initial public oerings (IPOs) are VC-backed. Gornall and Strebulaev(2020) estimate that public corporations, which were previously venture-backed, account for more than forty percent of R&D spending and one-h of the market capitalization of US public companies. On a fund performance level, R. Harris, Jenkinson, and Kaplan(2014) have provided empirical evidence that VC rms outperform the public markets net of fees. is chapter analyzes the microstructure of venture investments in technology startup rms within the Silicon Valley ecosystem. For many decades, investments in Silicon Valley startups have occurred in an intermediation structure that has been dominated by a closely-knit regional and relational network of venture capital rms. Firms like Kleiner Perkins, Sequoia Capital or Accel Partners have since institutionalized the process whereby the future technology- giants are identied, nanced and developed, years before they enter the public markets. In particular, the chapter takes a network-based perspective, as networks feature prominently throughout the lifecycle of venture investments. With respect to the screening stage, venture rms are known to rely heavily on their network with startups and other venture rms to source future deal ow. Secondly, rather than investing alone, venture rms oen syndicate deals with other VCs (Lerner, 1994). is gives VCs access to a web of relationships with other funds through current and past investments. Once invested, venture capital rms famously draw on their networks with service providers — ranging from lawyers, head hunters and investment bankers — to support their portfolio companies (Sahlman, 1990). e venture capital industry has changed substantially in the past decades. While the role of venture rms during the Dotcom boom was to prepare the startup for a relatively quick IPO, startups now have a median age of 11 years when they go public (Rier, 2019). is has led to a maturation of the VC industry and a specialization of rms across dierent stages and industry verticals. Similarly, one would expect venture networks to have changed over time. In this chapter, a dataset of venture capital investments from the post-Dotcom era in the time frame between 2004 and 2014 is analyzed empirically using established methods from network theory. Within the scope of this chapter, a number of open questions with respect to the dynamics of networked venture investments are analyzed. Firstly, it is explored how the position of the venture capital rm within the network aects the rm’s performance. Secondly, the chapter tries to establish whether and how venture networks dynamically adjust over time, industries and stages. Lastly, two disparate eects that have shaped the venture industry over the past decade are scrutinized: (i) the rise of billion dollar ‘mega funds’ and (ii) the emergence of founder-led venture funds.

3.2 Related Literature

To date, there exists only sparse literature that empirically looks at the network structure of venture capital rms:

• Hochberg, Ljungqvist, and Lu(2007) examine the performance consequences of the VC syndication network structure on portfolio company investments.

• Alexy, Block, Sandner, and Wal(2012) show how social capital of venture capitalists (VCs) aect the funding of startups. By building on the social capital literature and network methodology, they show a positive eect of VCs’ social capital, derived from past syndication, on the amount of money that startups receive.

1With respect to the geographical limitation, see e.g. the geospatial study of Guzman and Stern(2015), which maps entrepreneurial quality in the Silicon Valley by matching business names to intellectual property.

133 is chapter adds to this strand of the literature, by shedding further light on the topological network structures of VC networks. While both Hochberg et al.(2007) and Alexy et al.(2012) focus on the collaboration of VC rms, my research tries to drill deeper into the network and identify network dynamics and strategy shis over time.

3.3 Networked venture capital rms

3.3.1 Venture capital as capital and information brokers

One of the main value propositions of venture capital rms is their role as information intermediaries between portfolio companies. e need for information intermediaries arises in venture, both because the markets for idiosyncratic startup information are thin and because the ‘atomically’ small startups oen lack alternative trusted communication channels. To illustrate this point, one of Silicon Valley’s most venerable venture capital rms, Kleiner Perkins, has long claimed that it facilitates inter-organizational cooperation among its portfolio company network by ‘brokering’ strategically important information among them. In the past, the rm would publicly advertise on its website that there existed in excess of a hundred strategic alliances among its portfolio companies:2

We borrow the term ‘keiretsu’ from Japan’s powerful networks of companies. However, unlike Japan, Kleiner’s keiretsu is a particularly western, entrepreneurial, loosely coupled web of relationships. Kleiner doesn’t control any ventures: they’re each independent, run by strong, outstanding entrepreneurs. ere’s no central controlling bank, or interlocking board of directors. But the executives in the KPCB Keiretsu oen share experiences, insight, knowledge, and information. is network, comprised of more than 175 companies and thousands of executives, has proven to be an invaluable tool to entrepreneurs in both emerging and developing companies.

Lindsey(2002) has provided some empirical evidence of this ‘keiretsu’ phenomenon in VC portfolios. Aoki(2000) points to the fact that early-stage startups compete in innovation and thus need to encapsulate their information processing activities to excel and avoid substitution. Since these entrepreneurial rms face imperfect markets for information, venture capital rms play a central role in this evolutionary selection as information brokers. is chapter aempts to represent this keiretsu eect by building on the theory of Burt(1992), who has developed the concept of ‘bridging social capital’. Applied to the context of venture capital, it is assumed that social capital exists where venture capital rms have an advantage because of their location in a network. Contacts in the investment network, both to other venture capital rms and to startups, provide insights, collaboration and feedback, which can be benecial to the central players in the network. Social structures in high-tech areas tend to be characterized by dense clusters of strong connections. Information within these clusters tends to be rather homogeneous and redundant. When two separate clusters possess non-redundant information, there is said to be a structural hole between them. Venture capital rms bridge such structural holes and thereby have an advantage in detecting and developing rewarding opportunities, in other words, they can mobilize social capital by acting as ‘brokers’ of non-redundant information between two clusters that otherwise would not have been in contact.

Hypothesis 1. Venture capital rms with a central position in the investment network and high social capital endowment have a higher likelihood of funding successful startups.

Put dierently, the most lucrative investment opportunities can be appropriated by the most well-connected venture capital rms, constructing a winners’ contract network of Silicon Valley venture rms.

3.3.2 Network dynamics in Silicon Valley’s inner venture circle

It is a well-known fact in social networking theory that networks that persist over time have more meaning and are likely to have more real-world consequences, as they can serve more purpose. Furthermore, it is a robust nding in

2See Hsu(2004)

134 social network theory that the networks which are more closed, result in more stable relations and that the reputations emerging within these networks also tend to be more stable over time (Burt, Kildu, & Tasselli, 2013). Given the structural hole properties of venture networks described above, one could expect that venture networks will reach a static equilibrium, where a few rms at the center monopolize the best deal ow of the most promising startups. is, in turn, would allow them to raise larger follow-on funds and write larger checks. In other words, this would mean that the venture network would be static over time. However, the data from the past decade paints a dierent picture. While venerable venture rms have become indeed stronger and beer capitalized, a plethora of new funds has managed to enter the market and serve dierent segments of the market. Indeed, when Burt(1992) developed the concept of structural holes, he went beyond a static conception of social structure. He questioned the notion that powerful actors could ‘sit back’ and simply reap the fruits from their central structural position in the network. To the contrary, he developed a notion of what could be referred to as entrepreneurial networking. It reects the fact that entrepreneurs, just as they can strategically put other resources, such as money or time to work, can also put their social resources to work and turn them into a prot:

‘You enter the structural hole between two players to broker the relationship between them […] I will treat motivation and opportunity as one and the same […] a network rich in entrepreneurial opportunity surrounds a player motivated to be entrepreneurial. At the other extreme, a player innocent of entrepreneurial motive lives in a network devoid of entrepreneurial opportunity.’(Burt, 1992)

Burt(2005) has introduced a ‘structural entrepreneur personality index’, which can be understood as an aempt to quantify this inclination to exploit social resources. Against this background, hypothesis 2 is proposed as follows:

Hypothesis 2. Venture capital networks are not static, but rather dynamically adjust over time.

In the venture capital industry, such ‘entrepreneurial networking’ in the sense of Burt(1992) revolves around maintaining and enhancing the rm’s reputation within the venture and entrepreneurial community. As certication agents, the reputation of a venture rm is critical, both for their portfolio companies and themselves (Nahata, 2008). Friend (2015) posits that the signal value of a venture rm’s reputation can be so critical to founders, that founders are sometimes willing to accept a discount of twenty-ve percent on the valuation of a funding round.3 In the same vain, founders of notable unicorn startups, such as Stripe4 and Slack,5 have been rather vocal in pointing out the value of the certication eect of venture funding by highly networked venture rms for the company, potential employees, customers and the press. Given the networked nature of the venture industry and the recurring requirement to raise capital for new funds, it is critical for venture rms to remain relevant and well-respected among peers and entrepreneurs. Sahlman (1990) describes how a good reputation aracts potential investors, helps to source future deals and build meaningful relationships with entrepreneurs and other key service providers, such as lawyers, headhunters, investment bankers, auditors among others. is leads to a dynamic process whereby new rms enter, build up a reputation, while older rms either maintain their reputation or become sidelined by the new breed of fund managers.

3.4 Venture capital over time

e below characterization in section 3.4.1 and 3.4.2 of the transformation in the venture landscape is a summarized version of the more extensive description in chapter 2. us, for a more detailed account, including more data points and examples, chapter 2 of this thesis should be consulted.

3See Friend(2015) (‘e imprimatur of a top rm’s investment is so powerful that entrepreneurs routinely accept a twenty-ve per cent lower valuation to get it.’). 4See Friend(2015) (‘Patrick Collison, a co-founder of the online-payment company Stripe, says that landing Sequoia, Peter iel, and a16z as seed investors “was a signal that was not lost on the banks we wanted to work with.” Laughing, he noted that the valuation in the next round of funding—“for a pre-launch company from very untested entrepreneurs who had very few customers”—was a hundred million dollars.’). 5See Friend(2015) (‘Stewart Buereld, a co-founder of the oce-messaging app Slack, told me, “It’s hard to overestimate how much the perception of the quality of the V.C. rm you’re with maers—the signal it sends to other V.C.s, to potential employees, to customers, to the tech press. It’s like where you went to college.”.’).

135 3.4.1 Venture capital in the Dotcom era

e Dotcom boom, which started in the mid-1990ies and ended in the 2000s, famously saw the quick rise and fall of a plethora of rst wave internet and technology startups. During this time, both the venture capital industry and the startup ecosystem looked very dierent from today. Venture capital was a small niche asset class, raising funds in the lower $100m, rather than billions, as is oen the case today. With respect to the startup ecosystem, a lot of the ‘plumbing of the internet’, such as scalable cloud server infrastructure or open-source building blocks for developing web applications did not exist yet, making it costlier for people to build and launch the rst version of their product. To highlight a few salient features of the Dotcom era, one can point to:

• ick road to IPO and fewer funding rounds: With a median age of 5 years at IPO, the time from founding a startup to going public was extremely short during the Dotcom bubble, less than half of what it is today (Rier, 2019).

• Pre-product nancing: it was customary to raise venture funding on the basis of a business plan or sometimes a mere idea, as opposed to a live product with initial revenue traction or even a minimum viable product (MVP). As a result of the rush to public markets and the higher funding requirements, there was arguably less focus than today on building and perfecting the product before major nancing was deployed.

• Dominant venture capital rms: During the Dotcom era, venture capital funds oen assumed the role of dominant capital providers. In particular, they were known to quickly replace founders with professional managers, cut future funding rounds when founders would not comply with demands and impose stringent nancing terms that sometimes heavily diluted founders over time.

3.4.2 Modern venture industry

Aer the Dotcom bubble burst, the startup and venture capital environment has changed signicantly across the dimensions highlighted above. Venture capital now is an established and highly institutionalized asset class, with many funds managing multiple billions in assets under management (AuM).6 With respect to the startup ecosystem, a lot of the ‘plumbing of the internet’ is now in place, allowing entrepreneurs to build, ship and iterate on soware products in increasingly quick cycles. To highlight a few salient features of the Dotcom era, one can point to:

• Long road to IPO and multiple funding rounds: With a median age of 11 years at IPO, the time from founding a startup to going public is now more than double of what it was during the Dotcom boom (Rier, 2019). At the same time, startups have steadily raised more and more private funding rounds from venture capital rms.

• Lower funding requirements: Coyle and Green(2014) report that around 2005 a number of technological developments, but in particular the rise of a robust cloud computing infrastructure and third-party cloud providers, most notably Amazon Web Services (AWS), has led to a dramatic decline in the costs of launching a tech startup.

6See NVCA(2019) and NVCA(2011) (reporting total cumulative assets under management of the U.S. venture industry of $689bn compared to cumulative assets of $30.8bn in 1988 and of $127.8bn in 1998).

136 is has allowed many founders, not just to experiment, but also to self-nance or ‘bootstrap’ their startup, sometimes for many years, before raising their rst institutional round of venture funding.

• Rise of private acquisitions: At the same time, an increasing number of startups have exited by way of acquisitions by larger technology companies. is included so-called ‘aqui-hires’, whereby the startup is acquired primarily for the talent, rather than the business, product or underlying technology.

• Rise of FPOs: In addition to fewer IPOs and the rise of acquisitions, another common way for founders to exit their companies is by way of secondary transactions in the course of late stage nancing rounds, so-called ‘nal private oerings’ (FPOs).

• Product focus and traction: On the other hand, over the past decades, the bar has been raised for entrepreneurs seeking venture funding. With the declining costs of starting, startups are also expected to show some traction and ideally so-called ‘product-market t’ and positive ‘unit economics’ before approaching institutional capital providers.

• Emergence of ‘founder-friendly’ funds: However, for those entrepreneurs who successfully meet the criteria of VC funds, the pendulum of power has now swung in their direction. While founders were oen tightly controlled by venture rms in the 90ies, the top entrepreneurs can now command favourable terms, ranging from dual-class share structures to uncapped convertible notes.

• GPs with operational/founder backgrounds: In line with the picture painted above, many venture capitalists today have a background as founders or operators, rather than in investment banking or management consult- ing. is also reects the maturation of the technology industry. In the 1990ies, there was still a scarcity of entrepreneurs, who had successfully built, scaled and exited technology companies.

In summary, it can be said, that the startup and venture capital ecosystem has matured into a calmer and friendlier environment. However, startup founders have to work harder to prove their merit, while venture rms spend more resources actively marketing themselves to entrepreneurs. Digging a bit deeper into the venture capital network, in addition to what has been laid out in chapter 2 of this thesis, two macro trends in the venture industry can be identied. Firstly, the rise of ‘mega funds’, venture rms raising billion dollar fund vintages, and secondly, the emergence of ‘founder-funder’ VCs, former founders turning to angel and venture investing.

3.4.3 Rise of the mega funds

With the venture capital industry maturing over the past two decades aer the Dotcom boom, we have seen the rise of billion-dollar ‘mega funds’, which dominate late-stage startup funding (Series B onwards). ese ‘power players’ are oen VC rms that have been around over multiple decades, such as Kleiner Perkins, Sequoia, USVP, IVP, NEA, Venrock, Bessemer, Norwest and Accel to name just a few. A salient example is provided by Sahlman(1990), who reports fund sizes from the venture capital rm Institutional Venture Partners (IVP) in the late 80ies:

‘Institutional Venture Partners (IVP), a California- based venture-capital rm, raised $16.5 million in 1980, the year it was formed. In 1982 the IVP management company raised $40 million in a fund called IVP II. e group raised $96 million in 1985, launching IVP III, which was followed in 1988 by IVP IV, a $115 million fund.’

In contrast, in 2017, IVP announced that it raised a $1.5bn fund in 2017 (Roof, 2017b). is is just one of many example. e NVCA(2019) has reported on ten venture funds that have raised billion dollar venture funds in 2018 alone.7 In aggregate, the NVCA(2011) has reported a sharply growing average fund size in the U.S. venture industry

7See NVCA(2019) (these included Sequoia Capital ( $8bn), Tiger Global Management ($3.75bn), Bessemer Venture Partners ($1.85bn), Norwest Venture Partners ($1.5bn), General Catalyst ($1.375bn), GGV Capital ($1.36bn), Newview Capital ($1.35bn), Lightspeed Venture Partners ($1.05bn), rive Capital ($1bn), Index Ventures ($1bn)).

137 from $35.4m in 1988, $84.6m in 1998 to $203.3m in 2018.8 Oen times, these larger funds set out to nance startups emerging out of the still smaller early stage funds, providing them with capital at the later stage of their life cycles (from Series B or C onwards). ese ‘mega funds’ oen hold long track records, which provides them with substantial advantages when raising funds from institutional investors. Given the large fund sizes, they are able to write larger checks, thereby potentially monopolizing the high quality late stage deal ow. Over time, this has led to a two-tiered network structure, where the growth capital fund oerings of venerable venture rms serve a substantially dierent segment of the venture capital markets. While competition in the growth capital segment is now among the ercest, with many funds oen pre- empting the Series B or Series C shortly aer the previous round, this was long an under-served market segment. As growth capital funds invest only when there is ‘more signal’, in particular more revenue history and traction, the risk and return characteristics of the underlying portfolio companies also look very dierent.9

3.4.3.1 Startups staying private longer

e rise of ‘mega funds’, with sizeable growth capital fund oerings, is directly related to the fact that startups have increasingly chosen to stay private for longer. Rier(2019) has reported that the median age for tech companies going public in 1999 and 2000 was 5 and 6 years, respectively, compared to 12 years in 2018. is could not have been the case without the rise of ‘mega funds’ providing growth capital to late-stage startups with higher capital requirements. However, while clearly enabling longer stretches as private companies, the rise of ‘mega funds’ was not the only reason for this shi in the past decades. As outlined in chapter 2 of this PhD thesis, regulatory costs and founder preferences can also be considered key drivers. With respect to regulatory costs, one can point to the rising costs of being a public company by way of major reforms to securities regulation, in particular the Sarbanes-Oxley Act and the Regulation Fair Disclosure.

3.4.3.2 Rich-get-richer network dynamics

e rise of mega funds with valuable brands is consistent with the notion of ‘popularity’ in social networks.10 ereby, the more connected a node is, the more likely it is to receive new links. Nodes with a higher degree have a higher likelihood to ‘grab’ new links added to the network. is leads to a rich-get-richer dynamic, which R. K. Merton(1968) initially coined the “Mahew eect”,11 Newman(2001) referred to as “preferential aachment”, 12 and Price(1976) as “cumulative advantage”.13 As the network of a VC increases through investment experience, the ‘mega funds’ will become more eective over time in executing their information brokerage role among startups and VC rms. is is because these ‘power players’ are able to increase their network to startups, entrepreneurs and other VC rms over multiple fund vintage cycles and gain more proprietary knowledge about a particular high-tech sector. Each additional investment link enlarges the valuable information network of the venture rm,14 either through social contacts, insights from deal execution or experience in monitoring entrepreneurs. is leads us to hypothesis 3:

8See NVCA(2019) and NVCA(2011). 9See Shephard(2019) (on established VC rms moving upstream ‘VC rms are less likely to take on early-stage companies, opting instead to invest larger sums in fewer and more established enterprises. ey also have less time to oversee and get actively involved in multiple deals. […] It makes more sense to manage ve deals at $100 million each than 50 deals at $10 million. Smaller deals that can’t noticeably move the needle are no longer worth their time. […] Instead of taking a gamble on an early-stage company which is statistically likely to dissolve within the rst year, seasoned investors are increasingly taking a wait-and-see approach, banking on companies that emerge as top contenders within a specic niche or sector.’). 10See Stadtfeld, Takacs, and Voros(2020) (‘Popularity is an endogenous social mechanism and relates to the tendency that those who are perceived as popular by others tend to aract additional positive ties.’). 11See R. K. Merton(1968) (is is expressed in the principle of cumulative advantage that operates in many systems of social stratication to produce the same result: the rich get richer at a rate that makes the poor become relatively poorer’). 12See Newman(2001) (‘In the case of degree distributions, it is conjectured that, for a variety of reasons, vertices accumulate new edges in proportion to the number they have already, leading to a multiplicative process which is known to give power-law distributions. is process is oen called “preferential aachment”.’). 13See Price(1976) (‘A Cumulative Advantage Distribution is proposed which models statistically the situation in which success breeds success.’). 14See Sorenson and Stuart(2001) (‘Following this rationale, the structure of social and professional relations likely inuences which actors in the VC business become aware of promising, early-stage investment opportunities. e majority of investment targets are small, inchoate entities. Timely information regarding high-quality investment opportunities in this domain oen reaches a venture capitalist through her network.’).

138 Hypothesis 3. Venture networks are dominated by a few popular and well capitalized VC rms with the strongest network.

As a result of the ‘rich-get-richer’ eect, a small number of venture capital rms dominates the market aer only a few consecutive rounds. ese well-capitalized funds co-invest exclusively with funds of a similar size, thereby forming a close-knit co-investment network that exhibits another common property observed in social network formation referred to as ‘homophily’.15 is sub-network of dominant rms can be considered to form the inner circle of Silicon Valley. Due to the network growth algorithm in place, membership to the inner circle is conjectured to be relatively stable over time.

3.4.4 Innovation recycling

When the Dotcom bubble burst in early 2000, the stock market gains16 of many public market investors quickly evaporated.17 Similarly, many startup founders who missed the IPO window, did not get acquired, or who were restricted from selling shares post-IPO in the lock-up period, suered substantial ‘paper’ losses.18 However, despite widespread losses, many founders managed to lock in substantial exits and accumulate private savings. e funds realized from exits could be re-deployed in startup investments in the aermath of the Dotcom crash. is process can be referred to as ‘innovation recycling’, since it re-deployed nancial resources, but also personal experiences, which were obtained in the rst technology wave in the pre-Dotcom era. erefore, just like ‘petrodollar recycling’ involves the re-allocation of prof- its from oil-producing countries, ‘innovation recycling’ involves the successful re-allocation of prots into technology startups from the ‘founder-funder class’.

3.4.4.1 Emergence of the founder-funder class

A key element of ‘innovation recycling’ is the emergence of a new class of venture capital fund mangers, which is here referred to as the ‘founder-funder’ class. ese are previous startup co-founders or early employees of breakout startup, which, aer a successful exit have turned to angel and venture investing (mostly at the Seed to Series A stage).19 Notable examples of this ‘founder-funder’ class are Peter iel, co-founder of PayPal and Palantir, who has angel invested in Facebook and later started Founders Fund, Marc Andreessen, co-founder of Netscape and Opsware, who has started the venture rm Andreessen Horowitz, or Vinod Khosla, co-founder of Sun Microsystems, who started the venture rm Khosla Ventures. At an ever accelerating pace, there exist many such examples of emerging Seed/Series A venture rms being started by former operators. What they all have in common, is that they have a well-established network in the startup and venture community and nancial and knowledge resources to draw from when entering venture investing.

3.4.4.2 Founder-friendly environment

e rise of founder-funder venture rms has gone hand-in-hand with the emergence of a much more founder-friendly environment. While there are now a large number of venture capital providers available, the number of highly promising startups remains small and increasingly competitive to reach for venture rms.

15See Stadtfeld et al.(2020) (‘Homophily relates to the increased likelihood of forming ties to others who are similar.’); McPherson, Smith-Lovin, and Cook(2001) (‘Similarity breeds connection. is principle – the homophily principle – structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship.’). 16See DeLong and Magin(2006) (reporting on the doubling of the Nasdaq in the run-up of the Dotcom bubble ‘the NASDAQ index reached dizzying heights indeed as it exploded in late 1999 and more than doubled in value in the year up to its late winter 2000 peak’). 17See DeLong and Magin(2006) (reporting on the Nasdaq losses ‘immediately aerwards came the huge and bloody bath taken by investors in the NASDAQ from February 2000 to September 2002 as it lost three-quarters of its value.’). 18See Fost(2003) (chronicling the paper losses of Webvan founder Dan Fost ‘According to published reports from the time of Webvan’s bankruptcy ling in July 2001, Borders had invested $3.54 million in the company and sold his stake for $2.7 million, a loss of $840,000. His paper loss was something else entirely: At Webvan’s peak, Borders’ stake was worth $1.7 billion.’); Pinter(2003) (chronicling the paper losses of eGlobe founder Dan Fost ‘ree years earlier, at 24, Stephan Paternot was worth about $75 million-not cash, but merely an ethereal value assigned to the stock he owned. He’d co-founded theglobe.com in his Cornell University dorm room and, two years later, took the company public. […] eglobe.com’s stock ailed and languished, and today what’s le of the company trades at around 13 cents a share (down from an opening-day high of around $97). During his tenure, Mr. Paternot managed to cash out only around $1.5 million of his company stock (which he reinvested entirely in the soon-to-tank urbanfetch.com).’). 19See Jason Calacanis (Producer)(2020) (Michael Kim, Seed stage fund-of-fund manager at Cendana Capital describing the founder-funder class of Seed stage ventue investors ‘About two thirds of our fund managers are former entrepreneurs and that’s very important and ties to the fact that we want to see these people who have the credibility to lead their investments, because the founders want to work with them, because they had that experience in the eld. If you look at later stage rms, a lot of them are actually ex-bankers and lawyers so it’s a dierent kind of skillset. I think at the Seed stage and Series A, you want to see former operators, who had that experience of running teams and managing P&L.’).

139 e best startup founders manage to self-nance or ‘bootstrap’ their startup, oen for many years before raising their rst round of outside venture capital funding. In 2017, for example, the spell-checking startup Grammarly, raised its rst round of outside venture capital, a $110m Series A, aer having existed independently for almost a decade before that (Roof, 2017a). Sometimes these bootstrapped startups even have to be actively wooed and convinced by venture capitalists to take their money. Famoulsy, Jim Goetz, a partner at Sequoia Capital, would call the founder of Whatsapp for a long time before the company accepted venture nancing from the rm (most of which was never spent, as the company was operating protably almost from the beginning).20 As a general trend it can thus be noted, that for those entrepreneurs who successfully meet the stringent criteria of VC funds in terms of product-market t and traction, the pendulum has swung in their direction. Pollman(2019), for example, reports how many ‘unicorn’ startups, like Facebook, Airbnb and (infamously) WeWork, have implemented dual-class shareholding structures that give supervoting shares to founders. is gives founders a mechanism to control the board and strategic decisions, something that would not have been possible in the venture landscape during the Dotcom era. is trend towards a founder-friendly startup environment has impacted predominantly early stage investing, where the personality and cultural match between founder and VCs remains of utmost importance.

3.4.4.3 Play-to-pay in founder-led VC rms

e two trends outlined above, namely (i) the rise of the ‘founder-funder class’ of fund managers and (ii) the change in the startup ecosystem towards a more founder-friendly environment leads to hypothesis 4, which can be stated as follows:

Hypothesis 4. Founder-led venture rms have beer access to higher quality deal ow, resulting in higher fund returns.

is is referred to as a ‘play-to-pay’ dynamic, which is a reversion of the typical ‘pay-to-play’ expression. Under ‘pay to play’, it is typically understood that an economic agent pays an access or entrance fee to be able to participate in an economic activity or transaction. On the contrary, what we see in this early stage venture nancing segment is the opposite: in order for venture capitalists to be able to nance the high potential startups, they have to credibly demonstrate to founders that they can add value. In the economic literature, this dual role of venture capitalist as capital providers and advisors is typically modeled as a double-sided moral hazard problem.21 Venture capital rms thus oen dierentiate themselves through a number of services they oer.22 A major way venture capitalist can signal their value-add to entrepreneurs, is by eectively communicating that they have engaged in the economic activity of successfully starting and scaling a technology company before, in other words that they have ‘played’ the startup game before.23 is can mean having deep domain expertise in an industry vertical or more generally patience and respect for the entrepreneurial journey. For founders, it is highly valuable to be

20See McBride(2014) (Describing how Sequoia’s GP Jim Goetz was actively pursuing the WhatsApp deal ‘Founders say that Sequoia, based on Silicon Valley’s legendary Sand Hill Road, is adept at competing with other venture capitalists when a startup is seeking funds. It also excels at courting promising companies that believe they don’t need cash and persuading them to take it - like WhatsApp, whose founders weren’t actively seeking funding. “e notion of them marching up and down Sand Hill with a Powerpoint deck is comical,” said Goetz, referring to WhatsApp founders Jan Koum and Brian Acton. He had cultivated them since 2010 before closing Sequoia’s rst investment the following year.’). 21See Casamaa(2003) (‘is paper provides a theory for the dual (i.e., nancing and advising) role of venture capitalists. […] ite plausibly, I assume that the level of eort exerted by the advisor, as well as by the entrepreneur, to develop the project is not observable. Consequently the entrepreneur and the advisor face a double moral-hazard problem.’). 22See Friend(2015) (commenting on Andressen Horowitz’s services ‘Chris Wanstrath, GitHub’s co-founder and C.E.O., said that a16z’s services were a major araction: “It’s like a buet – they oered a bunch of great dishes, and we wanted to sample them all”.’). 23See Friend(2015) (‘Andreessen and Horowitz recruited only general partners who’d been founders or run companies.’); Emily Chang (Producer) (2016) (Vinod Khosla at minute 7:49 ‘if you’re going to advise entrepreneurs, you should have started companies, you need to know how hard and painful it is, how dicult the tradeos are, it is painful being an entrepreneur and unless you have empathy for an entrepreneur by having done it yourself you don’t have the right to advise an entrepreneur in my view.’); Carver(2011) (‘Sequoia branding itself with the slogan “e entrepreneurs behind the entrepreneurs”’); Mindus and Wessel(2017) (‘e best venture capitalists are former founders. at’s the conventional wisdom — at least among entrepreneurs, who oen say they prefer investors who have walked in their shoes.’); Feldman(2007) (chronicling the founding of Founders Fund ‘Howery and his partners also believe that their fund is dierent because it is run by former entrepreneurs–people who truly understand the diculties of running a startup. at experience resonates, says Darren Rush, chief executive of Koders, a Santa Monica-based search engine for soware developers. Rush received an investment from a group led by Founders Fund last April […] ”ere is cultural compatibility,” he says. Suneet Wadhwa, co-founder and CEO of Engage, an Internet dating site, feels similarly. At 39, he already has had one success as co-founder of Snapsh, which was snapped up by Hewle-Packard in 2005. So when he began trying to raise $1.1 million in seed-round nancing for Engage in June 2005, he had a lot of options. Why Founders Fund? Simple, says Wadhwa. ”ey’ve been there before.”’).

140 nanced by fund managers who have been there themselves and who have understood and seen the challenges of the space rst hand. As nancing rounds involving the most sought-aer founders are typically highly competitive,24 with the founders oen receiving multiple term sheets, we should expect that founder-led venture funds have a competitive advantage in geing an allocation in these nancings.

3.5 Network analysis

3.5.1 Network analysis methodology

Network analysis methodology was used to empirically study the structure of Silicon Valley venture capital (VC) investments. A network is a set of items (nodes or vertices) connected by links or edges. Here, nodes represent venture capital funds and edges represent equity investments by VC rms in startups. ere are strong arguments for using this methodology: rstly, network analysis allows us to empirically account for many paerns of interaction between the relevant economic actors in the Silicon Valley ecosystem, namely startups and VC rms, which could not be captured by traditional cross-sectional econometric analysis. Secondly, there exist endogenous statistical network features relating to network topology, such as degree centrality, betweenness or Eigenvector centrality, which is conjectured to have signicant implications for economic behaviour.

3.5.2 Data Collection

To carry out an empirical VC investment network analysis, venture funding was collected in November 2014 from the Crunchbase repository. At the time of the data collection, Crunchbase was an open, crowd-sourced repository containing startup and venture capital investment data, with a particular focus on high-tech sectors, such as enterprise, consumer internet and SaaS. At the time of the data collection, Crunchbase was still maintained by TechCrunch, one of the leading technology and venture capital news outlets in the United States. At the time, it was collaboratively maintained by a community of VCs and startup professionals. Each member could contribute data to the repository and all updates would go through an approval process before being made available online. e actual approval process used a much wider range of information sources than traditional Venture Capital databases, such as Venture Economics. is added to data comprehensiveness, especially for early-stage VC investments and unfunded startups. With respect to earlier startups, Crunchbase information has been reported to have been even superior to commercial VC databases (Werth & Boeert, 2013). As of July 2012, the database of Crunchbase included information about 95’284 companies, 8’013 venture funds and 29’583 funding rounds. Due to the almost complete coverage of startups and VC investors in the internet sector, including the relationships between them, a number of publications have made use of this dataset for empirical studies.25 For this chapter, a subset of the Crunchbase dataset has been extracted, pertaining to investments made by venture capital rms in the SF Bay Area (Silicon Valley), in the time frame between 2004 and 2014. e dataset contains the investments of 730 venture capital rms, 2’627 startup rms, in a total of 8’961 individual investment observations.

3.5.2.1 VC network construction

e VC investment network is represented by a graph (N, g), which consists of a set of nodes N= {1, ...n}, with each node representing either a VC rm or a startup and each edge representing a directional equity investment by the venture capital fund in the startup. e venture network is thus represented by a n×n adjacency matrix: g = [gij]i,j∈N , where gij ∈ {0, 1} represents the availability of an edge from node i to node j. e general form adjacency matrix for the investment can thus be represented as:

24See Grith(2020) (with respect to one of a more recent competitive round in cloud-based collaboration tool Notion where multiple leading venture funds have been rumoured to have courted founders for a long time‘In a moment of uncertainty, Mr. Kothari said, “nancing is a signal of stability, which is important to us.” So they called Sarah Cannon, an investor at Index Ventures who had been courting Notion for over a year.’). 25See, inter alia, Werth and Boeert(2013), Block and Sandner(2009) and Alexy et al.(2012).

141   g11 ··· g1j ··· g1N  . . .   ......   . . . . .    g =  gi1 ··· gij ··· giN     . . . . .   ......    gN1 ··· gNj ··· gNN

ereby gij equates to 1 if an agent i has made an investment into agent j and 0 if no investment is in place. e zeros along the diagonal of the matrix indicate that the agents cannot make an investment into themselves.

A graph is referred to as a directed graph (or digraph) if gij 6= gji and an undirected graph if gij = gji for all i, j ∈ N. Following the ow of funds, the VC investment network is modeled as a directed matrix, which dierentiates between the originating node (venture capital rm) and the receiving node (startup). e edge weight gij > 0 can also take on non-binary values, representing the intensity of the interaction, in which case it is referred to as a weighted graph. In the case of the VC investment network, the edge weight represents the investment amount in each funding round. Furthermore, to describe the venture capital investment networks, which consists of venture capital rms and startups, the concept of two-mode networks or bipartite networks is introduced. Consider a general bipartite network G(X,Y,E) where E is the set of edges. e nodes in X represent venture capital funds, while those in Y represent startups, they are denoted by x1, xi, ..., xn and y1, yi, ..., ym respectively. e general form adjacency matrix can thus be broken down into dierent sub-parts, such that the entire adjaceny matrix can be represented as: " # 0n,n B G = T B 0m,m

Since there are only investments from VC rms into startups and not vice-versa, the sub-part that is of interest for this analysis is the n × m bi-adjacency matrix denoted as B:   x1y1 ··· x1yi ··· x1ym  . . .   ......   . . . . .    B = xiy1 ··· xiyi ··· xiym     . . . . .   ......    xny1 ··· xnyi ··· xnym

Other studies, which have applied network analysis to venture capital investment data26 have focused on the syndication network eects by converting the bipartite network, consisting of two kinds of actors (VC rms and startups), into a one mode network consisting only of VC rms. However, to test the rst two hypotheses, the variables of interest are found at the individual investment level, namely the ex-post success of the nanced startup, as proxied by the exit rate.27 e network is therefore maintained as a bipartite network and the statistical analysis is conducted on the edge level.

3.5.3 Graphical Network Representation

Using the R package igraph, the investment network can be represented graphically. Figure 3.1 shows a graphical representation of the entire investment network with the colors indicating whether the respective node is a venture capital fund, an active startup or a startup that has been ‘exited’ (by way of IPO or acquisition).

26In particular Hochberg et al.(2007) and Alexy et al.(2012). 27is is consistent with the methodology of Hochberg et al.(2007).

142 Startups Startups VCs VCs Exited Startups Exited Startups

(a) Entire universe of 8961 investment observations (b) VC investment network subgraph with degree threshold set at 4 and 4524 individual investment observations

Figure 3.1: Silicon Valley investment network graph with VCs colored light blue, startups colored limegreen and “successful” startups, i.e. those with a liquidity event (acquisition or IPO), colored coral.

Applying dierent degree thresholds, whereby VC rms with fewer investments are excluded from the sample, allows us to zoom in on the network to obtain a beer picture of the inner circle of the Silicon Valley investment network. Figure 3.10 shows a subgraph with degree thresholds set at 6 and 9, respectively. From this graphical representation, it can already be seen that the occurrence of successful investments appears to be more dense in the inner circle of the investment network.

Startups Startups VCs VCs Exited Startups Exited Startups

(a) VC investment network subgraph with degree threshold set at 6 (b) VC investment network subgraph with degree threshold set at 9 and 2539 individual investment observations and 1036 individual investment observations

Figure 3.2: Silicon Valley investment network graph with VCs colored light blue, startups colored limegreen and “successful” startups, i.e. those with a liquidity event (acquisition or IPO), colored coral.

To graphically capture the fact that the most successful VC rms are operating at the heart of the investment network, the 10 most inuential VC rms – according to the Forbes VC Midas List 2014 – were colored red in gure 3.3. is representation shows all 10 VC rms densely clustered at the core of the network, which may be viewed as a graphical representation of their social capital endowment.

143 Startups Startups VCs VCs Top 10 VCs Top 10 VCs

Figure 3.3: Silicon Valley investment network graph with VCs colored light blue, startups colored limegreen and the top 10 VC rms (according to the Forbes VC Midas List) colored red.

3.5.4 VC network topology measures

Unless otherwise stated, the notations used correspond to those introduced by Wasserman and Faust(1997).

3.5.4.1 Degree centrality

Degree centrality measures connectivity of the economic agent via the number of ties of the node within the network, in this case VC investments. e more investments a VC rm executes, the deeper is the rm’s involvement in the ecosystem. is entails a higher level of access to information, peers, expertise and future deal ow. Formally, let Iij = 1 if there exists at least one investment from VC rm i into startup j. e VC’s degree centrality can be represented by an actor-level degree centrality index CD(ni):

J X CD(ni) = Iij (3.1) j=1

is degree centrality index was initially introduced by Freeman(1979) and is relied upon in the R package igraph, which is used for the empirical implementation of our model.

3.5.4.2 Betweenness centrality

Interactions between non-adjacent economic actors might require the interaction of other actors within the network. Betweenness centrality determines the extent to which nodes must rely on other nodes to make connections within the network. In our seing, betweenness centrality represents the extent to which a VC may act as a nancial or informational intermediary by facilitating complementary skills and investment opportunities in the absence of direct relationships. Formally, this measures the probability that a communication from an actor j to an actor k ows through the venture rm. For this purpose, it is assumed that each investment has equal weight and that information will move along the shortest path, also known as a geodesic. In the case that there exists more than one geodesic, it is assumed that all shortest paths will be used with the same probability. Let gjk be the number of shortest paths linking actors j and k.

Given an equal probability of using a geodesic, the probability of using an individual one can thus be simplied to 1/gjk. We can then try to gauge the probability that a distinct venture fund i is acting as an intermediary in the communication between j and k. Let gjk(ni) be the number of geodesics linking the two actors j and k with any involvement of i. Under the assumption that geodesics are equally likely to be chosen, gjk(ni)/gjk denotes the probability that venture rm i

144 intermediates between j and k. e actor betweenness centrality index can then be calculated as the sum of these estimated probabilities over all nodes, including actor i:

X gjk(ni) CB(ni) = (3.2) gjk j

e index calculates how ‘between’ the venture rm is. Like the degree centrality index, this betweenness centrality index was initially introduced by Freeman(1979) and is relied upon in the R package igraph used for the empirical implementation of our model.

3.6 Social capital hypothesis

To test the social capital hypothesis, three dierent logistic regression models are estimated, which add dierent network centrality measures to the specication.

Hypothesis 1. Venture capital rms with a central position in the investment network and high social capital endowment have a higher likelihood of funding successful startups.

To test hypothesis 1 empirically, the following econometric network model is specied:

Si,j,k = α + βjIi,j + βkXi,k + i,j,k (3.3)

e startup’s success rate, denoted as Si, constitutes the dependent variable. Startup ‘success’ is proxied by liquidity events, either an exit through an acquisition or an IPO. While this is in line with the methodology of Hochberg et al. (2007),28 it provides a rather conservative proxy, given that it relies on a binary outcome at the end of a startup’s lifecycle. An alternative performance measure would be a startup’s funding levels above a certain threshold. e downside of using funding levels as a performance proxy would be that it may over-index on ‘overfunded’ startups, which may later end up failing despite substantial funding, and under-index on capital-ecient startups. As the independent variables, both endogenous and exogenous network variables are dened, denoted as Ij and Xk respectively. Endogenous network variables are those variables that are generated directly through the network topology. In particular, this includes the two network metrics outlined in detail above, degree centrality and edge betweenness. Exogenous network variables are those which cannot be derived directly from network features. In particular, the investment size by the venture rm in a single round and in all rounds, are used as exogenous control variables.

3.6.1 Results

Table 3.1 summarizes the results from a logistic regression for this model under dierent specications. e results indicate that venture capital rms’ position in the investment network, as a proxy of their social capital, indeed signicantly predicts startup success rates, both with the VC degree specication (β = .17 at the p < .001 level) and with the VC betweenness specication (β = .12 at the p < .001 level). ese results also hold for an ordinary least square (OLS) regression with heteroskedasticity-robust standard errors. As conjectured, the direction of this relation is positive. e results therefore suggest that hypothesis 1 cannot be rejected.

28See Hochberg et al.(2007) (‘At the fund level, in the absence of publicly available data on VC fund returns, we examine “exit rates,” dened as the fraction of portfolio companies that are successfully exited via an initial public oering (IPO) or a sale to another company.’).

145 Table 3.1: Logistic Regression Results

Dependent variable:

Startup Success

(1) (2) (3)

Investment by VC (single round) -0.013 -0.015 -0.013 (0.023) (0.023) (0.023)

Total VC Investments (all rounds) -0.053∗∗∗ -0.051∗∗∗ -0.053∗∗∗ (0.006) (0.006) (0.006)

VC Degree 0.173∗∗∗ 0.182∗∗∗ (0.020) (0.031)

VC Betweenness 0.123∗∗∗ -0.011 (0.019) (0.029)

Constant -1.529∗∗∗ -1.046∗∗∗ -1.549∗∗∗ (0.081) (0.043) (0.097)

Observations 8,961 8,961 8,961 Log Likelihood -4,821.166 -4,839.313 -4,821.096 Akaike Inf. Crit. 9,650.331 9,686.627 9,652.191

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

3.6.2 Limitations

In the standard linear OLS regression model, y = Xβ + , the error term represents a random variable that is based on the assumption of being independently and identically distributed (i.i.d.) with a mean of zero that is uncorrelated between observations (E[ij X] = 0 for i 6= j, i.e. there is no autocorrelation) and equal variances across observa- 2 2 tions (E[i X] = σ , i.e. there is no heteroscedasticity). However, in the analysis of network data, the independence assumption for the error terms is frequently violated due to correlation among error terms. For example, a general limitation that arises when using network centrality measures, such as degree centrality or betweenness centrality, is that the results may be subject to network-specic endogeneity biases. Within the context of venture networks, Hochberg et al.(2007) have pointed to the potential of a reverse causality bias. Higher fund exit rates may enable a VC to improve their network position. us, rather than the returns being driven by a rm’s network position, the network position may instead be driven by the returns of the rm’s investments. is reverse causality bias may also be present in our results. Hochberg et al.(2007) aempt to address this endogeneity bias by calculating a fund’s network position based on the venture rm’s syndication activity prior to the fund’s lifecycle.29 us, their endogeneity

29See Hochberg et al.(2007) (‘e way we construct the centrality measures makes it unlikely that our results are driven simply by reverse causality (i.e., the argument that superior performance enables VCs to improve their network positions, rather than the other way around). For a fund of a given vintage year, measures of network centrality are constructed from syndication data for the 5 preceding years. Performance is then taken as the exit rate over the life of the fund, which lasts 10–12 years. us, we relate a VC rm’s past network position to its future performance.’).

146 robustness check relies on (i) a logical separation between the venture rm and the individual fund vintages30 and (ii) a time lag between the network centrality measure and the performance data. Neither of the two assumptions seem very convincing: the structural separation between venture rms and fund vintages does not correspond with the practical maer that reputation and brand value is typically built at the rm level and that it permeates across fund vintages. Similarly, the venture rms’ general partners oen engage over multiple fund vintages, with the value of their network oen accruing over multiple funds and the outside perception clearly being that they are GPs of a particular venture rm, rather than a particular fund vintage. In sum, the method proposed by Hochberg et al.(2007) to address reverse causality does not seem to reect the reality of the venture industry. More advanced methods to address endogeneity biases can be found in the network literature. In the context of social network analysis (SNA), so-called network autocorrelation models have been developed to deal with the issue of the network autocorrelation in the regression analysis.31 ese network autocorrelation models have become the ‘workhorse’ when it comes to accounting for biases resulting from social inuence.32 e standard approach, pioneered by Leenders(2002), is to map social inuence by specifying a weight matrix W network. Fujimoto et al.(2011) describe the weight matrix W as a single mode (actor-by-actor) network, characterized by a N × N matrix with elements wij representing the size of the inuence of actor j (alter) on actor i (ego). Leenders(2002) describes the weight matrix W as the ‘theory a researcher has about the structure of the inuence processes in the network’.33 Leenders(2002) operationalizes W by specifying ‘nearness’, which denes the set of alters inuencing the ego, and by row or column normalizing W, which determines how social inuence is allocated among alters in the network.34 In the context of the venture capital network analyzed, a network autocorrelation model could thus be implemented to deal with the potential endogeneity bias that may drive above results. is would require us to estimate a weight matrix W for the venture rms and startups and to choose what mechanism is driving social inuence in this network: such as communication, comparison or adjacency. Furthermore, either the rows or columns of W could be normalized. Whereby the rst would decrease the inuence each alter has on ego with each additional alter, the later would decrease the inuence alter has on ego with the number of actors inuenced by alter. ese model choices would require especially close consideration in the context of venture networks, since returns and opinion leadership in these networks are oen driven by a few dominant rms that exert substantial inuence on alters.

3.7 Dynamic inner circle hypothesis

Now let us look at hypothesis 2, which holds that venture networks are subject to dynamic change over time. With new rms entering and established rms exiting, hypothesis 2 aempts to identify the higher level network dynamics over time, both in terms of rms participating in the network and actors taking central positions within the network by way of Burt’s ‘entrepreneurial networking’.

Hypothesis 2. Venture capital networks are not static, but rather dynamically adjust over time.

To test hypothesis 2, four separate data sets were constructed from the Crunchbase database for dierent time frames and investment stages. With respect to time frames, the data set was split into two discrete time frames of equal length,

30e Crunchbase data set does not provide this level of granularity, as venture data is only captured at the rm level. 31See Paez, Sco, and Volz(2008) (‘e issue of autocorrelation in regression analysis is so pervasive in the analysis of spatial and network data that it has been called a foundational problem in the social networks literature, and forms much of the basis of contemporary methods in spatial statistical analysis.’); Leenders(2002) (‘Many physical and social phenomena are embedded within networks of interdependencies, the so-called ‘context’ of these phenomena. In network analysis, this type of process is typically modeled as a network autocorrelation model.’); Mizruchi and Neuman(2008) (‘Researchers interested in the eects of social network ties on behavior are increasingly turning to the network autocorrelation model, which allows for the simultaneous computation of individual-level and network-level eects.’). 32See Fujimoto, Chou, and Valente(2011) (‘It has been a workhorse for modeling theories of social inuence by measuring and statistically testing network eects on individual behaviors.’). 33See Leenders(2002) (‘Parameter estimates and inferences based on such autocorrelation models hinge upon the chosen specication of weight matrix W. is matrix represents the inuence process assumed to be present in the network and can be operationalized in many dierent ways. W is supposed to represent the theory a researcher has about the structure of the inuence processes in the network.’). 34See Leenders(2002) (‘With regard to an operationalization of W, two components play a role: the choice for an operationalization of nearness and the choice for a particular normalization which, given a denition of nearness, allocates inuence over the network. In other words, nearness denes which alters constitute ego’s frame of reference (zero and non-zero cells in W), whereas the chosen normalization determines how social inuence is allocated among these alters.’).

147 with the rst ranging from 2004 to 2009 and the second from 2010 to 2014 (November) respectively. While the choice of time frame was arbitrary, the rst window coincided with a period of (relative) restraint in venture funding (in part due to the global nancial crisis), while the second window coincided with a period of hyperactivity in venture funding. With respect to investment stages, the data set was split across seed and venture stage. e classication into ‘seed stage’ and ‘venture stage’ respectively was provided by the Crunchbase database itself. Typically, seed stage investments relate to early stage, ‘unpriced’ rounds made by way of convertible notes or SAFEs.35 In these rounds, in addition to institutional investors, individual angels may also co-invest. e Crunchbase(2020) funding glossary characterizes seed stage investments as follows:

‘Seed rounds are among the rst rounds of funding a company will receive, generally while the company is young and working to gain traction. Round sizes range between $10k–$2M, though larger seed rounds have become more common in recent years. A seed round typically comes aer an angel round (if applicable) and before a company’s Series A round.’

On the other hand, venture stage investments are typically priced rounds led by institutional venture capital rms only. For the present analysis, any round from Series A onwards was classied as ‘venture stage’. Using this data set, bipartite co-investment networks were constructed, which exist between venture rms syndi- cating deals and investing in the same startup in dierent rounds. To construct these co-investment networks a bipartite projection in the sense of Horvat and Zweig(2012) was performed. In this respect, the one-mode projection of B to X was based on the co-occurrence of the actor pairs from X (venture rms). Based on the notation of Horvat and Zweig

(2012), the co-occurrence of two venture funds v, w ∈ X could be denoted by cooccα(v, w) and equaled to the number of common neighbors (co-investments in startups) u ∈ Y they have with respect to relation types α ∈ E. e resulting graph is unipartite and weighted based on the number of co-occurrences. Furthermore, a one step community detection algorithm was applied to identify specic clusters in the investment network.

3.7.1 Seed stage networks

e seed stage communities identied below point to substantial dierences over time. e seed stage venture community in the timeframe from 2004 to 2009 appears to have been rather small, with only 268 seed investments. Notably, the most active investors include accelerator programs (e.g. Y Combinator, Plug & Play Ventures), Ron Conway’s highly active ‘super angel’ fund (SV Angels) and founder-led venture funds (First Round, Floodgate). On the other hand, the seed community identied between 2010 and 2014 is almost ten times larger, reecting the increased activity in the early stage segment of the market. In addition to the central players from the previous episode, there are now further accelerator programs active (e.g. 500 Startups) and substantially more founder-led venture funds (e.g. Andreessen Horowitz, True Ventures, Kapor Capital). In addition, more venerable venture funds, such as Kleiner Perkins, New Enterprise Associates (NEA) and Menlo Ventures, seem to have started investing quite actively at the seed stage as well.

35Discussed in more detail in chapter 4.

148 K9 Ventures● Redpoint● Ventures Y Combinator● First ●Round

Kleiner Perkins ●Caufield & Byers Slow Ventures●

SV Angel● CrunchFund● Felicis ●Ventures Plug & Play● Ventures Google ●Ventures

AngelPad● ● SV Angel● Quest Venture● Partners Data Collective Y Combinator● First ●Round Greylock● Partners Menlo Ventures● Andreessen● Horowitz FF Angel● LLC

True Ventures● SoftTech● VC 500 Startups● Foundation● Capital SoftTech● VC Accelerator● Ventures Felicis ●Ventures Draper Fisher ●Jurvetson (DFJ) New Enterprise● Associates FLOODGATE● XG Ventures● Morado Venture● Partners

Kapor ●Capital Blumberg● Capital Plug & Play● Ventures Crosslink● Capital

(a) Seed stage network (2004-2009) with degree threshold set at 5 and (b) Seed stage network (2010-2014) with degree threshold set at 30 268 individual investment observations and 2679 individual investment observations

Figure 3.4: Silicon Valley seed stage co-investment network graph with one step community detection algorithm

3.7.2 Venture stage networks

Similarly, the venture stage communities exhibit substantial dierences over time. e venture stage in the post-Dotcom timeframe from 2004 to 2009 appears to have been slightly less active, with only 4’621 investments, compared to 6’260 investments between 2010 and 2014. In the 2004 to 2009 timeframe, the most active investors appear to have been venerable venture funds, such as Kleiner Perkins, Sequoia Capital, U.S. Venture Partners (USVP), New Enterprise Associates (NEA), Bessemer, Norwest, Accel Partners and Mayeld. On the other hand, only one founder-led venture fund, First Round, was observed in that period. In the timeframe from 2010 to 2014, we can see that, while the venerable venture funds from before36 are still dominant, they are now joined by a eet of ‘founder-funder’ venture rms, in particular Adreessen Horowitz, Founders Fund, Khosla Ventures, First Round and Felicis Ventures.

InterWest● Partners DAG Ventures●

U.S. Venture● Partners Intel Capital● Crosslink● Capital Menlo Ventures● Norwest Venture● Partners Benchmark● Accel Partners● Morgenthaler● VenturesDraper Fisher ●Jurvetson (DFJ) Google ●Ventures

SV Angel● Greylock● Partners Intel Capital● Battery ●Ventures ● New Enterprise Associates ● ● Mayfield Fund Foundation Capital Sequoia● Capital Sequoia● CapitalKleiner Perkins ●Caufield & Byers Mayfield● Fund Draper Fisher ●Jurvetson (DFJ) 500 Startups● Accel Partners● Bessemer Venture● Partners Kleiner Perkins ●Caufield & Byers First ●Round DAG Ventures● New Enterprise● AssociatesLightspeed Venture● Partners Khosla ●Ventures Benchmark● SV Angel● Andreessen● Horowitz Menlo Ventures● Redpoint● Ventures Founders● Fund Redpoint● Ventures Norwest Venture● Partners Jafco Ventures●

U.S. Venture● Partners Felicis ●Ventures

First ●Round Bessemer Venture● Partners

(a) Venture stage network (2004-2009) with degree threshold set at 50 (b) Seed stage network (2010-2014) with degree threshold set at 70 and 4621 individual investment observations and 6270 individual investment observations

Figure 3.5: Silicon Valley venture stage co-investment network graph with one step community detection algorithm

36In particular, Kleiner Perkins, Sequoia Capital, U.S. Venture Partners (USVP), New Enterprise Associates (NEA), Bessemer, Norwest, Accel Partners and Mayeld.

149 3.7.3 Summary of results and limitations

From the above, we can see that the identied venture capital communities have dynamically changed over the past decades and that hypothesis 3 can thus not be rejected. Although the analysis performed was largely descriptive, it revealed that the nascent seed nancing segment has increasingly been occupied by founder-led venture funds. Later stage venture nancing rounds, on the other hand, were to a large part dominated by traditional decade old venture funds in the time frame between 2004 to 2009. is has changed over the ve years that followed this rst period, as founder-funder venture rms have emerged and managed to capture central positions in the venture network.

3.8 Mega fund hypothesis

To test the mega fund hypothesis empirically, it was explored whether the venture capital network converges to a power law distribution. e rise of mega funds is consistent with the preferential aachment and the rich-get-richer dynamics in social networks. ereby, the more connected a node is, the more likely it is to receive new links. Nodes with a higher degree have a higher likelihood to grab new links added to the network. Over multiple rounds, this leads to a network structure with a few nodes that are highly connected to other nodes in the network and many other nodes with only a few connection. e presence of mega funds will give the degree distribution a long tail, indicating the presence of nodes with a much higher degree than most other nodes. At the extreme, the network would converge to a super-star network, which is a network with N nodes and N−1 edges, where one node has a degree equal to N − 1 and all other nodes have a degree of 1 (Lieberman, Hauert, & Nowak, 2005). In other words, there would be one super venture rm and each of the other N − 1 nodes would only receive funding by this one rm.

3.8.1 Results

Hypothesis 3. Venture capital networks are dominated by a few dominant rms with the strongest network

To test hypothesis 3 empirically, a VC degree histogram is rst constructed, which graphically shows that most VC rms have very few connections and only a handful of funds in the tail of the distribution appear to be highly connected (see subgurea of gure 3.6). Based on this initial nding, it is explored whether the investment network converges to a power-law distribution with a probability density function (pdf) of the form:

α − 1 x −α p(x) = for x ≥ xmin (3.4) xmin xmin If a power-law distribution exists, the tail of such a distribution is fat, i.e. there tend to be disproportionately more nodes with very large degrees. Preferential aachment, going back to the seminal work of Barabasi and Albert(1999), is the network growth algorithm used to explain this phenomenon. e generative mechanism foresees that agents with higher connectivity (degrees) are more likely to capture new links added to the network (here successful investments), in other words a “rich-get-richer” eect exists. Using the R package poweRlaw, the VC degrees were ed to a power- law distribution (see subgureb of gure 3.6). When calculating the maximum likelihood estimator for the scaling parameter α, it was conditioned on a particular value of xmin, which required estimation. To estimate the lower bound xmin, the Kolmogorov-Smirno approach described by Gillespie(2015) was implemented. Applying this method, a threshold estimate of xmin = 5 was calculated with a scaling parameter of α = 1.85 and a Kolmogorov-Smirno statistic of D(5) = 0.081. Over the range 5 < x < 100, a good agreement between the power law best t line and the VC degree data was found. However, it can also be noted that the largest degree VC rms signicantly deviate from the t.

150 VC Degree Histogram CDF of VC Degree on Log−log Scale

200 0.500

150 0.200

100 0.050 P(> D) Frequency 0.020

50 0.005

0 0.002

0 50 100 150 200 1 2 5 10 20 50 100 200 VC Degree VC Degree (D)

(a) VC degree histogram (b) CDF plot of the VC degree generated from a power law t with parameters α = 1.85 and xmin = 5. e power law line of best t is the red line. e line of best t for the discrete log-normal with xmin set at 5 and 17 is represented by the blue line and the green line, respectively.

Figure 3.6: Silicon Valley investment network degree distribution.

To shed some more light on the goodness of the t, the sample was ed to another possible distribution, the discrete log-normal distribution. At rst, the Kolmogorov-Smirno approach was implemented for this distribution. is approach yielded xmin = 17 with a slightly beer Kolmogorov-Smirno statistic D(17) = 0.061. Graphically, it also appears that the discrete log-normal distribution is a beer t. However, as the xmin is much higher for the discrete log- normal distribution, the amount of data discarded has also increased. erefore, the data was also ed to the discrete log-normal distribution at xmin = 5. In this case, as the graph shows, the power law t appears to be a beer t. To conrm this intuition, a hypothesis test was conducted to see whether the power law provides indeed a beer t than the discrete log-normal distribution at an equal xmin. Testing the H0 that both distributions are equally far from the true distribution, it was found that this hypothesis can be rejected at the p = 0.00014 level. In conclusion, the empirical ndings suggest that hypothesis 3 cannot be rejected. In other words, there does seem to be a rich-get-richer eect at play in Silicon Valley venture networks, with a small number of VC rms being able to raise larger funds, make more investments and thus potentially capture a larger share of the high-quality deal ow.

3.9 Play-to-pay networks

To empirically analyze the play-to-pay dynamics described above, both small-scale and large-scale network eects are considered. In a rst step, a small-scale social and investment network relating to the so-called ‘PayPal maa’ is constructed. e aim of this is not to draw generalizable conclusions, but rather to exemplify the value provided by these close-knit networks and to provide some more nuance to the network dynamics that are typically at play in founder- funder venture networks. In a second step, a subset of venture rms are classied as founder-led or ‘founder-funder’ rms and it is examined in a large-scale network whether an investment of such venture rms has a statistically signif- icant eect on the nanced startups’ economic outcome.

3.9.1 Small-scale ‘PayPal maa’ network

One of the best known founder-funder networks has emerged around a group of co-founders and early employees of online payments processor PayPal, frequently also referred to as the ‘PayPal maa’. Originally established in 1998 as Connity by Peter iel, Max Levchine and Luke Nosek, the company PayPal has resulted from the early merger of Connity with X.com, Elon Musk’s payments startup at the time. As the rst tech company to go public aer the Dotcom crash, Paypal was acquired by Ebay a few month aer the IPO in October 2002. e company was later spun

151 out of Ebay in 2014 and now operates as a standalone public company. e ‘PayPal maa’ can be described as a social and investment network of former PayPal co-founders and early employees who have since founded and developed a number of highly successful ‘breakout’ technology startups, including rms like YouTube, Tesla, LinkedIn, Palantir, Yelp, SpaceX and Yammer. A number of individual members, such as Peter iel, Elon Musk, Reid Homan, or Keith Rabois, are notable gures in technology and venture circles and have been reported to have accumulated personal wealth in the USD billions in the process. e below gures set out a graphical representation of this PayPal maa network, which includes (i) startup rms, (ii) venture capital rms and (iii) individuals as nodes of the network and edges representing either (i) an angel or a VC investment or (ii) a founder relationship to the startup. e entire network consists of 254 edges and includes 23 founder-operator nodes, 12 venture rms, and 74 startups.

Startups Startups

VC firms PayPal Mafia founded startups

Members of PayPal Mafia VC firms

Members of PayPal Mafia

(a) Full PayPal maa network (b) PayPal maa network with node size representing the startup’s funding level

Figure 3.7: PayPal maa investment network graph with VCs colored light blue, startups colored limegreen, PayPal maa-founded startups colored dark green and PayPal maa members colored coral.

3.9.1.1 Outsized returns in the PayPal maa founder-funder network

e startups that have been founded or which have been nanced by members of the ‘PayPal maa’ have managed to raise substantial amounts of venture funding and achieved some of the largest exits in the Silicon Valley ecosystem. To show this graphically, funding levels of PayPal maa startups are ploed in a histogram next to the funding levels of the full population of venture-backed startups contained in the dataset.

152 500

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(a) Full population with 2620 individual investment observations and (b) PayPal maa-backed startups with 254 observations and investment threshold set at $150m investment threshold set at $150m

Figure 3.8: Histogram comparing funding levels of PayPal maa-backed startups funding levels with the full population and investment threshold set at $150m.

is graphical dierence becomes even more pronounced when the threshold is set to a higher level at $500m of total funding. While the full population of startups quickly ‘tampers out’ beyond the $100m funding level mark, PayPal maa startups count multiple instances beyond this cuto.

750 15

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(a) Full population with 2620 individual investment observations and (b) PayPal maa-backed startups with 254 observations and investment threshold set at $500m investment threshold set at $500m

Figure 3.9: Funding levels of PayPal maa-backed startups vs. full population with investment threshold set at $500m.

Figures 3.10a and 3.10b further graph these size dierences in boxplot histograms with threshold levels set at $2bn and $500m respectively.

153 500

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Full Population Paypal Mafia Full Population Paypal Mafia dataset dataset

(a) Investment threshold at $2.5bn (b) Investment threshold at $500m

Figure 3.10: Boxplot histograms with funding levels of PayPal maa-backed startups vs. full population at dierent investment thresholds.

To test these size dierences statistically, a Welch two-sample t-test was run to compare the funding levels of the PayPal maa small-scale network with that of the full population of venture-backed startups. e p-value associated with the test was 0.036 (t = 2.1348, df = 73.005), which means that the null hypothesis that there is no dierence between the average funding levels of the two groups can be rejected at the alpha = 0.05 signicance level. Also, a Wilcoxon rank sum test with continuity correction was run, which revealed a p-value of 2.718e-06, which further corroborates this nding.

3.9.1.2 Network-level dynamics

To add some color to the dierent drivers within the ‘PayPay maa’ network, the networking activities of some of the central individual actors within this network are highlighted below:

• Peter iel: As a co-founder of PayPal, Peter iel later went on to co-found Palantir Technologies (last valued above $20bn) with another PayPal maa member, Joe Lonsdale. As an angel investor, he famously wrote the rst check to Facebook and participated in the early rounds of Zynga, Stripe, Reddit, Addepar (co-founded by Joe Lonsdale) and IronPort Systems (co-founded by Sco Banister, another PayPal maa member) and ora. As an institutional investor, he co-founded Founders Fund with another PayPal co-founder, Ken Howery. rough this fund, he has invested more than $6bn in ‘unicorn’ startups, such as Airbnb, Ly, Spotify, Stripe and Oscar Health. Founders Fund was also the rst institutional investor to back SpaceX, a company co-founded by PayPal maa member Elon Musk.

• Elon Musk: As probably one of the best-known technology entrepreneurs of our times, Elon Musk has co- founded PayPal (through X.com), Tesla, SpaceX, Solarcity and has been the visionary behind Hyperloop One. While he has also angel-invested in a range of breakout startups, including Stripe, he has devoted most of his time and monetary resources to building frontier tech companies. For these companies, he has managed to raise substantial amounts of venture funding, in part through the ‘PayPal maa’ network. For example, Sco Banister, an early PayPal adviser, as well as Peter iel’s Founders Funds have been early personal and institutional investors in SpaceX. Similarly, David Sacks and Joe Lonsdale have co-invested in and co-chaired the Hyperloop One board.

• Reid Homan: Described in the past as the ‘Oracle of Silicon Valley’, Reid Homan founded one of the rst online social networks (SocialNet.com) before joining PayPal and later started and scaled the leading professional social network LinkedIn. He later angel-invested in Facebook, Airbnb, Zynga and IronPort Systems (co-founded by Sco Banister). He now is a partner at Greylock Partners, where he frequently co-invests with other PayPal

154 maa members: one example includes the startup Houseparty, where he co-invested alongside Chad Hurely, the co-founder of Youtube.

• Jeremy Stoppelman: As early vice president (VP) of engineering at PayPal, Jeremy Stoppelman later co-founded Yelp with another early Paypal employee, Russel Simmons. Yelp was incubated in a startup studio, MRL Ventures, which was founded by PayPal co-founder Max Levchin. As an active angel investor, Jeremey Stoppelman has since actively co-invested with a number of other ‘PayPal maa’ members, with his investments including Uber (Sco Banister, Kevin Hartz, David Sacks), Lever (Keith Rabois), Honor (Max Levchin), Envoy (Yishan Wong), Udemy (Keith Rabois), Opendoor (founded by Keith Rabois, co-investment with David Sacks, Max Levchin) and Airbnb (Jawed Karim, Kevin Hartz, Reid Homan, Keith Rabois).

All of the above examples point to a wide range of social and economic founder-funder interactions, which indicate numerous potential advantages of founder-led venture funds in identifying and securing allocations in high quality deal ow.

3.9.2 Innovation recycling hypothesis

While the small-scale ‘Paypal maa’ network ‘case study’ above is helpful for understanding the many social interactions in founder-funder networks, it provides local ndings that may not necessarily hold true outside of the narrow Paypal network. us, hypothesis 4 seeks to identify a generalizable founder-funder eect in the main specication and is formulated as follows:

Hypothesis 4. Founder-led venture rms have beer access to higher quality deal ow, resulting in higher fund returns.

To test this empirically in the main specication, venture rms are classied as ‘founder-funder venture rm’, if a venture rm meets the following criteria: (i) the rms has been founded in 2004 or later (to fully capture the post-Dotcom ‘innovation recycling’ eect) and (ii) if one of the founding partners of the venture rm has previously founded a startup (with a notable exit) or joined a ‘breakout’ technology startup as an early operating employee. is founder-funder VC classication has been included as a binary variable in the main specication. To identify founder-funder venture rms, the professional and personal backgrounds of the founding partners of the entire list of venture rms in the sample was manually researched online. In the below table, a list of the 15 venture rms that t the selection criteria and have thus been classied as founder-led venture funds is provided. Notably, the list includes three fund vehicles directly emerging out of the ‘PayPal maa’ network discussed above – Founders Fund, 500 Startups and Formation 8. From this list, venture rms that have reached considerable fund sizes and assets under management (AuM) include Andreessen Horowitz ($2.75bn AuM across 6 fund vintages), Khosla Ventures ($2.9bn AuM across 9 fund vintages), Founders Fund (c. $6bn AuM across 8 fund vintages) and First Round ($738m AuM across 7 fund vintages).

155 Name Founded Founders/Operators Companies founded/joined early 500 Startups 2010 Dave McClure Paypal Andreessen Horowitz 2009 Ben Horowitz, Marc Andreessen Netscape, Opsware Felicis Ventures 2006 Aydin Senkut Google (rst product manager) First Round 2004 Josh Kopelman Half.com Floodgate Fund 2006 Mike Maples Tivoli Systems, Motive Formation 8 2011 Joe Lonsdale Paypal, Palantir, Addepar Founders Fund 2005 Peter iel, Ken Howery, Keith Rabois Paypal, LinkedIn, Slide, Square, Palantir Javelin Venture Partners 2008 Jed Katz, Noah Doyle Rent Net, Mypoints.com Kapor Capital 2009 Mitchell Kapor Lotus Khosla Ventures 2004 Vinod Khosla Sun Microsystems Opus Capital 2005 Dan Avida Electronics for Imaging SV Angel 2009 Ron Conway Altos Computer Systems, PTS Social Capital 2011 Chamath Palihapitiya Facebook (early employee) True Ventures 2005 Toni Schneider, Tony Conrad, Kevin Rose about.me, Sphere, Automaic, Digg Y Combinator 2005 Paul Graham Viaweb

To test this hypothesis, the founder-funder classication factor Fl is added to the econometric network model specication of hypothesis 1, with all other factors remaining as dened before:

Si,j,k,l = α + βjIi,j + βkXi,k + βkFi,l + i,j,k (3.5)

Table 3.2 summarizes the results from a logistic regression for this model under dierent specications. e results indicate that the founder-funder nature of venture funds signicantly predicts startup success rates, if this classication is included as a binary variable (β = −.025 at the p < .001 level). ese results also hold up when an ordinary least square regression with heteroskedasticity-robust standard errors is run. However, contrary to the conjecture, the direction of this relation is not positive, but negative. e results therefore suggest that hypothesis 4 has to be rejected. A number of factors could explain these ndings. Firstly, venture capital fund performance is typically believed to follow a J-Curve,37 meaning that there is a tendency of funds to require substantial outows of capital in the initial years as the portfolio is still being built up and then increasingly return capital in later years, as investments mature and exits are being realized (Sahlman, 1990). Compared to private equity funds, this J-Curve eect is even more pronounced for venture funds, as the holding period and time-to-exit tends to be longer. Secondly, since founder-funder venture rms typically invest at the earliest stages (Seed to Series A vs. late-stage venture or growth), it may take even longer before an exit can be realized. To illustrate these two points, some of the founder-led venture rms have recognized spectacular ‘fund-returning’ exits on their investments in the time aer the cuto of the observation period in 2014. Andreessen Horowitz (a16z), for example, has participated in four IPOs in 2019 alone, including the companies Ly, Pinterest, Slack and PagerDuty. Notably, all of these are not recognized as ‘successful exits’ in our data set, as their outcome was still uncertain at that time. In the same vain, Friend(2015) has reported on the unrealized remaining value, the so-called ‘Residual Value to Paid-in-Capital’ (RVPI), of Andreessen Horowitz’s Fund I and II in 2015, shortly aer the end of our observation period.38 Lastly, it may indeed be the case that founder-led venture rms are under-performing compared to more established rms. One reason for this may be that these rms invest in the earliest stages, oen pre-product-market-t, were the risks are larger and there is less signal. More established rms, on the other hand, would in the past increasingly ‘sit on the fences’ and deploy capital only when there was more signal and traction.

37See Kupor(2019) (‘e eect of calling capital from LPs in the early years coupled with the long gestation cycles for companies to grow and ultimately exit – in many cases it takes ten or more years for companies to be sold or go public – creates what is known as the ”J curve”’.). 38See Friend(2015) (‘Its rst fund has already returned 2x, and contains such powerhouses as Slack and the identity-management company Okta. e fund’s internal rate of return, a calculation of annualized prot, is y per cent, which places it very high among funds raised in 2009. (Sequoia’s rate for its corresponding fund is sixty-nine per cent.) e rm’s second fund includes Pinterest and Airbnb, and its third fund includes Zenets, GitHub, and Mixpanel; both funds, on paper, are well into the black.’).

156 Table 3.2: Regression Results

Dependent variable: Startup Success (1) (2) (3) (4) Investment by VC -0.013 -0.015 -0.013 -0.015 (0.023) (0.023) (0.023) (0.023)

Total VC Investments -0.053∗∗∗ -0.051∗∗∗ -0.053∗∗∗ -0.053∗∗∗ (0.006) (0.006) (0.006) (0.006)

VC Degree 0.173∗∗∗ 0.182∗∗∗ 0.204∗∗∗ (0.020) (0.031) (0.032)

VC Betweenness 0.123∗∗∗ -0.011 -0.024 (0.019) (0.029) (0.030)

founder-led VC -0.025∗∗∗ (0.008)

Constant -1.529∗∗∗ -1.046∗∗∗ -1.549∗∗∗ -1.585∗∗∗ (0.081) (0.043) (0.097) (0.098)

Observations 8,961 8,961 8,961 8,961 Log Likelihood -4,821.166 -4,839.313 -4,821.096 -4,816.774 Akaike Inf. Crit. 9,650.331 9,686.627 9,652.191 9,645.548 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

3.10 Conclusion

Within the scope of this chapter, dierent network measures and eects have been analyzed empirically for Silicon Valley venture capital networks of past decades. Firstly, the position of venture rms in an investment network constructed from a Crunchbase data set collected for the period between 2004 and 2014 was empirically related to the venture rms’ investment performance. Consistent with prior empirical studies, in particular Hochberg et al.(2007), a strong positive eect of network centrality measures on the investment outcome at the startup rm level was identied. Under the second hypothesis, it was explored whether the ‘inner circle’ of venture rms reaches a static network equilibrium or whether venture networks are dynamic over time, as ‘entrepreneurial venture rms’ seek to obtain a beer position within the network. It was established that, as new venture rms enter and incumbents exit, central players in venture networks do indeed vary over time and investment stages. Furthermore, two disparate eects in the data set were identied, which have shaped the venture industry at dierent ends of the funding spectrum, (i) the rise of ‘mega funds’ at the late stages and (ii) the emergence of ‘founder-funder’ VCs at the early stages. Firstly, as the venture capital industry has matured as an asset class over the past decades, late stage funding has been dominated by ‘mega funds’, which oen raise funds with LP commitments in the billions. Most of these ‘power players’ are venture rms that have existed over multiple decades and thus have long track records, which provide them with an advantage to raise more sizeable funds from institutional investors. Given the growing fund sizes, these rms are able to write more and larger checks, thereby potentially monopolizing the prime (late stage) deal ow. For these mega funds, a ‘rich-get-richer eect’ among the population of Silicon Valley venture funds was hypothesized. e results indicate that such an eect may indeed be at play, with a few central rms funding disproportionately more startups. In addition, over time, this two-tiered network structure seems to have become even more exacerbated. Secondly, at the earlier funding stage, the emergence of a new breed of venture rms was analyzed. ese venture rms have been founded and are led by what is referred to here as ‘founders-funder’ venture capitalists. is new class

157 of fund managers has emerged in the post-Dotcom era, oen times placed at the center of the technology and venture network through a rst successful exit during the Dotcom era (‘innovation recycling’). As conrmed through the data set, these venture rms typically invest at the earlier stages of startups’ nancing cycles (Seed to Series A). Within the scope of this paper, the social and investment network eects of ‘founders-funder’ VCs are analyzed further, both by (i) studying the small-scale ‘PayPal maa’ founder-funder network and (ii) through a classication of venture funds as founder-led in the main specication. e analysis of the small-scale ‘PayPal maa’ network points to strong advantages of such founder-led funds across multiple social and funding channels. However, for our data set and observation period, this does not seem to translate to a generalizable nding across the wider venture network. In particular, the involvement of founder-led venture rms in the main specication does not seem to signicantly drive startup performance. To the contrary, in the main specication it appears that founder-funder VCs are signicant, but negative predictors of startup success. In summary, this chapter conrms ndings of prior studies on venture capital and contributes to the literature by highlighting recent phenomena at a more granular level, such as the rise of ‘mega funds’ and ‘founder-funder’ funds, which have not been documented to date.

158 Chapter 4

e Nature of the Banking rm

159 4.1 Introduction

Credit allocation in modern nancial systems is dominated by large banking rms, which take deposits and originate loans through the bank’s balance sheet. In contrast, the allocation of credit through nancial markets is still nascent and underdeveloped. is chapter investigates the central role played by regulation in maintaining the dominance of banking rms in credit and asks how the emergence of novel credit markets could be fostered through regulatory reforms. e analysis is guided by a law and economics theory developed in the rst chapter of this PhD thesis, the Coase eorem of Securities Regulation (the ‘eorem’). By applying the theory in the context of credit, the chapter explores how (i) competing securities and banking regulations eectively price most credit assets out of nancial markets and into banking rms and how (ii) the regulatory costs of securities regulation could be re-allocated among stakeholders to foster a market-based credit system.

4.2 Bank-based vs. market-based systems

Over the past decades, economists have developed a vast number of theoretical and empirical insights into the comparative advantages of dierent nancial systems. Nevertheless, the longstanding policy debate around the question of whether we should promote bank-based or market-based nancial systems remains open as policymakers continue to struggle with the relative merits of the competing systems.

4.2.1 Bank-based systems

Proponents of the bank-based view have highlighted the cost advantages of banks in acquiring information about rms and agents, which allows them to improve corporate governance and optimize the capital allocation.1 Other proponents have pointed to benets of banking rms when it comes to managing cross-sectional credit and liquidity risks over time (intertemporal risk smooting)2 and their ability to shi the composition of savings to growth-promoting segments.3 Another stream of the literature has focused on the economies of scale when mobilizing capital.4 e bank-based proponents have also stressed the shortcomings of market-based systems. Bencivenga and Smith(1985) points to the quick dissemination of information in markets, which lets market participants free-ride on market information, thus reducing the incentives for individual investors to acquire information.5 Banks, on the other hand, can mitigate this problem due to their opaque nature and their long-term relationships with agents.6 Boot and akor(1997) furthermore argue that by pooling investors, banking rms are beer positioned than markets to coordinate and monitoring

1See Diamond(1984) (‘An intermediary (such as a bank) is delegated the task of costly monitoring of loan contracts wrien with rms who borrow from it. It has a gross cost advantage in collecting this information because the alternative is either duplication of eort if each lender monitors directly, or a free-rider problem, in which case no lender monitors’); Ramakrishnan and akor(1984) (‘is tendency to centralize information production can be viewed as an explanation for the emergence of the traditional pure broker as described in the literature on nancial intermediation.’); See F. Allen and Gale(1990) (‘Intermediated nance is best when costs of information are high and there is not much diversity of opinion. e project is not nanced if there is diversity of opinion and costs of information are high. […] On the other hand, bank-nanced projects will be characterized by uniformity of opinion and the technologies they use will be relatively expensive to asses.’). 2See F. Allen and Gale(1997)(‘A commonly heard argument is that nancial markets are desirable because of the risk-sharing opportunities they provide. It is well known that this is correct as far as cross-sectional risk-sharing opportunities are concerned, but the results of the preceding sections suggest that this argument ignores the possibilities for intertemporal risk smoothing. We have shown in the context of a simple OLG model that an intermediated nancial system can make every generation beer o than it would be with nancial markets alone.’). 3See Bencivenga and Smith(1991)(‘Conditions are provided under which the introduction of intermediaries shis the composition of savings toward capital, causing intermediation to be growth promoting.’). 4See Baltensperger(1972) (‘Exploitation of economies of scale due to uncertainty is in some sense “raison d’etre” for banks. Banks are nancial intermediaries consolidating risk by having as assets the debt of a large number of dierent people independent in their solvency, and having as liabilities deposits of a large number of independently acting depositors. is permits them to hold relatively small amounts of liquid reserves against their liabilities and the associated risk of cash-drains, and relatively small amounts of capital account against their assets and the corresponding risk of capital losses and bankruptcy.’). 5See Bencivenga and Smith(1985) (‘On the other hand, it is not in the interests of any shareholder or small lender to devote much aention to the performance of a rm; for any gains that accrue to him as a result of his actions accrue to all similarly situated suppliers. ere is the free-rider problem which we discussed earlier.’). 6See Boot, Greenbaum, and akor(1993) (‘e modern literature on nancial intermediation has primarily focused on the role of banks as relationship lenders. In this capacity, banks develop close relationships with borrowers over time. Such proximity between the bank and the borrower has been shown to facilitate monitoring and screening and can overcome problems of asymmetric information.’).

160 rms, in particular when it comes to reducing post-lending moral hazard (in particular asset substitution).7 Bank-based arguments also regularly stress the lack of strong governance in liquid capital markets.8 In perfectly liquid capital markets, investors can always sell their assets, so that fewer incentives exist to exert corporate control. In other words, bank-based views stress that market-based systems are ill positioned to foster corporate control and economic growth. Rajan and Zingales(1998) make the argument, that in countries with weaker contract enforcement capabilities, dominant banking rms can be more eective at forcing debt repayment of rms compared with credit markets.9 Markets may be too atomistic, lacking the monitoring capabilities of powerful banks. us, external investors may be reluctant to nance industrial expansion in countries through credit markets with underdeveloped institutions. To summarize the above, the bank-based view holds that banks may be beer positioned than markets to allocate credit, due to scale economies in information processing, long-term credit relationships that reduce informational frictions, beer reduction of moral hazard and asset substitutions through tighter monitoring capabilities. As a result, they hold that bank allocation can boost economic growth more eectively than credit markets.

4.2.2 Market-based systems

In contrast, proponents of the market-based view point to the growth enhancing role of well-functioning capital markets through a range of channels. Holmstrom and Tirole(1993) have pointed to the fact that deep, liquid capital markets serve as an incentive mechanism for investor to acquire information, since they can nancially benet from such research.10 Others have pointed to the benets of capital markets when it comes to facilitating risk management.11 M. Jensen and Murphy(1990) have argued that capital markets enhance corporate governance through takeovers and tying managerial compensation to rm performance.12 Like the bank-based view, the market-based view also stresses problems with the allocation through the banking rm. One set of market-based proponents have argued that powerful banks may stymie innovation by providing large borrowers, which have close bank ties, with non-competitive rates and by extracting informational rents.13 Others have argued that in the absence of regulatory restrictions, powerful banks may engage in collusive behaviour with rm managers against other creditors.14 Market-based views hold that in contrast to this, transparent capital markets are more ecient at diusing market signals to investors, which can boost aggregate - nancing levels and economic growth.15 us, proponents of the market-based view stress that markets will reduce the inherent ineciencies associated with banks and enhance economic growth.

7See Boot and akor(1997) (‘Banks arise as coalitions of agents who coordinate their actions to resolve asset-substitution moral hazard.’). 8See Bhide(1993) (‘e analysis in this paper suggests that enhanced market liquidity has come at a price. Rules that now fragment intermediaries’ holdings prevent them from playing a meaningful shareholder role and may actually have increased the concentration of power.’). 9See Rajan and Zingales(1998) (‘is paper suggests that relationship-based systems work well when contracts are poorly enforced and capital is scarce. Power relationships substitute for contracts, and can achieve beer outcomes than a primitive contractual system.’). 10See Holmstrom and Tirole(1993) (‘Like any successful institution, the stock market serves several purposes, many of them unforeseen at the time the institution was created. ere is lile doubt that the stock market was set up for other reasons than managerial monitoring; in particular, risk sharing and acquisition of capital were major benets. But it seems equally clear that the stock market today performs an important role as a monitor of management, both directly by assessing past contributions to value and indirectly as a market for corporate control.’). 11See R. Levine(1991) (‘Stock markets arise in this model to help agents manage liquidity and productivity risk, and, in so doing, stock markets accelerate growth.’); Obstfeld(1994) (‘is paper has demonstrated that international risk sharing can yield substantial welfare gains through its positive eect on expected consumption growth.’). 12See M. Jensen and Murphy(1990) (e threat of takeovers also provides incentives since managers are oen replaced following a successful takeover’). 13Hellwig(1991) (‘As cartelization increases the gross returns owing from industry to its nanciers, it will also improve the position of the enforcing banks in the competition for funds. In the absence of countervailing eects, the process of competition for funds should thus give rise to a banking industry which is concentrated or coordinated enough to impose cartel behaviour on its industrial clients, using the returns to aract deposits’) Rajan (1992) (‘is paper argues that while informed banks make exible nancial decisions which prevent a rm’s projects from going awry, the cost of this credit is that banks have bargaining power over the rm’s prots, once projects have begun. e rm’s portfolio choice of borrowing source and the choice of priority for its debt claims aempt to optimally circumscribe the powers of banks.’). 14Wenger and Kaserer(1998)) (‘Obviously, banks may use their voting power to appoint bank representatives as members of the supervisory board, and thereby exercise control over the management.’). 15See F. Allen and Gale(1990) (‘e model implies market-nanced projects will be characterized by considerable diversity of opinion about their likely commercial success and the technologies they are based on will be relatively cheap to assess.’); Boot and akor(1997) (‘A key aribute of the nancial market, and one that delineates its role from that of a bank, is that there is valuable information feedback from the equilibrium market prices of securities to the real decisions of rms that impact those market prices. is information loop provides a propagation mechanism by which the eects of nancial market trading are felt in the real sector. Bank nancing does not have such an information loop.’).

161 4.2.3 Contribution of this chapter

is chapter adds to the above literature by stressing the role of the legal system in shaping the respective allocation form. In particular, the chapter highlights the dierential costs that securities regulation on the one hand, and banking regulation on the other hand, put on credit transactions. ese dierential regulations can price certain transactions into and out of markets or banking rms, respectively. e chapter further explores key policy levers through which credit markets can be optimized across its dierent functions.

4.3 Coase eorem of Securities Regulation

e Coase eorem of Securities Regulation (hereinaer the ‘eorem’) is a novel law and economics theory of securities regulation. Based on the foundational work of Ronald Coase and Guido Calabresi, it develops a novel perspective on securities regulation, which allows us to have a beer understanding of the role of security laws on the formation of rms and markets. e theory breaks securities regulation into three functional layers: disclosure, liquidity and diversication. Under the rst part of the theory, it analyses how security laws can lead to dierential pricing between the rm and the market allocation. Under the second part, it re-conceptualizes the costs of the market and in particular securities regulation as an externality and considers the optimal assignment of these costs at each functional layer.

4.3.1 e three functional layers

e eorem introduces the concept of the ‘three functional layers of securities regulation’, which act as the main units of the regulatory analysis. ese three layers follow a hierarchical and sequential logic. At the base layer, which is referred to as the ‘disclosure and information layer’, the issuer of a security, either required by securities regulation or voluntarily, discloses information to investors. Once the security has been issued, it is placed in the public market by an underwriter and actively traded on a securities exchange. is set of activities and functions is referred to as the ‘investment and liquidity layer’. Lastly, as investors assume idiosyncratic risk by investing into the security of a single issuer, modern portfolio theory dictates that they should diversify across multiple issuers. is diversication at of the buy-side is referred to as the ‘diversication layer’.

4.3.1.1 Disclosure and information layer

Economically speaking, a security always involves a ow of funds from:16

• a surplus agent (investor or depositor, with net assets); to

• a decit agent (individual or rm, with net liabilities).

Within the scope of the credit seing of this chapter, surplus agents refer to creditors or depositors. On the other hand, decit agents refer to bank borrowers or credit market issuers. e eorem denes a disclosure as a one-sided, unilateral ow of information from the decit agent to the surplus agent. In the credit market seing, the disclosure layer refers to disclosures mandated by the SEC and produced by the issuer. In a banking rm seing, the disclosure layer refers to the disclosures made between borrowers and the bank and between the bank and depositors, respectively. However, the disclosure and information layer is not constrained to disclosures, but is dened more broadly, going beyond the traditional focus of mandatory disclosures. In particular, it covers the entire information and data ow between (i) surplus agents, (ii) decit agents and (iii) third-party data providers.

16See Boot and akor(1997) (using a similar terminology ‘A primary function of the nancial system is to facilitate the transfer of resources from savers (“surplus units”) to those who need funds (“decit units”).’).

162 4.3.1.2 Investment and liquidity layer

Whereas the disclosure and information layer governs the ow of information, the investment and liquidity layer governs the ow of funds between creditors and borrowers. e investment and liquidity layer can be further broken down into two separate sub-functions:

• primary market activities; and

• secondary market activities.

Primary market activities relate to the initial ow of funds from surplus agents to decit agents. In credit markets, this is typically associated with the underwriting of bonds or the sponsor-led origination of ABS securities. Secondary market activities relate to the secondary ow of funds between surplus agents, which are typically facilitated by nancial exchanges and market makers. Under the rm allocation, ‘primary activities’ comprise the loan origination within the banking rm structure, while ‘secondary activities’ typically entail holding loans to maturity on the bank’s balance sheet.

4.3.1.3 Diversication layer

Whereas both the disclosure and the liquidity layer look at the individual credit issuer, the diversication layer is concerned with the pooling of economic exposure across multiple issuers. e essence of diversication is to shi economic exposure away from a single issuer towards multiple issuers. In credit, diversication traditionally takes place through the banking rm or through xed-income pooling vehicles, such as xed-income ETFs or mutual funds, which aggregate funds from investors and depositors and spread the economic exposure over multiple credit issuers.

4.3.2 First part of the eorem

In his seminal 1937 paper ‘e Nature of the Firm’,17 Coase asks the question: why do rms exist? Similarly, the rst part of the eorem asks, why do we see certain economic transactions allocated through the rm and others through the markets? Coase oers a number of reasons why rms may come into existence. e main argument put forward is that the parties encounter dierent costs, depending on whether they are operating through the rm or the market. In chapter 1 of this thesis, the distinction between baseline and regulatory costs under Coase is made. Under the rst part of the eorem, the focus lies on regulatory costs, in particular the costs of securities regulation and alternative rm-specic regimes. Within the scope of this credit-focused chapter, the alternative rm-specic regulation is bank regulation. Coase describes what is referred to as ‘regulatory costs’ here as follows:18

Within the rst part of the eorem, the chapter introduces a novel perspective on securities regulation which makes these laws an integral part of explaining why certain transactions are allocated through the rm or the market. us, it views the rm and the market as being governed by competing legal regimes, which impose dierent prices on economic transactions. In this regard, securities regulation determines the price of the market, while rm-specic regulation (in this chapter bank regulation) determines the regulatory costs of the alternative (bank) rm seing. Based, in part, on these regulatory prices, economic actors choose to allocate through the market or the rm. us, the observed allocation mechanism can be regarded as a representation of rational preferences of economic actors, given the existing legal seing.

17See Coase(1937). 18See Coase(1937).

163 4.3.3 Second part of the eorem

Under the rst part of the eorem, the costs of securities regulation and bank regulation are considered endogenous transaction costs. is is an adequate level of analysis, as the main focus there lies on comparing the costs of market regulation to the costs of rm-specic regulation (bank regulation in the context of this chapter). us, it does not require a level of granularity beyond that of the aggregate costs that are compared to the alternative regulatory regimes. is is dierent for the second part of the eorem established in chapter 1 of the thesis, which hones in on the costs of the market allocation. e core contribution of the second part is that it re-conceptualizes the costs of the market, including security laws, as externalities. By regarding these costs as externalities, the eorem allows us to analyze them under the traditional Coasean seing of ‘e Problem of Social Costs’.19 us, at its core, the second layer of the eorem is an application of the classical Coasean seing to the realm of securities regulation. In the classical ‘Coasean’ environment, a frictionless and purely theoretical seing, the parties are able to allocate the costs of the externality in a highly ecient manner. To recount, the original Coase eorem can be split into two main propositions:

• the eciency proposition; and

• the invariance proposition.

e eciency proposition states that in the absence of transaction costs, parties can overcome ineciencies otherwise caused by externalities. In other words, in a frictionless seing, the costs involved with the negative externality, such as the regulatory costs of transacting through the market can be optimally allocated between the parties involved. us, in the context of securities regulation, the issuer and the investor can engage in Coasean bargaining and optimally allocate the regulatory costs. e invariance proposition goes one step further and states that in a frictionless seing, the initial assignment of legal entitlements or obligations does not aect the eciency of the nal allocation of resources. In the context of securities regulation, this means that in a world without transaction costs, the assignment of rights and obligations by securities regulation to either the issuer or the investor does not aect the eciency of the nal allocation.

4.4 First part of the eorem

As outlined above, the rst part of the eorem views the rm and the market as being governed by competing legal regimes, which impose dierent prices on economic transactions. In this regard, securities regulation determines the price of the market, while banking regulation determines the price of the bank allocation. Based, in part, on these empirical regulatory ‘prices’, economic actors choose to allocate through the market or the rm. us, the observed allocation mechanism can be regarded as a representation of rational preferences of economic actors, given the existing legal regime.

4.4.1 Disclosure and information layer

e disclosure and information layer is concerned with the ow of information between creditor and credit issuer. e disclosure and information layer can be thought of as the foundational functional layer. e availability of data and information crucially determines the ‘upstream’ eciency of pricing and allocating credit at the investment and diversication layer, whether this is through the rm or the market. As will be shown in this section, while the market and the rm rely on similar data sources when it comes to credit, there exist substantial dierences with respect to the regulatory costs of producing this information.

19See Coase(1960).

164 4.4.1.1 Costs of the market

Where credit is allocated through the market, there exists a direct ow of information between the credit issuer and creditors. is makes intuitive sense, as creditors enter into a contractual relationship with borrowers. is exposes them, in the absence of government guarantees, to the risks and returns of the nanced credit asset. If the borrower defaults on the credit obligation, creditors take a loss. us, overcoming the informational asymmetries between credit issuer and creditors is crucial for the ecient functioning of credit markets.

4.4.1.1.1 Securities regulation

Where the credit transaction is allocated through the market, it is by default subject to mandatory disclosure obligations under federal security laws. In practice, as further outlined below, a large part of credit is placed in the market by way of exemptions and thus operates in the shadows of security laws. Crucially, when compared to the banking rm allocation, regulatory costs are imposed on each individual credit transaction. is makes the SEC-regulated credit market substantially more expensive from a disclosure perspective, as it introduces a high initial cost for new credit issuers and substantial marginal cost for every additional credit issuance. In other words, the disclosure obligations under security laws introduce a minimum threshold for borrowers, below which it is economically not viable to access credit markets. e eect of this threshold is that individual retail borrowers are completely priced out of the market. Even for small-and-medium sized corporations, the costs of accessing the public bond markets are oen prohibitively large. As a result, accessing credit markets on a single issuer basis, is largely a privilege of large corporations. Smaller credit issuers either access credit markets through exemptions from security laws (Regulation 144A or sovereign exemptions) or on a pooled basis (such as credit issuers in ABS pools).

4.4.1.1.2 Corporate bonds

When accessing SEC-regulated credit markets, corporate bond issuers provide the same kind of information in prospectuses for debt securities as they do for equity oerings. Regulation S-K20 sets forth the core disclosure requirements for prospectuses. e SEC publishes a range of registration statement forms, which incorporate the requirements owing from Regulation S-K, including most notably the annual report Form 10-K.21 Materially, these forms require corporate bond issuers to disclosure consolidated nancial information, including income statements, balance sheets and cash ow statements. In practice, a large portion of the corporate bond market operates as intra-institutional markets in the shadow of security laws. In particular, under Rule 144A of the Securities Act of 1933,22 bonds that are privately placed or sold in an otherwise exempt oering (such as under Regulation S23), may be re-sold to large institutional investors without registration. However, while exempt oerings may avoid some regulatory scrutiny, it should be noted that institutional 144A creditors typically require similar bilateral disclosures on a bilateral basis from credit issuers.

4.4.1.1.3 Structured credit transactions

As security laws were originally conceived in the sphere of corporate issuers, they have traditionally addressed corporate boards and management.24 us, in structured credit transaction, such as asset-backed securities (ABS), the absence of a management has initially posed a regulatory puzzle for the SEC.25 rough the no-action leer and registration review practice, a new regulatory framework gradually emerged to address the dierent nature of structured credit securities. is eventually culminated in Regulation AB,26 which was adopted by the SEC in 2004 and consolidated the

2017 C.F.R. §§ 229.10-.915. 2117 C.F.R. § 249.310. 2215 U.S.C. §§ 77a-77mm. 2317 C.F.R. §§ 230.901-905. 24See Hu(2012) (‘Longstanding SEC disclosure and reporting requirements were designed for corporate issuers and their securities, and were focused on such maers as the corporation’s business or management.’). 25See Hu(2012) (‘With ABS, there was no business or management. Instead, information about the characteristics of the asset pool, the servicing of the assets, and the transaction structure was oen what was most important to investors.’). 26Asset-Backed Securities, Securities Act Release No. 8518, Exchange Act Release No. 50,905, 70 Fed. Reg. 1506, 1506 (Jan. 7, 2005).

165 prior practice.27 Hu(2012) notes that Regulation AB relies on the traditional ‘intermediary depiction model’, where a corporate issuer is required to ‘cra and transmit depictions of reality’. In the context of structured credit, the SEC prescribes the content and narrative of such depictions to structured credit sponsors.28 Prior to the nancial crisis, disclosure and information requirements for asset-backed securities (ABS) registered with the SEC were rather lenient, not requiring issuers to disclose asset-level data.29 Instead, private-label ABS sponsors, most notably investment banks, were required to provide highly aggregated data about the composition and characteristics of the asset pool.30 In the wake of the nancial crisis in 2008, it appeared that many investors and other market participants, including rating agencies, had materially failed to understand the underlying risks of ABS and were unable to properly value them based on the existing disclosures.31 e inability of the market to properly price asset-backed securities ultimately also led to the demise of Lehman Brother, which had used its ABS portfolio in repurchase agreements to nance its operations.32 When the market failed to properly price ABS and counterparties no longer accepted these securities as collateral, Lehman was forced to declare Chapter 11. As a result of these failures at the disclosure layer during the nancial crisis, Section 942(b) of the Dodd-Frank Act added Section 7(c) to the Securities Act, which required the SEC to adopt regulations requiring ABS originators to disclose, for each tranche or class of securities, information regarding the underlying assets, including asset-level and loan-level data.33 Following this mandate, the SEC developed asset-level disclosure requirements in Regulation AB II, which became eective in November of 2016.34 Materially, Regulation AB II requires sponsors of SEC-registered ABS securities, through Schedule AL,35 to provide loan-level credit data through Asset Data Files in XML format (a machine- readable language). ese full technical specications clearly dene the data schemata and each required loan-level data point.36 Crucially, while the private ABS market has generally suered aer the nancial crisis, it appears that implementation of Regulation AB II shut down what was still le of the SEC-registered ABS market. SEC Chairman Jay Clayton recently announced that, following the enactment of the new disclosure rules in 2014, not a single SEC-registered RMBS oering has been made and the mortgage-backed ABS market has instead been dominated entirely by securities issued by government-sponsored enterprises (GSE):

‘[…] since the Commission revised its ABS rules in 2014, no SEC-registered RMBS oerings have taken place. By contrast, in the ve years ended June 30, 2019, Fannie Mae and Freddie Mac have issued an aggregate of approximately $4.47 trillion in face amount of RMBS.’37

In other words, stringent disclosure requirements have priced the remainder of these ABS transactions fully out of the SEC-regulated credit markets. In contrast to private ABS, securities issued or guaranteed by Freddie Mac or Fannie Mae are exempt from the regis-

27See Hu(2012) (‘Currently,and extending back to the period prior to the GFC, the disclosure requirements applicable to registered ABS oerings ow primarily from Regulation AB, adopted by the SEC in 2004.’). 28See Hu(2012) (‘Consistent with how the intermediary depiction model operates with regard to corporations and corporate issuances of securities, the SEC identies explicit items that should be covered in such depictions of ABS and oers guidance as to both the substantive content and its narrative, numerical, and graphical presentation.’). 29Hu(2012) (‘ABS issuers typically have substantial amounts of relatively pure information about their pool assets, most of which is not depicted to investors. In particular, an ABS issuer is required to disclose only certain information, and only at the aggregated level.’). 30Item 1111 of Regulation AB [17 C.F.R. 229.1111]. Hu(2012) (‘In terms of pool assets, the core Regulation AB provisions are Items 1111 and 1105. Item 1111 requires the issuer to provide broad information regarding the asset pool types and selection criteria. […] Item 1105 of Regulation AB also requires the disclosure of what is referred to as ”static pool” information, ”[u]nless the registrant determines such information is not material.”). 31See Schwarcz(2008) (‘In the subprime mortgage crisis, there is to date relatively lile dispute that the disclosure documents describing the MBS, CDO, and ABS CDO securities and their risks generally complied with the federal securities laws. e complexity of the transactions, however, caused the disclosures to be insucient, cuing into the very heart of federal securities regulation, whose ”exclusive focus is on full disclosure.’). 32See R.-R. Chen, Chidambaran, Imerman, and Sopranzei(2014) (‘Furthermore, it is also now known that Lehman Brothers was not only pledging the high quality, highly liquid assets that are usually assumed to be involved in repo transactions (i.e., Treasuries, Agencies, etc.), but was also pledging highly risky securitized products, some of which were structured by Lehman themselves and issued by special purpose vehicles that they set up and owned.’). 33Section 7(c) of the Securities Act [15 U.S.C. 77g(c)]. 34Securities and Exchange Commission (SEC). 2014. Asset-backed securities disclosure and registration, Final Rule. 17 C.F.R parts 229, 230, 232, et al. Federal Register 79 (185). 3517 C.F.R § 229.1125. 36Available under hps://www.sec.gov/info/edgar/specications/absxml.htm. 37See Clayton(2019).

166 tration requirements of the Securities Act under their legislative charters,38 and they do not le registration statements with the SEC for the oering of such securities. It should be noted that GSEs currently collect and disseminate asset-level information to the public. RMBS oerings by Fannie Mae and Freddie Mac generally disclose approximately 100 data points for each credit asset, while Regulation AB II requires 270 data points for each loan in an SEC-registered RMBS oering.39 However, as will be shown under the diversication layer, despite these substantial dierences in disclosure requirements, government guarantees are a more likely explanation for this shi in the ABS markets.

4.4.1.1.4 Peer-to-peer consumer loans

Peer-to-peer lending platforms originate unsecured consumer loans on a loan-by-loan basis. is niche credit market segment provides an interesting case study insofar as small notional amounts are originated through SEC-registered securities. In the law review article with the unequivocal title ‘e misregulation of Person-to-Person Lending’,40 Verstein (2011) analyzes the SEC’s application of security laws to the edge case of market-based lending. Verstein(2011) makes the normative argument that the SEC overreached its competence by armatively asserting that P2P loans were securities under Howey. In fact, the SEC’s decision to place P2P transactions under the scope of securities regulation had interesting second order eects. e small-lot consumer loan transactions, which were previously allocated through an unregulated market structure that enabled a direct contracting between borrowers and lenders,41 suddenly became subject to the SEC’s mandatory disclosure obligations. is meant that each transaction now required a costly prospectus and that, if the existing unregulated market structure was to be maintained, each individual borrower would eectively have to become an issuer. Materially, an SEC-registered prospectus for an unsecured consumer credit would contain the specics of the loan (amount, term, grade, intended use of funds), a credit score (such as most notably a FICO Score) and some information about other outstanding debts of the borrower.42 However, despite the sheer triviality of these single issuer credit disclosures, individual borrowers cannot be expected to le a prospectus with the SEC, given the complexity and rigidity of the ling format. Given the unreasonable costs of such a system, it was instead decided that the P2P platforms would act as issuers of securities and would ‘shelf-register’ multiple securities in advance of the actual transactions.43 Legally speaking, this means that P2P platform creditors now have the platform operators as their direct contracting counterparty, rather than the borrower.44 is, of course, introduces a further credit risk and thus has eectively re-transitioned these loans into a rm allocation structure.45 Instead of enabling markets, securities regulation thus had the opposite eect – strengthening the role of the rm over the markets.

4.4.1.2 Costs of the banking rm

e disclosure and information layer under the banking rm allocation looks substantially dierent from the market allocation. Under the market allocation, information ows directly between borrower and creditor. In contrast, under

38For Freddie Mac, 12 U.S. Code § 1455 (g), for Fannie Mae, 12 U.S. Code § 1717 (c)(1). 39See Clayton(2019). 40See Verstein(2011). 41See Verstein(2011) (‘In the early days of Prosper and Lending Club, the platforms made loans to borrowers and assigned those notes to lender- investors.199 Lenders experienced no credit risk with respect to the intermediary.’). 42See, as a comparison, what is disclosed under the ‘Standard Program Loans’ by peer-to-peer market leader Lending club, Lending Club(2019) (‘Borrowers come to our platform to apply online for a loan. During the simple application process, our platform uses proprietary risk algorithms that leverage behavioral data, transactional data and employment information to supplement traditional risk assessment tools, such as FICO scores, to assess a borrower’s risk prole.’). 43See Verstein(2011) (‘Under Rule 415, the issuer can register securities now, but not actually sell them until later when they ‘take them o the shelf’ on which they have been metaphorically waiting. Rule 415 also allows issuers to enjoy economies of scale in registration. It is much cheaper to register a bundle of securities and then take them o the shelf at intervals rather than to begin registration anew with each small security issuance.’ ). 44See Verstein(2011) (‘While P2P lenders once had credit exposure only to the underlying borrower, they are now unsecured creditors of the intermediary platform. Consequently, these lenders now risk platform default, which could leave them vying for a share of the P2P notes against other, more senior, creditors.’ ). 45See Verstein(2011) (‘Under Rule 415, the issuer can register securities now, but not actually sell them until later when they “take them o the shelf” on which they have been metaphorically waiting. Rule 415 also allows issuers to enjoy economies of scale in registration. It is much cheaper to register a bundle of securities and then take them o the shelf at intervals rather than to begin registration anew with each small security issuance.’).

167 the bank allocation, the information ow is short-circuited at the level of the banking rm, which is placed as a central hub between borrowers and depositors. On the loan origination side, a borrower taking out a loan from a bank does not share any data with the depositors who are nancing the mortgage, consumer loan or corporate credit facility. e borrower makes disclosures to the banking rm only. e borrower is completely le in the dark with respect to the identity and preferences of the credit suppliers and economic benecial owners of the originated credit assets. On the depositor side, the bank can also be regarded as a black box. Depositors are not informed about the nature and scope of loans that are nanced through their deposits. ey receive no data on the maturity and risk prole of these loans. ey are provided by the bank with a blank check ‘account balance’, which does not appear to uctuate with the economic exposure of the nanced credit pools. At the end of the year, depositors receive a blended interest rate. What is masked by these blank check bank-depositor disclosures is the fact that the demandable debt of depositors provides the credit supply for the bank’s balance sheet, nancing a specic pool of loans with variable returns. In other words, when credit is allocated through the banking rm, the disclosure and information layer is characterized by high levels of opacity. e buck stops with the bank. While credit markets pride themselves for their transparency, it is a generally accepted principle that banks silo relational credit data. In many ways, the closed nature of the bank disclosure regime is a direct consequence of the core economic function and contractual architecture of banking rms: nancing long-term credit with short-term demandable debt. In other words, under the banking rm allocation, maturity-matched debt is split into two separate contracts. Given this contractual division, the banking rm can restrict itself to making bilateral disclosures under its contractual obligations. And the bank can aord to be opaque, as there is no pressure from creditors to make more substantial disclosures. In the presence of the federal deposit insurance scheme, depositors have no incentive to monitor the bank’s loan portfolio or to require more granular loan level data (see legal diversication below). is disclosure regime results in substantial structural and regulatory cost advantages under the bank allocation. In particular, the rm-specic regulation of banks allows the loan-level disclosures to be made in an informal, relational context. ese disclosures are analyzed within the banking rm and disclosed to bank regulators in an aggregated form only. is substantially decreases the marginal cost of every originated loan. It does not, however, mean that the nature of the loan disclosures materially diers from the credit market allocation or that this disclosure regime is without costs.

4.4.1.2.1 Loan-level disclosures

It is important to note that disclosures made by borrowers in the course of bank-based loan originations do not dier materially from disclosures made in SEC registration statements:

• Corporate credit: Bank loans made to corporations require corporate borrowers to disclose their consolidated nancial statements, including balance sheet, income statement and cash ow statement. is does not dier materially from what the SEC requires from corporate bond issuers.

• Asset-backed consumer loans: Similarly, for collateralized consumer loans, borrowers are required to make asset- and loan-specic disclosures, similar to what the SEC requires under Regulation AB II.

• Individual consumer loans: Lastly, with respect to unsecured consumer loans, the bank is likely to request a credit report and credit score from one of the three main credit bureaus, Equifax, TransUnion or Experian.

us, the data basis of the banking rm does not dier materially from what is supplied by borrowers accessing the credit markets. What diers under the rm structure is that this data is not disseminated outside of the banking rm.

4.4.1.2.2 Regulation of bank disclosure regime

Bank regulations require banks to disclose information about the originated loans and the rm’s aggregate credit exposure to the bank regulator. While this does trigger substantial regulatory costs for the banking rm, these mandatory

168 disclosure requirements arise on an aggregate loan level. us, rather than having to make disclosures for every single loan, as required under security laws, the bank can report aggregate holdings instead. A new loan origination does not trigger a securities registration. us, under the rm allocation, the regulatory costs can be spread more eciently across the entire loan portfolio, resulting in regulatory economies of scale. To comply with the regulatory requirements, banks have to consult their internal data, aggregate them into consumer data, risk data, counterparty data, nance and treasury data and general ledger data. ey then have to process these dierent datasets for stress tests, risk reports and a number of consolidated account lings with regulators. For example, without being comprehensive, bank holding companies in the United States, are required to le the following reports with the Federal Reserve:

• FR Y-9C:46 Consolidated Financial Statements for Holding Companies is ‘basic’ report, which was rst introduced in 1978, collects basic nancial data on a quarterly basis from banks on a consolidated basis in the form of a balance sheet, an income statement, and detailed supporting schedules, including a schedule of o-balance sheet items.

• FFIEC 101:47 Regulatory Capital Reporting for Institutions Subject to the Advanced Capital Adequacy Framework is report collects data on a quarterly basis from banks on the components of an institution’s capital and risk- weighted assets, where the regulated bank is subject to the Advanced Capital Adequacy Framework under the Basel Standards.

• FRY-14A:48 Capital Assessments and Stress Testing is annual stress test report was introduced in 2009 as a reaction to the global nancial crisis in an aempt to strengthen regulatory oversight. It collects information from large bank holding companies with total consolidated assets above USD 100 billion. e reporting comprises quantitative projections for balance sheet assets and liabilities, income, losses, and capital across a range of macroeconomic scenarios and information on the specic methodologies used to develop internal projections.

As demonstrated by the exemplary list of regulatory lings above, bank disclosures are on an aggregate basis only. In other words, while the aggregate regulatory disclosure costs of the banking rm may be substantial, the marginal costs are much lower than under the credit market allocation, where security laws place a disclosure cost on every single transaction.

4.4.1.2.3 Comparative pricing

From the above, it appears that the regulatory costs put on the individual credit transactions dier substantially depending on whether they are allocated through the rm or the market. In the credit market, every single credit origination triggers a security registration requirement. As a result, the marginal costs of a new loan origination is substantial. As the peer-to-peer niche credit market demonstrates, SEC registration requirements make it impossible for individual borrowers to issue credit with retail-sized notionals. Furthermore, the asset-backed securities (ABS) market has fully grinded to a halt as a result of stringent disclosure requirements. Even for corporate debt, a large portion of the credit market operates in purely intra-institutional markets through exemptions form security laws. e banking rm, on the other hand, can internalize much of the costly disclosure layer within the rm. It receives credit requests from borrowers, prices and directly underwrites loans internally, without having to make any disclosures to its depositors. Instead, depositors are presented with blank check account balances and an annual blended interest rate. In other words, the disclosure cost savings under the rm allocation are substantial. Even in the face of tightening

46 Required by Section 5(c) of the BHC Act (12 U.S.C. § 1844(c)), section 10 of Home Owners’ Loan Act (HOLA) (12 U.S.C. § 1467a(b)), section 618 of the Dodd-Frank Act (12 U.S.C. § 1850a(c)(1)), section 165 of the Dodd-Frank Act (12 U.S.C. § 5365), and section 252.153(b)(2) of Regulation YY (12 C.F.R. 252.153(b)(2)). 47As required by 12 C.F.R. Part 3 (OCC); 12 C.F.R. Part 217 (Federal Reserve); 12 C.F.R. Part 324 (FDIC). 48As required by section 165 of the Dodd-Frank Act (12 U.S.C. 5365) and section 5 of the Bank Holding Company Act (12 U.S.C. 1844).

169 regulatory scrutiny, banking rms are still able to make disclosures on a consolidated basis. By allowing the banking rm to realize regulatory economies of scale, this eectively reduces the marginal disclosure costs of a loan to competitively low levels.

4.4.2 Investment and liquidity layer

Whereas the disclosure and information layer governs the ow of information between borrower and creditor, the investment and liquidity layer governs the ow of funds between the two. e investment and liquidity layer can be further broken up into primary market activities, which govern the primary ow of funds from creditors to borrowers, and secondary market activities, which govern intra-creditor liquidity. In practice, primary market activities typically involve an underwriter rm, which provides the borrower with initial credit nancing with the intention of re-selling credit exposure as securities into the credit markets. Secondary market activities on the other hand typically involve market makers, regulated nancial exchanges or alternative OTC platforms through which credit securities can be traded amongst creditors. Under the bank allocation, originated loans are underwrien by the banking rm and are then held-to-maturity on the bank’s balance sheet. e bank’s balance sheet is nanced by short-term demandable debt from depositors and wholesale funding. While credit markets are maturity matched, allocation through the banking rm breaks credit into a contractual system with dierent maturities: (i) medium to long-term loans on the asset side of the balance sheet and (ii) short-term demandable debt (deposits) on the liability side of the balance sheet. is process of maturity transformation,49 which is a core part of the ‘qualitative asset transformation’ that banks perform, renders the banking rm inherently fragile and thus makes the investment and liquidity layer the centerpiece of banking regulation.50 ese fundamental dierences in the architecture of maturity-matched debt and deposit-funded bank credit results in dierences in the regulatory costs associated with the respective allocation.

4.4.2.1 Costs of the market

4.4.2.2 Costs of the market: primary market structure

In securities markets, where an investor and an issuer transact, there is typically no direct ow of funds from investors to issuers. Rather, the initial economic transaction, whereby the issuer receives liquidity, involves an intermediary and an underwriter rm, which purchases (or ‘underwrites’) securities from the credit issuer. e underwriter then re-sells these securities to investors. is initial purchase is what the investment leg of the investment and liquidity layer refers to. As will be outlined below, the nature of underwriting can vary among dierent forms of credit. e current process of bond underwriting can be seen as a transitory banking rm allocation structure. is transitory bank allocation relieves both information asymmetries and liquidity concerns, as the underwriting conveys a reputational signal to the market and at the same time provides immediate liquidity to credit issuers.51 is central role of underwriters as a transitory banks, blurs the rm and the market allocation at the investment and liquidity layer. Underwriting activities should thus not be regarded as ‘pure form’ market allocation mechanisms. In addition, as the global nancial crises in 2008 revealed, where the nature of the underwriter’s inventory becomes permanent rather than just transitory, it exposes underwriters to the same systemic risks as a traditional banking rm.

49See Entrop, Memmel, Ruprecht, and Wilkens(2015) (‘Maturity transformation evolves in most cases as a consequence of the provision of liquidity when xed-rate long-term loans are nanced using short-term deposits.’). 50See Bhaacharya and akor(1993) (listing maturity transformation along with credit allocation and liquidity transformation as the core functions of banks). 51See Daniels and Vijayakumar(2007) (empirically showing the certication eect of underwriters in the municipal bond markets ‘We examine the role of underwriter reputation in the tax-exempt municipal bond market. […] Our results are consistent with the view that reputation facilitates underwriter activities that leads to reducing information asymmetries between borrowers and issuers in the municipal bond market. Our results are also consistent with larger and more reputable underwriters providing a certicatory role for issues underwrien by them.’).

170 4.4.2.2.1 Corporate bond markets

Similar to equity markets discussed in chapter 1, primary bond markets are heavily dominated by the underwriter model. is means that credit issuers do not access credit markets directly, but rather through an intermediary. More specifically, through SEC-regulated broker-dealers, typically investment banks.52 As for equity oerings, investment banks typically make rm commitment underwritings for new bond issuances.53 For a set underwriting fee, they agree to - nance new credit issuances. From the perspective of the borrowing entity, rm commitment underwritings look much like traditional bank loans: credit nancing is provided through the investment bank’s balance sheet, which in turn is typically nanced by wholesale debt. However, investment banks aim to hold this credit exposure for a logical second only,54 as they pre-sell bond oerings to buy-side credit market investors. Traditionally, this is achieved through a debt capital markets road show, where creditors provide an indication of interest (in terms of allocation and interest yield) which allows the investment bank to build the book and price the bond issuance. Finally, to ensure an adequate uptake of the new issuance and to reduce their own inventory risk, investment banks typically underprice new issuances. Com- pared to equity markets, underpricing in bond markets tends be lower, as there exists more moderate price uncertainty. e empirical identication of underpricing in bond markets is more challenging compared to equity oerings. Under- pricing is typically measured by the ‘rst day trading increase’ of a newly issued bond. However, bonds are less liquid than stocks, making a rst-day price spike more dicult to identify, given the thinness of the market. For a US corporate bond oering sample collected between 1995 and 1999, Cai, Helwege, and Warga(2007) nd no signicant underpricing for investment grade bonds and an underpricing of only 47 bps for high-yield bond oerings. us, when compared to equity markets, total issuance costs in bond markets are substantially smaller in size.55 As for equity oerings, the (baseline) costs of negotiating both underpricing and underwriting fees, rather than the regulatory costs, are the main cost drivers encountered by issuers at this layer.

4.4.2.2.2 Structured credit markets

Similar to traditional bond markets, underwriting also takes place in structured credit markets. By entering the sphere of traditionally bank-mediated credit, underwriters have increasingly become an important credit supply source.56 How- ever, given the nature of structured credit as a pooling mechanism for multiple small borrowers, credit underwriting is more complex in nature. In particular, underwriting in ‘lower level’ pass-through asset-backed securities (ABS) and ‘higher level’ collateralized debt obligations (CDOs), takes place over multiple market layers. Loan level underwriters e lowest underwriting level involves the origination of individual loans, such as residential mortgages and other consumer credits. In the time leading up to the nancial crisis, these ‘ground level’ underwriting activities were dominated by traditional mortgage banks, such as Countrywide Financial and Washington Mutual, as well as mortgage brokers, such as New Century Financial.57 rough the nancial crisis, many of the most active ‘ground level’ credit

52Under the Securities Exchange Act of 1934 section 3(a)(4)(A), a broker is a ‘person engaged in the business of eecting transactions in securities for the account of others’, while a dealer is ‘any person engaged in the business of buying and selling securities for his own account, through a broker or otherwise’ (15 U.S.C. § 78c). 53See Ramakrishnan and akor(1984) (‘e investment banker guarantees the rm xed proceeds from the issue and bears the risk of the actual proceeds being.’). 54For equity oerings, FINRA rule 5130 holds that underwriter rms may not purchase a new issue in which the underwriter has has a benecial interest, except for ‘sticky securities’, securities which the underwriter is unable to sell to the public. For corporate bond underwritings no such rule exists. 55See SEC(2015) (reporting that underwriting fees are on average, approximately around 1-1.5% of the proceed for public bond oerings, compared to around 7% on average for equity initial public oerings); Manju(2018) (reporting on even lower gures ‘In the U.S., underwriting fees, in recent years, have averaged about 0.7 percentage point on investment-grade corporate bonds, meaning that for a $1 billion bond issue, companies would pay about $7 million to banks arranging the sale.’). 56See Adrian and Shin(2010) (‘Although broker-dealers have traditionally played market-making and underwriting roles in securities markets, their importance in the supply of credit has increased in step with securitization. us, although the size of total broker-dealer assets is small in comparison to the commercial banking sector (at its peak, it was approximately only one-third of the commercial bank sector), broker-dealers became a beer barometer for overall funding conditions in a market-based nancial system.’). 57See Bar-Gill(2009) (‘Another important group of participants in the mortgage origination process is the brokers: Mortgage brokers act as intermediaries between lenders and borrowers, and for a fee, help connect borrowers with various lenders that may provide a wider selection of mortgage products. In 2006, brokerages accounted for 58 percent of total origination activity.’); Berndt, Hollield, and Sandas(2012) (nding that ‘Prior to the subprime crisis, mortgage brokers originated about 65% of all subprime mortgages.’).

171 underwriters either went into bankruptcy (such as New Century) or were absorbed by larger banking conglomerates (such as Countrywide Financial by Bank of America). Today, loan level underwriting is instead dominated by so-called ‘non-bank lender’ companies, such as icken Loans, PennyMac and LoanDepot. Buchak, Matvos, Piskorski, and Seru(2018) show that non-bank lenders’ share in mortgage originations has nearly doubled from roughly 30 percent in 2007 to 50 percent in 2015. In the Federal Housing Administration (FHA) market, the market share of non-bank lenders was even as high as 75 percent. A key advantage of these non-bank, loan-level underwriters has been their integral use of technology, as loan applications can to a large part be completed online, involving no more human loan ocers and thereby saving both labor and rental costs.58 However, there are also key regulatory savings, as non-bank underwriters did not have a specically assigned federal regulator before the mortgage crisis and are to this day not heavily regulated.59 In particular, for the context of this chapter, they do not fall under the scope of SEC regulation, as they re-sell credit to institutional pool level underwriters only. In other words, loan level underwriters do not access the credit markets directly, but instead, they can be thought of as transitory quasi commercial banking structures that re-sell credit exposure to higher-level transitory banking-like allocation structures. Pool level underwriters In the period leading up to the nancial crisis, loan level underwriters, such as New Century Financial, would sell mortgages and other loans to investment banks, such as Morgan Stanley and Lehman Brothers, which acted as traditional credit underwriters by taking these loans on their books on a transitory basis.60 e loans were warehoused and packaged, in a rst instance, into asset- or mortgage-backed securities (ABS or MBS) or, in a second instance, repackaged into collateralized debt obligations (CDO). e assembled ‘private-label’ securities61 were then re-sold in the credit markets.62 Alternatively, loan level underwriters could sell these loans to government-sponsored enterprises (GSE), i.e. the Federal Home Loan Mortgage Corporation (Freddie Mac) and the Federal National Mortgage Association (Fannie Mae). Loans securitized by these state entities are commonly referred to as agency securities.63 Prior to the nancial crisis, the traditional underwriting model, whereby market-based assets are held through a transitory bank allocation before being sold, was widely known as the ‘originate and distribute’ model.64 e Eigenossische¨ Bankenkommission (EBK), the former Swiss banking regulatory authority, described this model in the case of UBS as follows:

‘UBS, like many other market players, sourced assets under the originate-to-distribute model for purposes of securitization as an ABS or CDO. Prior to the completion of the relevant securitization, UBS held these assets in a “warehouse” and, upon completion, sold the related securities to investors.’65

In the period leading up to the credit crisis, the transitory nature of underwriter’s balance sheet positions became

58See Bank of International Selement (BIS)(2013) (‘FinTech lenders may make more intensive use of digital innovations. For instance, FinTech lenders automate far more processes than traditional credit providers and thus provide a relatively convenient and quick service to customers.’). 59See Huszar´ and Yu(2019) (Holding that non-bank lenders were not under the regulatory oversight of Oce of the Comptroller of the Currency (OCC), the Federal Reserve (FRS), Federal Deposit Insurance Corporation (FDIC), the National Credit Union Administration (NCUA), or the Oce of ri and Supervision (OTS). Also holding that today, the e Department of Housing and Urban Development (HUD) reviews lending practices of all mortgage lenders to ensure and enforce that all lenders operate according to the Fair Lending Act (FLA) and the Equal Credit Opportunities Act (ECOA).). 60See Bar-Gill(2009) (‘ese mortgage companies, and increasingly also depository institutions, sold the loans that they originated to Wall Street investment banks that pooled the loans, carved up the expected caswh ows, and converted these cash ows into bonds that were secured by the mortgages. At the peak of the subprime expansion, most mortgages were nanced through this process of securitization.’). 61See Bertaut, DeMarco, Kamin, and Tryon(2012) (‘To establish terminology, throughout this paper, “ABS” refers to mortgage-backed and other asset-backed securities that are “private-label,” meaning they are not guaranteed by the GSEs.’). 62See Bar-Gill(2009) (‘As a result, the ”owners” of the loans are the investors who purchased shares in these Mortgage (or Asset) Backed Securities (MBSs or ABSs).’). 63See Bertaut et al.(2012) (‘All securities issued or guaranteed by the GSEs are collectively referred to as Agency securities.’). 64See Andersen, Hager,¨ Maberg, Næss, and Tungland(2012) (‘is gave rise to a system today known as “originate and distribute” (or “shadow banking system”), where credit is given for the purpose of distributing it rather than holding it until maturity. e modern “originate and distribute” structure had two main inuences on how banks started doing business and generate revenues. First of all, incentives to complete thorough assessments of the borrower’s credit worthiness quickly evaporated since the originator sold o the loan and was no longer exposed to the credit risk should the loan default. Second, a large part of banks income was now generated from fees related to processing loan inquiries and distributing (selling) the loan.’); Levitin(2011) (‘In the originate-to-distribute model, mortgage loans were made with the objective of reselling them in the secondary market, generally as part of securitizations.’) Hu(2012) (‘e originators of loans increasingly relied on an ”originate-to-distribute” model, wherein the originators increasingly sold their loans to securitizers who then sold securities backed by these loans to investors.’). 65See EBK(2008).

172 more permanent for the portion of the credit securities that could not be sold to investors (traditionally referred to as ‘sticky oerings’66) or where the banks intentionally retained exposure.67 In other words, the function of these intermediaries shied from a market-enabling role to a more traditional bank role, thereby blurring the lines between the market and the rm allocation:

‘At least towards the end of the mortgage boom, the CDO securitization business functioned only to the extent that market players such as UBS, Merrill Lynch and Citigroup were willing and able to retain “unaractive” low-yield Super Senior CDO tranches of individual securitizations on their own (trading) books.’

e credit crises had a dierential impact on private-label and agency asset-backed securities. In the aermath of the crisis, the private-label RMBS market has collapsed to levels not seen since the early 1990s.68 On the other hand, the GSE-sponsored RMBS market has blossomed, eventually reaching and surpassing pre-crisis levels.69 As mentioned in section 4.4.1.1.3, in contrast to private-label ABS, securities issued or guaranteed by Freddie Mac or Fannie Mae are exempt from the registration requirements of the Securities Act under their legislative charters,70 and they do not le registration statements with the SEC for the oering of such securities. us, like ‘non-bank’ lenders, they operate in the ‘shadows of securities regulation’.

4.4.2.3 Costs of the market: primary market regulation

While the disclosure and information layer regulates every single transaction, security laws at the investment and liquidity layer govern the intermediaries at the institutional level in an entity-centric manner.71 As the nature of underwriting activities involves routing new issuances through the underwriter’s balance sheet, the lines between a market-enabling function and a banking rm operation are blurred. As the above example of UBS shows, this is particularly relevant for cases where balance sheet positions of underwriters become permanent rather than transitory. In the past, this dual nature has been reected by regulation, which has gone back-and-forth between separating and merging underwriting activities with banking operations. e Glass–Steagall Act of 193372 can be seen as an early regulatory eort in this respect, which aimed to separate commercial bank lending activities from investment banks’ underwriting activities. e main objective of this separation was to prevent banks from taking proprietary positions in the underwrien securities and thus to clearly distinguish market-enabling functions from banking activities. How- ever, even under the Glass-Steagall regime, underwriting activities were already largely dependent on commercial bank wholesale nancing. As an investment banking ocer in a 1976 testimony in a hearing on the Glass-Steagall Act noted:73

“Banks would very likely dominate revenue bond underwriting, as they do general obligation underwriting [... ] we must borrow money from the banks, while at the same time we compete with them. Like any other business, we use bank loans to carry inventory”

66See Winnike and Nordquist(1993) (describing this in the context of equity oerings ‘In some cases, an oering will prove to be less aractive to investors than anticipated. If the lack enough of serious demand is obvious, the oering may be postponed or abandoned. In some instances, however, the participants will have elected to go forward before they are fully aware of, or notwithstanding, an inadequate demand. is lack of demand may be manifested in an incomplete “book” of orders […] is type of situation is called a “sticky oering” […] When an oering turns sticky and the underwriter is forced to purchase shares for its own investment). 67See V. V. Acharya, Schnabl, and Suarez(2013) (describing how banks used ABCP conduits to retain exposure ‘Eectively, banks had used conduits to securitize assets without transferring the risks to outside investors, contrary to the common understanding of securitization as a method for risk transfer. We argue that banks instead used conduits for regulatory arbitrage.’). 68See Fabozzi(2016) (‘Non-agency RMBS annual issuance began a rapid decline post-2007 as it was aected by the credit crisis and the recession that began at the end of 2007. […] In the years just aer 2008, non-agency RMBS annual issuance remained at low levels not seen since the early 1990’); Clayton(2019) (In the same vein, SEC Chairman Jay Clayton notes that since the nancial crisis, activity in the SEC-registered RMBS space has been very limited and since the Commission revised its ABS rules in 2014, not a single SEC-registered RMBS oerings has taken place.). 69See Fabozzi(2016) (‘In contrast, agency passthrough issuance was relatively unaacted by the onset of the crisis, as slower originations in the GSEs’ traditional markets were oset by initiatives to support borrowers previously served by the non-agency sector. As a result, agency passthrough issuance increased in 2009 by more than 33% above 2008 levels. […] while agency passthrough annual issuance remained higher than it was in 2006-8.’). 70For Freddie Mac, 12 U.S. Code § 1455 (g), for Fannie Mae, 12 U.S. Code § 1717 (c)(1). 71See Krug(2013) (‘If the entity is the marketplace actor, then, logically, the entity should be the regulatory subject.’). 7212 U.S.C. 227. 73Bank Underwriting of Revenue Bonds: Hearings, Ninetieth Congress, First Session, on S. 1306, p. 207.

173 In other words, SEC-regulated broker-dealers required bank nancing to carry underwrien loans as transitory inventory. us, even under the Glass-Steagall regime, where a formal separation of commercial banking activities and market-enabling activities existed, there was already a close economic connection between the two allocative systems. Starting in the 1980ies, the SEC took a number of decisions, which (by the Commission’s own admission) blurred the lines between the banking and the securities industry even further.74 By the end of the 1990ies, as Citibank acquired the broker-dealer rm Salomon Smith Barney (through Traveler’s Group) in 1998, the rigid divide between banking operations and market-enabling rms had already been almost fully eroded. Shortly thereaer, in 1999, almost a decade before the global nancial crisis of 2008, the Glass-Steagall Act was formally repealed by the Gramm–Leach–Bliley Act.75 is allowed banking rms to enter the underwriting business, as it removed barriers among banking and securities rms that previously prohibited any one institution from acting as a combination. In the years that followed, banks aggressively entered the underwriting business and soon dominated capital-intensive segments of that market, in particular the private ABS and CDO credit markets. In the aermath of the nancial crisis, a number of Glass-Steagall Act provisions were reinstated through the Volcker Rule76 in the 2010 Dodd-Frank Act,77 thereby aempting to once again separate market-enabling functions from banking operations. Reecting the blurry lines between regulation, we can distinguish between (i) institutions that are exclusively subject to security laws during the credit crisis and (ii) institutions that are subject to both security laws and bank regulation. Under both regimes, the process of ‘allocating an asset through the market’, at least formally through the legal practice of securitization, provided opportunity for regulatory arbitrage.

4.4.2.3.1 Entities exclusively supervised by the SEC prior to the nancial crisis

Traditionally, underwriter activities were exclusively carried out by SEC-regulated broker-dealers.78 Broker-dealers are subject to a range of general conduct regulations, such as antifraud provisions or order execution obligations.79 However, most relevant within the context of the nancial crisis were nancial responsibility regulations, in particular the net capital rule.80 Although controversial, a number of scholars have argued that enacted changes in this net capital rule in 2004,81 along with a special SEC supervisory program to administer compliance with this rule, the Consolidated Supervised Entity (CSE) program,82 have allowed investment banks to substantially increase their proprietary positions in the pre-GFC period. In particular, this regulation centered around the ve large investment banks at the time: Bear Stearns, Lehman, Merrill Lynch, Goldman Sachs, and Morgan Stanley. ese were the only non-bank broker-dealers, which were exclusively regulated by the SEC. Under the CSE program, the SEC had exclusive supervisory power over the bank-like activities of these broker dealers. e credit crises resulted, in part, from lax supervision related to the primary market underwriting activities of

74See SEC(1983) (‘During the past year, the blurring of traditional boundaries between the banking and securities industries has resulted in the Commission’s taking various positions on Glass-Steagall.’). 7515 U.S.C. § 6801 et seq. 7612 U.S.C. 1851. 77Dodd-Frank Wall Street Reform and Consumer Protection Act, Pub. L. 111-203, §§ 1001-1100, 124 Stat. 1955-2113 (21 July 2010), 7 codied at 12 U.S.C. §§ 5301, 5481-5603. 78Under the Securities Exchange Act of 1934 section 3(a)(4)(A), a broker is a ‘person engaged in the business of eecting transactions in securities for the account of others’, while a dealer is ‘any person engaged in the business of buying and selling securities for his own account, through a broker or otherwise’ (15 U.S.C. § 78c). 79For example, Securities Exchange Act of 1934 section 9(a), 15 U.S.C. § 78i, prohibits a range of manipulative practices with respect to securities registered on a national securities exchange. Similarly, Securities Exchange Act of 1934 section 10(b), 15 U.S.C. § 78j, prohibits the use of ”any manipulative or deceptive device or contrivance” in connection with the purchase or sale of any security. 80Rule 15c3-1, 17 CFR § 240.15c3-1. is rule can be understood as the SEC’s version of the banking regulators’ capital adequacy requirements. e rule requires a broker-dealer to maintain enough liquid assets to promptly satisfy customer claims, even if the broker-dealer should go out of business. 81See Beccalli et al.(2015) (‘Finally, when analyzing changes in regulation, on the one hand our data conrm the common view that the 2004 SEC new net capital rule strongly increased the level of formal leverage of investment banks.’); Lo and Mueller(2010) (questioning the impact of the net capital rule ‘ese reports of sudden increases in leverage from 12-to-1 to 33-to-1 seemed to be the “smoking gun” that many had been searching for in their aempts to determine the causes of the Financial Crisis of 2007–2009. […] While these “facts” seemed straightforward enough, it turns out that the 2004 SEC amendment to Rule 15c3–1 did nothing to change the leverage restrictions of these nancial institutions.’). 82See Gerding(2009) (‘Broker-dealer holding companies could opt into this program, aer which the SEC would supervise not only registered broker-dealer entities, which it has historically regulated, but unregulated aliates and the holding-company parents of those broker-dealers. […] With respect to these CSEs, the SEC took responsibility for seing regulatory capital. But it decided not to apply the same capital-standard regulatory framework that it had applied to SEC-registered broker-dealers before the CSE Program. […] Aer the CSE rules took eect in 2004, the regulatory capital of those entities admied to the program dropped.’).

174 these investment banks in the private ABS and CDO markets. In 2008, as a result of the illiquidity of balance sheet exposure to such securities, Bear Stearns and Merrill Lynch were forced into rescue mergers with J.P. Morgan and Bank of America, respectively.83 At the peak of the crisis, Lehman Brothers famously had to declare bankruptcy under Chapter 11. Goldman Sachs and Morgan Stanley soon thereaer became licensed as bank holding companies, which gave them direct access to the banking system’s preferential liquidity pools, in particular the discount window84 and the FHLBanks System.85 Following these drastic actions and the exit of all non-bank broker dealers, the Consolidated Supervised Entity (CSE) program was shut down. Schapiro(2010) notes that the SEC devoted too few resources to the supervision of the ve large investment banks, just about four sta members per rm.86 By comparison, the Fed devoted about 19 supervisory sta members on average to each of the systemically important nancial rms under its supervision. From a systemic risk perspective, a key concern was that the SEC’s supervision of the investment banks focused mainly on the protection of the bank customers and on liquidity, rather than on the solvency of the investment banks’ balance sheets (as traditional banking regulation does).87 In summary, it can be noted that certain transactions allocated through the credit markets, seem to have been subjected to lower regulatory capital costs and supervision compared to traditional banking rms.

4.4.2.3.2 Entities subject to both securities and banking regulation prior to the nancial crisis

Similar to ‘pure’ broker-dealers, banks that held credit in the form of market-based (securitized) credit, rather than as traditional bank loans, beneted from lower regulatory capital charges.88 In particular, there existed a regulatory arbitrage opportunity between the traditional banking book and the trading book.89 According to the Basel II standards, a bank’s ‘trading book’ was intended for tradable claims only, namely assets which are held for reselling in the short term. In contrast, the banking book was reserved for ‘held-to-maturity’ loans.90 Already before the Basel II standards were passed, most investment banks were holding the majority of their assets on the trading book.91 Basel II then allowed for an ‘internal ratings-based’ (IRB) to valuing market-based exposure, which reduced regulatory capital charges signicantly, in part because it neglected correlation risk.92 us, the (theoretical) market-based allocation, helped these banking rms to reduced the rm-based regulation costs. However, in eect, these market allocation mechanism were used to cloak a more highly leveraged rm-based allocation.

83See Bebchuk(2010) (‘Bear Stearns sold itself in a re sale to JPMorgan in March 2008, and half a year later Lehman led for bankruptcy, triggering a worldwide panic.’). 84See Judge(2014) (‘e primary way that the Fed provides liquidity to banks in need of it is through its Discount Window. All national banks and state banks have access to the Discount Window, subject to eligibility requirements’). 85See Judge(2014) (describing the importance of this source of liquidity during the crisis ‘One of the most signicant sources of liquidity for banks following the August 2007 credit freeze was advances from the FHLBanks.’). 86See Schapiro(2010) (‘Notwithstanding the hard work of its sta, in hindsight it is clear that the program lacked sucient resources and stang, was under-managed, and at least in certain respects lacked a clear vision as to its scope and mandate.’). 87See Ohlrogge and Giesecke(2016) (‘[A] key feature of net capital for broker-dealers is its focus on liquidity, rather than solvency as is the case for bank capital.’). 88See Gerding(2016a) (‘Banks and other nancial institutions game the types of capital requirements envisioned by Basel I and II in a number of ways. e most important forms of regulatory capital arbitrage have involved various types of investment structuring facilitated by securitization and derivatives.’). 89See E. Lee(2014) (‘Excessive leveraging fuelled the global nancial crisis of 2007-2009. Moving risky loan exposures o balance sheet and manipulating the regulatory arbitrage between the trading book and banking book were just a few tricks commonly employed by banks in order to make the most use out of their capital.’). 90See Bank of International Selement (BIS)(2004). (paragraph 687 ‘Positions held with trading intent are those held intentionally for short-term resale and/or with the intent of beneting from actual or expected short-term price movements or to lock in arbitrage prots, and may include for example proprietary positions, positions arising from client servicing (e.g. matched principal broking) and market making’). 91See Securities Industry and Financial Markets Association (SIFMA)(2003) (‘Investment banks place virtually all their nancial instruments in the “trading book”.’). 92See Mariathasan and Merrouche(2014) (‘e main goal of Basel II was to reduce the margins for regulatory arbitrage and improve eciency by linking capital charges more directly with risk-taking. Regulators also hoped that the negotiations leading up to Basel II would improve credit risk-modelling and the communication between banks and supervisors. In practice, however, Basel II raised the level of complexity and implemented a hybrid regime in which banks calculate parameters, such as a loan’s probability of default (PD), with internal models, whilst the actual capital charge is determined by inserting these parameters into a model decided by the BCBS. e new framework failed to account for correlations and relied on short time series; it neglected endogeneity, and led some experts to conclude that risk calibration under Basel II caused excessive indebtedness and maturity transformation’).

175 4.4.2.3.3 Regulatory responses to the nancial crisis

As a result of the nancial crisis and the poor quality of some of the ABS securities (most notably subprime MBS), the consensus among policymakers and legislators in the aermath was that underwriters in the primary credit market should have more ‘skin in the game’, meaning that they should retain more credit risk.93 Consequently, Article 15G of the Securities Exchange Act of 193494 was added by section 941 of the Dodd-Frank Wall Street Reform and Consumer Protection Act (the Act or Dodd-Frank Act).95 Section 15G requires the sponsors of asset-backed securities to retain not less than 5 percent of the credit risk of the assets collateralizing the asset-backed securities.96 e idea behind this regulation is that the adverse selection of ABS sponsors should be avoided, namely that investment banks should be disincentivized to originate bad loans and quickly ooad them from their balance sheets.97 However, the eect of the higher risk retention requirements is that it actually makes the transitory nature of underwriting more permanent. In other words, this regulation has further increased the extent to which primary credit market underwriting activities resemble traditional bank lending activities. To the extent that it mandates banks to maintain balance sheet exposure to structured credit underwritings, this regulation can be regarded as shiing the market allocation back to a banking rm-based allocation.

4.4.2.4 Costs of the market: secondary market

Credit securities, once issued, can be trade on the secondary market, either through public exchanges or on an over- the-counter (OTC) basis between nancial institutions. However, fewer than 5% of all bonds are listed on the NYSE.98 Instead, the majority of credit securities are traded on an OTC basis.99 For a long time, secondary credit markets have been dominated by ‘principal’ market structures, where broker- dealers hold bonds on inventory to make a market.100 ey were compensated for their market-making activity through bid-oer spreads. ese markets were operated (and to some extent still are) over the phone between dealers and required brokers to take inventory on the balance sheet.101 However, over the last decades, electronic OTC platforms have emerged as ‘agency markets’, where the dealers do not take inventory on the books.102 ese markets are compensated through explicit commissions instead. In particular, MarketAxess has been reported to have an 85% share of the secondary corporate bonds market.103 On the other hand, the electronic trading platform Tradeweb has come to dominate the government and treasuries market.104 Structurally, secondary credit market activities traditionally involve dealers

93See Bay(2011) (‘e risk retention requirements are intended to cause nancial institutions that package and sell asset-backed securities to beer manage risks relating to such securities by requiring those institutions to keep ”some skin in the game” rather than selling the entire issuance to third parties.’); Levitin(2011) (‘e Dodd-FrankAct’s “skin-in-the-game” credit risk retention requirement is the major reform of the securitization market following the housing bubble. Skin-in-the-game mandates that securitizers retain a 5% interest in their securitizations.’). 9415. U.S.C. 78o-11. 95Dodd-Frank Wall Street Reform and Consumer Protection Act, Pub. L. 111-203, §§ 1001-1100, 124 Stat. 1955-2113 (21 July 2010), 7 codied at 12 U.S.C. §§ 5301, 5481-5603. 96See Levitin(2011) (‘e denition of the 5% risk retention is le up to regulatory implementation. ere are also to be a number of exceptions to the skin-in-the-game requirement, including a signicant one for ”qualied residential mortgages,” a phrase le open for further regulatory denition’). 97See Levitin(2011) (‘e skin-in-the-game requirement reects an assumption that the originate-to-distribute model contains a moral hazard because loan originators do not hold the credit risk on the loans they make and instead are compensated through upfront fees and the sale of the loans. e result of this moral hazard was higher volume and lower quality of mortgage lending.’); Hu(2012) (‘e draers of Dodd-Frank were especially concerned with two incentive alignment issuers: First, under the “originate to distribute” model, because loans were made to be sold into securitization pools, lenders did not expect to bear the credit risk of borrower default.” Second, investors found it impossible to assess the risks of the underlying assets, especially when those assets were resecuritized into complex instruments like collateralized debt obligations (CDOs) and CDOs squared. By forcing securitizers to retain a material amount of risk, their economic interests would be aligned with the interests of investors, and securitizers would thus have a strong incentive to monitor the quality of the assets.’). 98See Edwards, Harris, and Piwowar(2007) (‘Fewer than 5% of all bonds are listed on the NYSE. For those bonds, ABS trades, which are almost all small retail trades, represent from zero to 40% of all transaction’). 99See Saunders, Srinivasan, and Walter(2002) (‘Despite its importance the market-micro structure of the secondary market for corporate bonds remains something of a mystery. e major reason for this has been the OTC inter-dealer nature of this market.’). 100(‘For decades, xed income markets have been structured as over-the-counter (OTC), “principal” markets where the dealer owns or acquires the bonds and is compensated for market-making activity through the bid-oer spread, or the dierence between purchase and sale price.’). 101See Blackrock(2014) (‘To eectively function, a principal market requires the dealer community to warehouse a signicant inventory of bonds to serve investor demand.’). 102See Blackrock(2014) (‘is is in contrast to an “agency” market where the purchase or sale transaction is brokered, and the compensation for this brokerage is an explicit commission. […] In the recent past, several dealers have introduced proprietary e-trading platforms, incumbent e-trading rms including MarketAxess and Tradeweb broadened their product oerings, and nascent rms have started up.’). 103See Dugid(2019). 104See Staord(2019) (‘Tradeweb itself handles about $80bn a day in deals related to Treasuries, mainly between banks and large investors, using prices from roughly 30 dealers.’).

176 holding credit securities on inventory. us, these are again transitory banking rms, although this role seems to decline with the increased presence of agency-type market operators. Securities regulation can govern these secondary market activities in two distinct ways. Firstly, for the (few) credit securities, which are listed on public exchanges, through security laws that govern national securities exchanges. Sec- ondly, both ‘traditional’ OTC dealer markets, as well as the newly emerging electronic OTC platforms, are regulated as broker-dealers under existing security laws.

4.4.2.5 Costs of the banking rm

4.4.2.5.1 Primary activities

Under the credit market allocation, every credit issuance requires that the underwriters ‘go out’ into the credit market and make an active eort to place the specic credit securities with creditors. At the time that creditors make a purchase order for bonds or asset-backed securities, they have typically conducted a due diligence on the risks of the specic credit issuer(s). In contrast, primary ‘activities’ under the banking rm regime take a very dierent form. Under the banking rm allocation, banks can pre-nance loan pools on a blind pool basis. When depositors ‘put their money in the bank’, they eectively underwrite future loans, that can be originated by the banking rm. However, at the time that depositors placing their savings with banks, unlike creditors in the market allocation, they do not receive any disclosures about the nature and risks the future loans that are being originated. is pre-nancing feature ensures a commied pool of funds, as well as speed-of-execution. Bank Regulation e question why deposit-taking and lending, two very dierent operations, occur within the same rm in the bank allocation, has been subject to a great deal of academic debate.105 A related question to this has long been, why rms from other industries would not engage in bank-like activities.106 As an outspoken proponent of the credit creation theory of banking,107 Werner(2014b) argues that the ability of banks to (i) legally accept deposits and (ii) ‘mix’ these deposits with other assets on their balance sheet is the distinguishing feature of banks, seing them apart from other corporations or giving them an ‘unfair advantage’.108 While the fact that banks do not have to segregate deposits clearly provides a partial explanation,109 the costly regulatory alternative of banks having to issue individual securities for every individual loan, provides an even more convincing explanation of why bank’s hold a dominant position in credit markets. In other words, the exclusion of banking activities from security laws. us, bank regulation of primary loan origination activities can be characterized by the regulatory privilege or ‘monopoly’ of banking rms to accept retail deposits110 and transform them into long-term loans, without having to issue individual securities for each loan origination. e deposits are exempt from security laws, which allows banks to pre-nance loan portfolios on a blind pool basis.

105See Kashyap, Rajan, and Stein(2002) (‘commercial banks are institutions that engage in two distinct types of activities, one on each side of the balance sheet—deposit-taking and lending. […] A great deal of theoretical and empirical analysis has been devoted to understanding the circumstances under which each of these two activities might require the services of an intermediary, as opposed to being implemented in arm’s-length securities markets. While much has been learned from this work, with few exceptions it has not addressed a fundamental question: why is it important that one institution carry out both functions under the same roof?’). 106See Werner(2014b) (‘is is likely true, but the question remains precisely which regulations are crucial to allow banks to engage in the activity that makes them unique, and likewise, which regulations, if applying equally to non-banks, would allow non-banks to behave in the same way as banks.’). 107See Werner(2014a) (‘A third theory maintains that each individual bank has the power to create money ‘out of nothing’ and does so when it extends credit (the credit creation theory of banking […] is study establishes for the rst time empirically that banks individually create money out of nothing. e money supply is created as ‘fairy dust’ produced by the banks individually, ”out of thin air”.’). 108See Werner(2014b) (‘anks to this exemption they are allowed to keep customer deposits on their own balance sheet. is means that depositors who deposit their money with a bank are no longer the legal owners of this money. Instead, they are just one of the general creditors of the bank whom it owes money to.’). 109See Werner(2014b) (‘What makes banks unique and explains the combination of lending and deposit-taking under one roof is the more fundamental fact that they do not have to segregate client accounts, and thus are able to engage in an exercise of ‘re-labelling’ and mixing dierent liabilities, specically by re-assigning their accounts payable liabilities incurred when entering into loan agreements, to another category of liability called ‘customer deposits’.’). 11012 U.S. Code § 1828.

177 4.4.2.5.2 Secondary activities

As we have seen above, credit allocated through nancial markets is actively traded through the secondary market. In the traditional bank allocation, however, the reigning principle is illiquidity. Under the bank allocation, originated loans are underwrien by the banking rm and are then held-to-maturity on the bank’s balance sheet. e bank’s balance sheet is nanced by short-term demandable debt from depositors and wholesale funding.111 While credit markets are maturity-matched, allocation through the banking rm breaks originated credit contracts into a contractual system with dierent maturities: medium to long-term loans on the asset side and short-term demandable debt on the deposit side. is mismatch or maturity transformation process is the core economic function of banks,112 but also what makes them inherently fragile.113 In particular, it can lead to the traditional retail bank runs114 or wholesale funding runs, such as the run on Northern Rock in the wake of the nancial crisis in 2007.115 is risk thus makes the investment and liquidity layer the centerpiece of banking regulation. Bank Regulation e illiquidity of the bank’s long-term loan portfolio, nanced by short-term deposits, is the crux of the banking regulation puzzle.116 Traditionally, regulators have addressed the contractual maturity mismatch problem by prescribing banks a certain design of their ‘contractual portfolio’. In particular, the regulators supervise the banking rm’s balance sheet composition, most notably the bank’s equity buer. Ex-ante regulatory tools To this day, these are still the main tools within the banking regulation toolset. ere exist multiple points of entry for regulating a bank’s balance sheet. Regulating bank capital essentially means that regulators prescribe the bank to enter into contracts with its shareholders in a certain proportion to the outstanding notional of the bank’s outstanding debt contracts with creditors (liabilities, bank debt) and borrowers (assets, bank loans). Similarly, the introduction of the liquidity coverage ratio by the Basel III framework, prohibits banks from deploying funds above a certain ratio to long- term, illiquid contracts. Lastly, the acceptance of contingent convertible bonds as regulatory ‘tier one capital’, provides banks with incentives to nance themselves through debt contracts with a conversion contingency in stress scenarios. e below list provides a non-comprehensive menu of such ex-ante regulatory measures in a contractual framework:

• Capital Buers: Since equity does not mature per se, but is retired/repaid only upon liquidation of the rm, increasing a bank’s equity ratio naturally adds to the resilience of the bank’s contractual system.117 In terms of a contract framework, long-term loans on the asset side are matched by a higher proportion of ‘innite-term’ equity on the liability side. Recent regulatory reforms have further stressed the need to increase this equity buer pro-cyclically.118

111See Calomiris and Kahn(1991a) (‘Demandable debt warrants explanation because, in several respects, it appears more costly than available alternative contracting structures. By issuing demandable debt, banks created a mismatch between the maturity of assets and liabilities.’). 112See Diamond and Dybvig(1983a) (‘Banks are able to transform illiquid assets by oering liabilities with a dierent, smoother paern of returns over time than the illiquid assets oer. ese contracts have multiple equilibria. If condence is maintained, there can be ecient risk sharing, because in that equilibrium a withdrawal will indicate that a depositor should withdraw under optimal risk sharing. If agents panic, there is a bank run and incentives are distorted.’). 113See Calomiris and Kahn(1991a) (‘is mismatch le them exposed to the possibility that depositors would aempt to withdraw more funds than a bank could supply on short notice. When this occurred, the consequences were costly. Individual banks that did not meet their obligations were forced into expensive procedures (liquidation or receivership) that would not have arisen in an equity-based or maturity-matched contracting structure.’). 114See Diamond and Dybvig(1983a) (‘Bank runs are a common feature of the extreme crises that have played a prominent role in monetary history. During a bank run, depositors rush to withdraw their deposits because they expect the bank to fail. In fact, the sudden withdrawals can force the bank to liquidate many of its assets at a loss and to fail. In a panic with many bank failures, there is a disruption of the monetary system and a reduction in production.’). 115See Shin(2009) (distinguishing the Northern Rock bank run from traditional retail bank runs ‘us, the real question raised by the Northern Rock episode is not so much why retail depositors are so prone to loss of condence that lead to bank runs, but instead why the plentiful short-term funding that Northern Rock enjoyed before August 2007 suddenly dried up. […] us, the key to the initial ”run” on Northern Rock was the norenewal and medium-term paper. is was the run that led to the demise of Northern Rock – a run that happened out of sight of the television cameras.’). 116See Diamond and Dybvig(1983a) (‘Illiquidity of assets provides the rationale both for the existence of banks and for their vulnerability to runs.’). 117See Shin(2009) (‘Traditionally, capital requirements have been the cornerstone of the regulation of banks. e rationale for capital requirements lies in maintaining the solvency of the regulated institution. By ensuring solvency, the interests of creditors especially retail depositors can be protected.’). 118See Dewatripont(2014) (‘e main innovation here concerns the “Countercyclical Capital Buer”: for the rst time in Basel, the procyclical bias of a constant capital ratio is addressed explicitly, and authorities are meant to require banks to raise their capital ratio in the upside of macroeconomic cycle, in order to be able to ‘release’ capital on the subsequent downside.’); Repullo and Suarez(2004) (‘As described in BCBS (2010), the new interna-

178 • Liquidity Reserves: the maturity mismatch can also be curtailed via the asset side of the balance sheet, namely by transforming the maturity prole of assets by holding more ‘liquid’ assets.119 Liquid assets either have a maturity of zero (cash or reserves held with the central bank), are short-term (commercial paper) or can be transformed quickly into cash because of the low risk prole of the borrower (sovereign or inter-bank debt).

• Private bail-in measures: the contractual mismatch can also be addressed by extending the maturity of bank debt in a crisis situation. rough the issuance of contingent convertible bonds (‘CoCos’), a private market bail- in can be achieved by pre-negotiating a distress-contingent conversion of medium-term debt into ‘innite-term’ equity.120

e regulatory overhaul in the aermath of the global nancial crisis has put most of the initial focus on these ex- ante regulatory tools. Notably, the Basel III framework121 has (i) increased the minimum capital buer, (ii) introduced a liquidity coverage ratio122 and (iii) has made it aractive for banks to issue private contingent convertible debt.123 Ex-post regulatory tools In the aermath of the nancial crisis, a new set of bank regulatory tools have been introduced, which mark a fundamental departure from traditional nancial regulation, namely by shiing the focus from ex-ante to ex-post contractual design regulation. As part of these tools, the nancial regulator amends or overrides previously entered contracts ex- post. In particular, these ex-post regulatory tools entail:

• Special resolution mechanisms: ese measures, inspired by the Federal Deposit Insurance Corporation (FDIC) receivership regime,124 provide an alternative to the traditional bankruptcy process,125 giving a resolution author- ity126 the rights to separate a bank into a robust part, the ‘good bank’, and a fragile or non-viable part, the ‘bad bank’.127 Basically, such good-bank/bad-bank measures aim to eliminate the crisis elements that have led to a disruption in the maturity transformation, such as hard-to-value assets that have experienced a price shock and led to a funding squeeze on the liability side.

• Public bail-in measures: rough public bail-in measures, private bank debt is restructured through a debt- tional agreement on regulatory standards reinforces capital regulation by means of higher requirements of core Tier 1 capital and by complementing them with a capital preservation buer and a countercyclical buer. e idea behind these mandatory buers is to force banks to build up buers in good times and release them in bad times.’). 119See Calomiris and Kahn(1991a) (‘If depositors en masse aempted to withdraw funds from the entire banking system, banks as a group were forced to suspend convertibility of their liabilities into specie on demand. Such suspension was also disruptive and costly. To defend against either of these undesirable consequences, banks had to hold a proportion of their assets in idle reserves to insulate themselves from excessive withdrawals.’); Aldasoro and Faia(2016) (‘Equity requirements are meant to control and prevent the spread of losses on banks’ asset side. Liquidity requirements, newly introduced in Basel III and subsequent regulations (CRD IV and CRR), aim at mitigating the impact of liquidity freezes.’). 120See Jang, Na, and Zheng(2018) (‘One remarkable evolution in the capitalization of banks under this new regulation is the emergence of a new hybrid asset class called contingent convertible bonds or CoCos for short. CoCos are a type of bond that is automatically converted into equity or wrien down when the issuer’s capital-ratio falls below a specied level. is automatic conversion characteristic means that CoCos are expected to reduce the economic costs of bankruptcy for the benet of all debt and equity holders.’); McDonald(2013) (‘A frequently discussed reform is to have banks issue claims that behave like debt during normal times and which convert to equity during a crisis. Such claims are variously referred to as “reverse convertibles” and “contingent capital”. Because these claims convert to equity, contingent cap-ital is a buer against default.’). 121Bank of International Selement (BIS)(2010) 122Bank of International Selement (BIS)(2017). 123Bank of International Selement (BIS)(2011). 124See Morrison(2009) (‘e Federal Deposit Insurance Corporation (“FDIC” or “the Corporation”) has authority to seize control of a commercial bank that is approaching (or has entered) insolvency or has engaged in conduct signaling fraud or unsound risk management practices. Once it intervenes, the FDIC has broad power to succeed to the institution, operate it, revoke its charter, remove management, and choose whether to liquidate the bank or reorganize it.’). 125See Morrison(2009) (discussing why the ordinary bankruptcy process is insucient ‘[…] the bankruptcy process is managed by a judge. ough federal regulators are subject to political pressure, they possess expertise that is generally beyond the ken of judges. When a systemically important institution suers distress, rapid decision making is necessary. Federal law permits this kind of speed when the FDIC seizes a bank.’). 126Under Dodd-Frank, an “Orderly Liquidation Authority” (OLA) was created that shares many features of the traditional bankruptcy procedures. 12 C.F.R. Part 380. 127See Armour(2014) (‘e rst generation of such procedures, which generally were based on the Federal Deposit Insurance Corporation (FDIC) receivership regime in the US, involve a waiver of creditors’ ordinary property rights in order to complete the process extremely rapidly. ’Good’ assets and depositors’ claims are transferred to a purchaser literally overnight, and the ’bad’ assets that remain in the rump entity are wound down gradually in a way that does not transmit a shock.’); Baird and Morrison(2011) (‘e recently enacted nancial reform legislation empowers the Secretary of the Treasury to appoint the Federal Deposit Insurance Corporation (FDIC) as receiver for troubled nancial companies when their failure poses a systemic risk. Previously, the resolution process for these companies was le to the bankruptcy process. By common account, the new law reects a repudiation of traditional bankruptcy law when it comes to the collapse of giant corporations that threaten the economy as a whole. Instead we have a mechanism that brings the regime used to liquidate failed commercial banks to a broader range of institutions.’).

179 equity-swap by a public sector resolution body.128 In order to complete this process very rapidly, ordinary contractual rights of both shareholders and creditors are waived during the resolution phase.129 In contrast to bailouts, which aect public debt, losses are absorbed by existing creditors.130 Bail-in measures address the maturity mismatch problem by converting medium- or long-term bank debt into ‘innite-term’ equity.131

Banks, as described above, are dynamic contractual systems. Bank regulation monitors and controls these dynamic contractual systems. As such, it imposes rights and obligations on the contractual freedoms between banks and their creditors. Since bank failures result from a mismatch in contractual maturity terms, bank regulators are empowered to adjust these contractual terms either ex-ante or ex-post. As the broadness of the regulatory toolkit above shows, the compliance costs that banking regulations put on bank-based credit transactions to address illiquidity are quite extensive.

4.4.2.5.3 Comparative pricing

Comparing the costs of a banking rm allocation with the costs of credit markets at the investment and liquidity layer can be a challenging task. Firstly, the role of underwriters, both in primary and secondary markets, oen blurs the lines between a market-based and a bank-based allocation. As outlined above, to a much larger degree than in equity markets, there exists a tendency of underwriters to permanently hold on to originated assets in credit markets. As a result, underwriter activities may at the same time be subject to securities and banking laws. Secondly, clearly identifying the costs of the bank-based allocation at the investment and liquidity layer is dicult, since the activities functionally overlap with the diversication layer. Baseline costs To empirically compare the baseline costs at the investment and liquidity layer, one could compare the credit underwriting costs (including the underwriting fee and underpricing) with the net interest margin of the banking rm,132 as the core cost of bank-based intermediation. An upper bound estimation would be a corporate bond underwriting fee of 1.5 percent133 and an underpricing of 0.5 percent.134 is would put the total credit underwriting costs at around 2 percent, which could then be compared to empirical observations of the net interest margin (the costs of allocating credit through the bank). Angbazo(1997) and Nguyen(2012) have reported net interest margin levels between 4.2 and 4.4 percent.135 More recently, net interest margins are reported to be around 3.3 percent.136 From this back-of-the-envelope calculation it would appear that the costs of transacting through the rm are higher than through the market. However, such an empirical specication would not fully capture the costs of either the market-based underwriter or the banking rm. Herein lies the diculty of the investment and liquidity layer. With respect to the underwriting costs, where the underwriter permanently holds on to the asset, one would need to take account of the broker-dealer’s net interest margin. Similarly, since the net interest margin typically covers the full economic rent of the banking rm, including the diversication layer of the banking rm, the aggregate net interest gure would overestimate the cost of

128See Armour(2014) (‘However, the ‘bail-in’ powers are very dierent from the rst-generation resolution mechanisms: they are, in eect, expedited reorganisation procedures, as opposed to liquidation procedures. at is, they envisage the same corporate entity remaining, but with a restructuring of the terms of its nancing.’). 129See Avgouleas and Goodhart(2015) (‘Essentially, bail-in provisions mean that, to a certain extent, a preplanned contract replaces the bankruptcy process, giving greater certainty as regards the suciency of funds to cover bank losses and facilitating early recapitalization. Moreover, the bail-in tool can be used to keep the bank as a going concern and avoid disruptive liquidation or dismembering of the nancial institution in distress.’). 130See Barucci, Colozza, and Milani(2019) (‘By replacing the bail-out of a bank with a bail-in, the new rules try to break the direct link between bank troubles and public debt and to improve the bank governance, which should benet from a more accurate oversight from shareholders and bondholders.’). 131See Dewatripont(2014) (stressing the importance that eective bail-in regimes should ensure that banks have ‘sucient long-term securities that can be bailed-in before deposits start to face risk’.). 132See L. Allen(1988) (‘e interest margin is the dierence between the weighted average of yields on assets (inter revenue) and liabilities (interest expense) - also called the banker’s markup.’). 133See SEC(2015) (reporting underwriting fees of 1-1.5% for corporate bond oerings); Manju(2018) (reporting underwriting fees of 0.7% for U.S. corporate bonds). 134See Cai et al.(2007) (nd no signicant underpricing for investment grade bonds and an underpricing of 47bps for high-yield bond oerings). 135See Angbazo(1997) (reporting an average interest margin of 4.229 percent for a U.S. sample between 1989-1993); Nguyen(2012) (reporting an average interest margin of 4.3664 for commercial banks in a group of 28 ‘nancially liberalized’ countries between 1997 and 2004). 136See Federal Reserve Bank of St. Louis(2020) (reporting a maximum net interest margin of 4.91 percent in Q1 1994, a minimum net interest margin of 2.95 percent in Q1 2015 and a recent net interest margin of 3.31 for Q4 2019).

180 the investment and liquidity layer.137 Credit market regulation With respect to the regulatory costs, it is equally dicult to make a direct comparison. In terms of regulatory capital cost, however, the above account of the regulatory seing in the period prior to the nancial crisis, seems to oer an indication that the market-based allocation faced lower costs. Both purely SEC-regulated broker-dealers, as well as market-based (securitized) assets held on bank’s balance sheet, were able to take advantage from regulatory arbitrage opportunities between the banking and the trading book.138 Bank regulation With respect to primary ‘activities’, banks have the distinct regulatory privilege of being able to accept retail deposits without having to segregate such deposits or issue a credit security for every single loan they originate and/or deposit they accept. is allows them to pre-nance loans on a blind pool basis. Unlike credit markets, where exposure is traded in the secondary market, a bank’s secondary ‘activities’ are characterized by utmost illiquidity. Given that bank loans are held-to-maturity on the bank’s balance sheet, nanced by short-term, demandable debt, banks are inherently fragile institutions. Due to this intentional maturity mismatch and the associated risks of ‘bank runs’, banking regulation oen imposes stringent capital requirements.

4.4.3 Diversication layer

While the disclosure and information layer governs the ow of information and the investment and liquidity layer the ow of funds between the surplus and decit agent, the diversication layer is concerned with the optimal parceling of investments by the surplus agent. e diversication layer is key for a comprehensive understanding of credit. Most investors access credit exclusively through the diversication layer. Rather than investing in single credit issuer bonds, they obtain credit exposure through bank deposits, fund vehicles or pension funds. e essence of diversication is a shi of economic exposure away from a single source of risk towards multiple sources. Traditionally, diversication in credit occurs through the banking rm or through specialized pooling vehicles, which aggregate funds from investors and depositors and spread the economic exposure over multiple credit issuers. is is referred to as ‘economic diversication’. As outlined in more detail in chapter 1, economic diversication has been covered extensively in the nance literature, most prominently through the capital asset pricing model (CAPM) and the ecient market hypothesis (EMH). Against the backdrop of these dominant streams in the nance literature, the general notion is that investors are generally best served if they can invest in a fully diversied ‘market portfolio’. However, with respect to credit, the more signicant diversication occurs, not on the actual economic asset level, but rather through the interaction with the state. ereby, the risk is shied from individual credit issuers or pools of credit issuers (economic diversication) to the national economy as a whole. Such diversication can either be provided for through explicit legal guarantees, such as the federal deposit insurance, or through implicit guarantees, such as those provided to too-big-to-fail nancial institutions. Within the scope of this thesis, this form of diversication is referred to as ‘legal diversication’.

4.4.3.1 Costs of the market

Diversied exposure to the credit markets comes in many avors, ranging from retirement saving plans, to xed-income mutual or ETF funds and asset-backed securities (ABS) with pooled credit exposure. ese are not exclusive, but can rather form part of an institutional hierarchy of entities and vehicles providing market participants with economic di-

137See Ho and Saunders(1981) (providing a theoretical model for the net interest margin without diversication, but noting that diversication would need to be addressed in an extended version ‘is paper has developed a model of bank margins or spreads in which the bank is viewed as a risk-averse dealer. […] Extending the model from a structure with one kind of loan and deposit to loans and deposits with many maturities should lead to further interesting insights into margin determination especially as ”portfolio” eects may become apparent.’). 138See Milcheva(2013) (empirically investigating regulatory arbitrage on broker-dealer’s balance sheets ‘Regulatory arbitrage is resulting from a dierent treatment of capital requirements between the banking systems of dierent countries/regions or between the traditional and the shadow banking system. Lightly regulated nancial intermediaries open the door for traditional banks to escape tight capital requirements, as the former require less or no regulatory capital.’).

181 versication. Unlike for diversication under the banking rm structure, economic diversication in the credit markets is typically associated with specialized diversication intermediaries, which are subject to their own set of regulations. While market-facing rms have become highly eective at providing economic diversication, the scope of legal diversication in credit markets is very limited in scope.

4.4.3.1.1 Economic diversication

As mentioned above, economic diversication in credits markets can be provided by a range of dierent institutions. ese institutions vary signicantly in terms of diversication levels, transparency and regulation.

• Pension funds: Pension funds are generalist diversication vehicles, which diversify long-term retirement savings across both liquid and illiquid asset classes. Pension funds operate outside the scope of security laws. ey are explicitly excluded from the denition of an investment company by section 3(c)(11) of the Investment Companies Act.139 Instead, they are regulated by specic rules and agencies, in particular the Department of Labor (DOL) under the Employee Retirement Income Security Act (ERISA).140 On the xed-income side, they mainly invest in corporate and treasury bonds and xed-income mutual funds and ETFs.141 While dened-benet pension funds can theoretically also invest directly in traditional bank-mediated credit, such as corporate loans and mortgages, their exposure to private credit has historically been rather limited.142 With the macro shi from dened-benet pension plans to dened contribution plans over the past decades mentioned in chapter 1, it can be expected that the private debt allocation has decreased on an aggregate basis.143 us, despite their ability to invest in illiquid credit assets traditionally mediated through banking rms, pension funds have historically not been able to provide a market-based alternative to bank nancing. Within the entire diversication stack, they are structurally placed above SEC-regulated pooling vehicles, raises the overall compliance costs even further as diversication materializes over multiple layers. To illustrate, where a pension fund invests in a xed-income mutual fund, there are compliance costs (i) at the single credit issuer level, (ii) the mutual fund level and (iii) the pension fund levels. us, a credit market transaction by an individual bond issuer that is routed through this ‘full stack’, is subject to three dierent layers of regulation and supervision. In contrast, where the same bond creditor would route the same economic transaction as a bank loan through the banking system, they would be subjected to only a single layer of regulation.

• Fixed-income mutual funds and ETFs: Mutual and exchange-traded funds (ETFs) oer highly diversied exposure to corporate bonds, treasuries and (to a limited extent) asset-backed securities. ETFs and mutual funds are subject to extensive securities regulations, which are set forth in the Investment Company Act144 and the associated SEC rules.145 As outlined in more detail in chapter 1, the principle of ‘redeemability’ is deeply ingrained in

13915 U.S.C. § 80a–3(c)(11). 140Where the plan is sponsored by private sector employees the pension plans are regulated by the U.S. Department of Labor (DOL) under the Employee Retirement Income Security Act (ERISA), 29 U.S.C. §§ 1001-1461, or, where it involves a dened benet pension fund, by the Pension Benet Guaranty Corporation under Title IV of the ERISA, 29 U.S.C. §§ 1301-1311. Where the plan is sponsored by public sector employees, it is regulated by the Oce of Personnel Management (OPM) under the Civil Service Retirement Act (CSRA), 5 U.S.C. §§ 8331-8351. 141See Pagnoncelli, Cifuentes, and Denis(2017) (‘From a structural viewpoint, most pension funds share certain common features. ey all have mid- to long-term investment horizons; enjoy a rather stable inow of funds; and invest primarily in equities and bonds. For example, at the end of 2014 U.S. pension funds had 63% of their portfolios invested directly in bonds and stocks. e total exposure to these assets was probably slightly higher since pension funds also invest in mutual funds which, in turn, hold these two types of assets. e exposure to alternative assets (such as mortgages, infrastructure loans, private equity, and structured products) was minor compared to the exposure to stocks and bonds. Although in general there has been a global trend to decrease the overall stocks-and-bonds holdings, and increase the positions in alternative assets, stocks and bonds remain dominant.’). 142See Bajtelsmit and Worzala(1995) (detailing an exposure of 1.1 to 2.9 percent to mortgage credit (excluding securitized mortgage exposure) for the timeframe between 1981 and 1993); Nauman(2020) (detailing a recent increase to private credit ‘Both the $227bn California State Teachers Retirement System (Calstrs) and the $215bn New York State Common Retirement Fund have identied private credit as an opportunity for investors that have enough liquidity to lend to struggling companies. Meanwhile, data from FT Specialist publication MandateWire shows that numerous other public funds in the US are also looking to make similar investments. For example, the $32bn Connecticut Retirement Plans and Trust Funds approved a $1.5bn allocation to private credit last month.’). 143See Zingales(2009) (‘In 1975, the value of privately held pension assets represented only 18% of the gross domestic product (GDP) and 70% was represented by dened benet plans, which did not directly expose workers to nancial market risk; today, pension assets represent 60% of the GDP, 70% of which is in dened contribution plans and thus exposed to nancial market risk.’). 14415 U.S.C. §§ 80a-1 to 80a-64. 14517 C.F.R. §§ 270.0-1 to 270.60a-1.

182 the Investment Companies Act and, through multiple statutory provisions,146 eectively limits the ability of funds to make substantial investments in private credit. us, these fund vehicles traditionally invest only in securitized market-based credit, in particular corporate bonds and treasury securities. In addition to regulating credit at the single issuer level, this introduces another regulation layer. Within the entire diversication stack, mutual funds and ETFs sit somewhere in the middle: they are less diversied than pension funds, but typically more broadly diversied than structured credit securities (ABS or CDOs).

• Structured credit: As outlined above, neither pension funds nor mutual funds have substantial direct exposure to private credit. Given the high operational costs of loan origination and servicing, banks have traditionally dominated retail and SMB loans. However, starting in the 1970s and with an accelerating pace aer the 1980s Savings and Loan Crisis, such loans have been pooled and securitized.147 In such asset-backed securities transactions, pools of smaller-sized loans, most notably mortgages, are aggregated and sold as single securities. e loans are typically collateralize by real assets, such as residential real estate. Single ‘pass-through’ ABS can be further aggregated into lager pools of loans, known as ‘collateralized debt obligations’ (CDOs). Economically speaking, the economic exposure obtained through ABS and CDO securities can be compared to that of traditional commercial bank’s balance sheet. Mortgage-backed securities (MBS) make up the largest segment of asset-backed securities. Prior to the nancial crisis, MBS would be oered, both by private originators, in particular investment banks, as well as government-sponsored enterprises (GSE). As outlined above in section 4.4.2.2.2, in the aermath of the global nancial crisis, the private-label MBS market has almost completely collapsed, while agency MBS issues by GSEs have blossomed. Notably, all MBS issued or guaranteed by Freddie Mac or Fannie Mae are exempt from the registration requirements of the Securities Act under the respective legislative charters148 and GSEs do not have to le registration statements with the SEC for such securities. Instead of the SEC, Freddie Mac and Fannie Mae are regulated by the Federal Housing Finance Agency (FHFA), which was established in 2008 through the Federal Housing Finance Regulatory Reform Act.149 In other words, the ABS diversication vehicles operate to a large part in the shadows of security laws. While exemptions from security laws may have reduced compliance costs, the resilience and boom in agency MBS can primarily be explained by means of legal diversication (see below). In the hierarchy of the diversication stack, asset-backed securities operate at the lower end of the spectrum, oering relatively concentrated credit risk buckets at the single security level.

4.4.3.1.2 Legal diversication

As a general rule, credit allocated over the market does not oer any legal diversication through government insurances or guarantees. When corporate bonds default, creditors bear the full costs. It is the function of economic diversication vehicles to limit exposure to single credit issuers, such that defaults can be optimally absorbed within the portfolio allocation. However, the agency residential mortgage-backed securities (RMBS) segment oers an exception to this general rule. For this segment of the market, both implicit and explicit means of legal diversication exist, which eectively spreads the credit exposure from individual issuers to the government (and thereby to the economy at large):

• Explicit legal diversication: While the Government National Mortgage Association (GNMA or Ginnie Mae) does not originate or purchase loans itself, it provides government guarantees for government-approved securities

146For example, Section 22(e) gives investors in open-ended funds a right to demand prompt redemption and compels such funds to make payment on the investor’s redemption request within seven days of receiving the request. See 15 U.S.C. § 80a–22 (e)). 147See Deku and Kara(2017) (‘Private enterprises started to securitise assets in the 1970s. Innovative new products, such as the REMICs, and weaknesses of Basel I capital requirement regulations for securitised assets were the main drives of securitization markets in the 1980s and 1990s. e market showed an immense growth in the 2000s prior to the 2007–2009 nancial crisis and came to a halt aer the crisis.’). 148For Freddie Mac, 12 U.S.C. § 1455 (g) (‘All securities issued or guaranteed by the Corporation (other than securities guaranteed by the Corporation that are backed by mortgages not purchased by the Corporation) shall, to the same extent as securities that are direct obligations of or obligations guaranteed as to principal or interest by the United States, be deemed to be exempt securities within the meaning of the laws administered by the Securities and Exchange Commission.’), for Fannie Mae, 12 U.S.C. § 1717 (c)(1) (‘Participations or other instruments issued by the Association pursuant to this subsection shall to the same extent as securities which are direct obligations of or obligations guaranteed as to principal or interest by the United States be deemed to be exempt securities within the meaning of laws administered by the Securities and Exchange Commission.’). 14912 C.F.R. § 1200.1.

183 backed by single-family and multifamily loans. As a government agency, Ginnie Mae is able to provide explicit legal diversication guarantees, backed by the full faith and credit of the federal government.150

• Implicit legal diversication: Prior to the nancial crisis in September 2008, the guarantees of the GSEs Fannie Mae and Freddie Mac relied on an implied federal government guarantee only. In fact, to this date, agency MBS bonds explicitly disclaim any federal government sponsorship.151 Prior to the nancial crisis, despite the modest guarantee fees,152 creditors already assumed from the privileged nature of the GSEs and the strong government involvement in the MBS market that the federal government would step in if agency MBS were to default at scale. In the wake of the nancial crisis, as the GSEs were placed in government conservatorship, the market proved to be right about this implicit guarantee. Although there is to date no explicit government guarantee, Fannie Mae and Freddie Mac have since enjoyed a de facto federal guarantee. Firstly, the Treasury has been, through the preferred stock agreements, commied to keep the GSEs alive. Furthermore, the Treasury and Federal Reserve demonstrated their backing of the agency MBS market through the massive purchases of GSE obligations and securities.153

4.4.3.2 Costs of the banking rm

In contrast to credit markets, depositors who put their money in the bank are provided with immediate global diversication. Crucially, the very nature of bank deposits is that they oer both economic and legal diversication.

4.4.3.2.1 Economic diversication

A bank deposit is a short-term credit agreement between a depositor and a bank. It is demandable, unsecured debt, which provides the depositor with economic exposure to the bank’s entire balance sheet. Depositors implicitly nance all loans the bank originates and holds at any point in time. Whether it is mortgages, consumer credit, corporate loans or municipal loans, depositors are backing them in full. e scope of economic diversication can vary considerably between banks. A community bank, which only engages in retail banking activities, is naturally less diversied than a multinational bank with multiple lines of business. e community bank’s balance sheet is by its nature much smaller, oering exposure to a regionally concentrated loan portfolio only. In contrast, a multinational bank may originate loans across the nation, have multiple lines of business and operate across borders. ere exists a broad nance literature discussing the optimal diversication of banks among dierent activities.154 Furthermore, the bank does not disclose to depositors the nature and extent to which it diversi- es. Economic diversication through the banking rm takes place behind the closed curtains of the banking rm. As discussed under the disclosure and information layer, this constitutes a key operational and regulatory saving under the bank allocation. Furthermore, as will be discussed below, bank depositors have no incentive to actively monitor the

150See Dunn and McConnell(1981) (’e mortgage loans which back a GNMA security are composed of three values-default-free nancing, default insurance, and servicing. With a GNMA security, the servicing is provided by the security issuer, while the U.S. Government provides the default protection. As a consequence, the value of a GNMA security is the value of the default-free nancing.’). 151 Federal Housing Enterprises Financial Safety and Soundness Act of 1992, Pub. L. 102-550 §§1302(4), 1381(f)[at 3996], 1382(n)(2), 106 Stat. 3941 (Oct. 28. 1992). 152Present Condition and Future Status of Fannie Mae and Freddie Mac, Hearing before the H. Subcomm. on Cap. Markets, Ins., and Gov. Sponsored Enterprises, at 27 (June 3, 2009). 153CONGRESSIONAL BUDGET OFFICE, FANNIE MAE, FREDDIE MAC, AND THE FEDERAL ROLE IN THE SECONDARY MORTGAGE MARKET 9-10 (Dec. 2010). 154See V. Acharya, Hasan, and Saunders(2006) (‘ere are several reasons why the focus versus diversication issue is important in the context of FIs and banks. First, FIs and banks face several (oen conicting) regulations that create incentives either to diversify or to focus their asset portfolios, such as the imposition of capital requirements that are tied to the risk of assets, branching and asset investment restrictions, and so forth. Hence, from a policy standpoint, it is interesting to ask whether FIs and banks benet or get hurt from diversication of their loan portfolios.’); Yang, Liu, and Chou(2019) (‘Diversication is benecial for an individual bank since it can increase the bank’s resilience to shocks. However, if all banks diversify they may end up holding similar portfolios. Hence, when the economy is hit by a strong shock, all banks may be aected and fail or have diculties at the same time. us, an unintended consequence of diversity at the individual bank level can be an increase in systemic risk.’); Beck, Demirguc-Kunt, and Levine(2006) (Outlining the conicting theories on the relationship between concentration of the banking industry and banking system fragility ‘First, concentrated banking systems may enhance market power and boost bank prots. High prots provide a “buer” against adverse shocks and increase the charter or franchise value of the bank, reducing incentives for bank owners and managers to take excessive risk and thus reducing the probability of systemic banking distress […] Second, advocates of the “concentration–fragility” view argue that (i) relative to diuse banking systems, concentrated banking systems generally have fewer banks and (ii) policymakers are more concerned about bank failures when there are only a few banks.’).

184 economic diversication levels, given the legal diversication of bank deposits. However, while depositors are le in the dark about bank diversication, bank diversication is still subject to regulatory supervision. Bank regulation of diversication levels Diversication at the bank level is implicitly regulated through a number of capital adequacy measures, which aim to address credit, market and operational risks. More explicitly, bank regulations address diversication levels through large exposure rules. On a global level, the Basel Commiee announced new standards for exposure limits in 2014.155 According to these rules, banks have to report to regulators all ‘large’ exposures exceeding 10 percent of their Tier 1 capital and the value of an exposure to a single counterparty should not exceed 25 percent of the Tier 1 capital. In the US, this Basel standard was implemented in 2018 through Regulation YY, which implemented section 165(e) of the Dodd-Frank Act. Said rule under the Dodd-Fank Act required the Federal Reserve to impose limits on the amount of credit exposure. Under the nal rule, a bank with $250 billion or more in total assets is prohibited from having aggregate net credit exposure in excess of 25 percent of its tier 1 capital. us, in essence, while bank diversication levels are subject to certain regulatory oversight, this regulation is part of the general regulatory oversight and, if at all, adds only marginal additional regulatory costs. Despite such regulation, banks still enjoy substantial leeway in terms of diversication, especially in the absence of creditor monitoring.

4.4.3.2.2 Legal diversication

e notion of legal diversication is fundamental for the understanding of the banking rm. Whether through explicit or implicit government guarantees, legal diversication is what gives the banking rm an unfair advantage over a large portion of the credit markets. In the famous rst bank failure in the United States in 1809, the Farmer’s Exchange Bank in Gloucester bank held roughly $580,000 in liabilities against assets of $86.46 and the bank’s creditors lost everything when the bank went under.156 us, before modern bank regulation, there existed strong monitoring incentives by holders of demandable unsecured debt. If there existed some indication that the bank sat on bad credit, depositors would engage in the classical ‘bank run’.157 is is no longer the case today, as rm-specic regulation has eliminated the direct economic risk exposure of the banking rm’s depositors.158 Today, depositors can be rationally indierent to what goes on within the bank’s balance sheet. Whether the bank manager issues only bad credit or not or whether he fails to diversify among many creditors, the depositor can still sleep calmly, as he can rely on means of legal diversication. us, only shareholders and regulators have the incentives or mandate, respectively, to monitor the diversication levels of banking rms.159 Explicit legal diversication Under the bank allocation, explicit legal diversication most notably emerges from the the $250,000 deposit insurance provided by the Federal Deposit Insurance Corporation (FDIC).160 e FDIC manages a large national fund nanced by payments from its member banks, known as assessments.161 Signicantly, since this fund is backed by the ‘full faith and credit’ of the United States,162 the surplus agents do not need to worry about whether this macro fund itself could go

155See Bank of International Selement (BIS)(2014) (‘One of the key lessons from the nancial crisis was that banks did not always consistently measure, aggregate and control exposures to single counterparties or to groups of connected counterparties across their books and operations. […] Large exposures regulation has been developed as a tool for limiting the maximum loss a bank could face in the event of a sudden counterparty failure to a level that does not endanger the bank’s solvency.’). 156See Nelson(2015) (‘For the rst time in U.S. history, Americans realized just how safe “their money” was in the hands of a bank. In fact—they realized—the money they deposited into their bank was only as safe as the bank holding it.’). 157See Shin(2009) (‘Bank runs were not uncommon in the United States up through the 1930s, but they have been rare since the start of deposit insurance backed by the Federal Deposit Insurance Corporation.’). 158See O’Hara and Shaw(1990) (‘Advocates argue that by removing the incentive to ”run”, deposit insurance protects individual nancial institutions from instability in the intermediation process, thereby providing stability to the nancial system as a whole.’). 159See Garten(1989) (‘From the perspective of the equity holder, diversication is an eective way of reducing overall risk in an investment portfolio. If bank safety is seen from this point of view, similar advantages may be expected from diversication of activities within banks. […] Recent changes in bank regulation have encouraged this portfolio approach to bank diversication.’). 16012 U.S.C. § 1821(a)(1)(E) (‘For purposes of this chapter, the term “standard maximum deposit insurance amount” means $250,000’). 16112 C.F.R. § 327.3. 162FDIC, Full Faith and Credit of U.S. Government Behind the FDIC Deposit Insurance Fund, 87 Op. FDIC Counsel 36 (1987); 12 U.S.C. § 1828(a)(1)(B) (2006).

185 bankrupt. In other words, from the perspective of the surplus agents, i.e. the depositors, the rm-specic regulation of banks provides for full and adequate diversication. Implicit legal diversication Implicit legal diversication emerges through implicit government guarantees. Given the implicit nature of this form of diversication, there are no statutes and provisions to point to. To the contrary, where a guarantee is not explicit, governments have an incentive to actively discourage the markets from assuming implicit guarantees. e bail-in measures introduced in the aermath of the nancial crisis can be seen, in part, as a way for the legislators to discourage implicit guarantees going forward.

4.4.3.3 Comparative pricing

Again, we have to ask ourselves, how the costs of the market compare to those of the bank allocation. In particular, we have to ask this with respect to the costs of regulation. Economic diversication As outlined above, the credit market diversication layer is fragmented, both institutionally and from a regulatory perspective, ranging from pension funds to investment funds and asset-backed securities. While the SEC-regulated pooling vehicles, such as xed-income mutual funds or ETFs, have proven to be highly eective at providing economic diversication, they also introduce another intermediation and regulation layer. e baseline costs of this intermediation layer can range from c. 1-1.5% for actively managed funds to 0.3-0.9% for passive funds.163 On the other hand, banks provide economic diversication by exposing depositors instantly and comprehensively to the bank’s entire balance sheet. As such, the level of diversication can vary considerably, depending on the size of the bank’s balance sheet. While there are some compliance costs with respect to rm-level diversication, these are part of the general bank supervision regime. As such, unlike under the market allocation, diversication does not introduce another layer of regulation. Empirically, the costs of diversication under the bank allocation can be considered as a hard-to-quantify fraction of the net interest margin, which can range from c. 3-4%.164 Legal diversication With respect to means of legal diversication, the banking rm seems to have an ‘unfair competitive advantage’ over the market allocation. In particular, banks enjoy the privilege of extensive government guarantees, both explicit and implicit ones. rough deposit insurance schemes, depositors are instantly and comprehensively hedged on the principal of their deposits. With respect to credit markets, on the other hand, the means of legal diversication are restricted to a limited xed-income credit market segment (treasuries, agency RMBS market). Summary From the above, it appears that the regulatory costs of providing comprehensive diversication is substantially lower under the banking allocation than it is for the market allocation. is is primarily driven by means of legal diversication, in particular deposit insurance schemes oering explicit legal diversication.

4.5 Second part of the eorem

163See Guercio and Reuter(2014) (detailing expense ratio of 0.99-1.57% for actively managed funds and 0.37-0.86% for index funds). 164See section 4.4.2.5.3 on empirical realizations of the net interest margin above.

186 assignment of rights and obligations does not aect the eciency of the nal allocation.165 In the tort context, the Coase eorem thus holds that, whether the polluter or the neighbor is held liable for the costs of the negative externality, does not aect the eciency of the nal allocation in a frictionless regime. In the context of credit markets, this means that as transactional costs approach zero, the legal assignments of rights and obligations to either the investor or the issuer under security laws has diminishing eects on the eciency of the nal outcome. For example, where relevant data about the issuer become freely and openly available at the disclosure layer, whether security laws mandate disclosures by the issuer or pass responsibility on investors to gather information does not aect the eciency of the nal allocation. Similarly, as the costs of diversication and maintaining an active primary and secondary market approach zero, the relevance of the implicit or explicit legal assignment of these costs by security laws diminishes. Since the frictionless regime of the Coase eorem is a stylized scenario which does not reect economic reality, our concrete analysis of the equity market cost ‘externality’ below is guided by the work of Guido Calabresi, who holds in the seminal book ‘e Costs of Accidents’ that an optimization should (i) minimize the sum of the costs of the externality, including the costs of regulating the externality and (ii) an assignment of the costs of the externality to the most ecient cost-avoider.

4.5.1 Disclosure and information layer

4.5.1.1 Misinformation externality

At the disclosure and information layer, the relevant negative externality is misinformation. As outline in more detail in chapter 1, the misinformation externality results from the absence of information production or through false and misleading information production. us, information production costs constitute the relevant transaction cost related to this externality. e key insight of the second part of the eorem is that in the absence of transaction costs related to the market externalities, the legal assignment of rights and obligations under security laws, does not aect the eciency of the nal allocation. e second part of this eorem thus holds that with transaction costs (information production costs) approaching zero, whether security laws assign (i) mandatory disclosure obligations on the issuer or (ii) data gathering obligations on the investor does not aect the eciency of the nal allocation. In a positive transaction cost seing, however, the initial assignment of rights and obligations by the law does maer. In this respect, an optimal regime is one that minimizes the costs of the market and assigns them to the least cost avoider, the party that is best positioned to reduce the costs of the market. us, in the context of the disclosure layer, the objective is to reduce disclosure and information costs and assign these costs to the party best positioned to reduce them. In the below chapters, a (hypothetical) optimal regime is explored, which disaggregates the monolithic disclosure costs such that it allows for a more nuanced assignment of costs to the least cost avoider.

4.5.1.2 Optimal regime

In this section, an optimal disclosure and information regime under the eorem is proposed, which applies the blueprint of an optimal regime presented in chapter 1 of this thesis. In particular, it proposes to ‘disentangle’ or ‘unbundle’ the production of raw data and information (ground truth data) from data processing and analysis:

• Ground truth data layer: Ground truth data refers to electronic, raw and unprocessed data sources, which the surplus agents can credibly rely on when making the investment decision. Such data sources can be nancial or other alternative business data sources. 165See Coase(1960) (‘It is necessary to know whether the damaging business is liable or not for damage caused since without the establishment of this initial delimitation of rights there can be no market transactions to transfer and recombine them. But the ultimate result (which maximises the value of production) is independent of the legal position if the pricing system is assumed to work without cost.’).

187 • Data processing and analysis layer: Under data processing, the preparation of raw data sources for the end user is understood. In particular, this can entail the aggregation of ground truth data entries into consolidated information sets. In contrast, data analysis refers to the use of processed data as an input for a wider range of calculations and model-based inferences, oen in combination with external data sets.

e goal is to conceptualize a hypothetical, cost-eective mode for dierent credit market segments to disseminate relevant information to creditors. Under the current design of security laws, disclosures oen rely on highly aggregated data that is reviewed and aested for by costly information or nancial intermediaries, such as accountants or structured credit sponsors, which sit between creditors and borrowers. e emphasis thus lies on the data processing and analysis layer, where the bulk of costs are incurred in practice. What is ultimately presented to investors has oen gone through multiple rounds of human processing, review and aggregation. However, we know that such human processing is slow, biased and costly. Furthermore, these costs are shouldered primarily by issuers. us, the approach presented here explores how a clear separation of the data processing and analysis layer from the ground truth data layer would allow for a more nuanced cost assignment.

4.5.1.3 Optimal regime: ground truth data layer

In the credit markets, ground truth data relating to corporate bonds, mortgage credit, auto loans and unsecured consumer credit sits in diverse data silos. In particular, it can be found (i) at the level of the rm, (ii) at the sponsor level, (iii) at the level of the collateral, namely the property or other asset that secures the loan, or (iv) at the level of the individual, the person taking out the consumer loan.

4.5.1.3.1 Corporate bonds

Under the current disclosure regime, corporate bond oerings primarily rely on aggregated nancial data, in particular consolidated nancial reporting. In chapter 1, an optimal ground truth data ow is presented for the context of nancial reporting. In particular, based on the proposition of former SEC Commissioner Wallman(1997), the option of disaggregated nancial data disclosures is explored. In such a ground truth disclosure regime, credit issuers would be able comply with their basic disclosure obligations by linking a set of ground truth data sources to a regulatory API specied by the SEC. In chapter 2, this has been further tailored to the context of technology startups, by highlighting industry-specic alternative data sources. As corporate bonds are nancing instruments for more mature, cash-ow stable companies, the technology startup ‘shareholder lens’ materially diers from the corporate bondholders ‘creditor lens’. As a result, the ground truth data sources may dier considerably as well:

• Financial data sources: nancial information is a crucial source for bondholders to assess the ability of corporate borrowers to repay their debt.166 In the context of technology startups in chapter 2, cloud-based accounting soware solutions have been identied as a potential ground truth data source for nancial data. In contrast, larger companies oen rely on the soware solutions of SAP and Oracle for ERP and accounting purposes, as these solutions are beer tailored to more complex accounting operations encountered in larger corporations. However, the principle of digital data storage and processing remains the same: cloud-based or on-premise accounting soware is used to digitally record booking entries and prepare reports for shareholders, creditors and tax authorities. An ecient ground truth data pipeline in the sphere of corporate bonds could link directly to these accounting systems. At the extreme end of the transparency spectrum, such a data feed could even provide highly granular booking-level data, allowing creditors to trace every transaction. In contrast, past accounting scandals and corporate bond defaults have oen been preceded by nancial disclosures that have provided very lile information at the transaction level.167 In addition to general nancial accounting data, a corporate issuer’s

166See Hartzmark(2011) (‘An important concern to the bondholders is the overall nancial health of the company, which determines the ability of the company to pay the promised series of coupons and the principal amount.’). 167See Kroger(2005) (noting with respect to the Enron default ‘e disclosures also showed that Enron was not interested in providing investors with a straightforward explanation of these transactions. On the contrary, the disclosures were incredibly opaque: the names of the ”related parties”

188 cash ows and cash reserves are particularly important data points for corporate bondholders.168 us, ground truth data pipelines could be established to the issuer’s cash accounts, with the depository institutions providing the data feed.

• Alternative data sources: In chapter 2, a number of industry-specic alternative metrics for technology startups are discussed in more detail. ese data sources have focused more on indicators of company performance and growth, which is the primary concern for equity holders. However, company growth and the related equity upside is of secondary interest to creditors. Wallman(1997) has pointed to a wide range of non-nancial data sources and forward-looking information, including patent or customer cohort data.169 For corporate bonds, the most relevant alternative, non-nancial data sources relate to the collateral of secured bonds. Mann(1997) has pointed to the substantial costs involved with creditors acquiring information related to the collateral.170 As the collateral assets used to secure corporate bonds can include real estate, plants and machinery and even patents, the potential data sources for a ground truth disclosure pipeline vary widely. Due to this heterogeneity, a range of public and privately operated title registries could provide meaningful data pipelines.

As outlined in more detail in chapter 1, ground truth data pipelines for corporate issuer of securities could allow investors to access granular company data through a public investor API. In the extreme, this could entail access to accounting information in its entirety, in real-time and without any ‘window dressing’. In other words, it would ensure both data lineage and provenance. However, such a high level of transparency is unlikely to immediately garner the full support of credit issuers and stakeholders. Under a more realistic scenario, the SEC could dene a data schemata and aggregation level for the API, which would cater to creditors’ information requirements, but at the same time also address the issuers’ condentiality concerns. Such a ‘lter’ could, for example, only disclose data at quarterly intervals or at pre-specied higher aggregation levels. However, under such a solution, it would still be a fundamentally ‘passive mode’ of disclosure. While the specications of the regulatory API would have to be met, the issuer would not have to actively prepare and le data in addition to linking the ground truth data source. Ground truth data sources in rm-based corporate credit While it may appear far-reaching from a privacy perspective that credit issuers would grant bondholders such granular data access, the practice of disclosing relevant information through ground truth data pipelines is already very much a reality in rm-based credit. One interesting example is the ntech technology startup Kabbage, which provides loans to small and medium-sized businesses (SMBs). Kabbage nances the originated credit through multiple asset-backed securitization (ABS) vehicles, totaling $940 million in credit commitments at the time of writing.171 Crucially, the ntech underwriter is actively using multiple third-party ground truth data sources. Kabbage started out serving Ebay power sellers, with a fully digital and easily traceable business track record, but now serves a much wider range of SMBs. Loan applicants provide the company with access to their various nancial record accounts by linking in the respective APIs to the Kabbage platform. rough these ground truth data pipelines, the rm can make a decision on a loan application in just minutes.172 were not revealed, the identities of the ”senior Enron executives” involved in the transactions were not provided, the purposes of the deals were not disclosed, and the descriptions of the deals themselves were so convoluted, no one would be able to understand what the transactions actually involved.’). 168See Hartzmark(2011) (‘Highly rated investment-grade bonds rarely default. In other words, rms issuing investment-grade bonds have adequate cash ows to cover current interest and principal payment obligations and sucient assets to back up the long-term payment obligations.’). 169See Wallman(1997) (‘In addition, and in particular, this shi would further our ability to convey, more easily, data that is increasingly viewed as critical to an understanding of knowledge-based companies. Such information might include non-nancial or forward-looking information, as well as information such as the number of patents obtained or their value or revenues generated relative to research and development expenditures, or the number of repeat customers among businesses in a particular industry.’). 170See Mann(1997) (‘ese costs, of course, are primarily the costs of acquiring information about the value of the collateral and the borrower’s title to it. In an unsecured transaction, creditors focus on the creditworthiness of the borrower as a whole. When the borrower is publicly traded, creditors readily can obtain information without any additional expense, either from lings required by the securities laws or from the eorts of analysts evaluating the value of the company’s outstanding securities. erefore, the existence of detailed nancial information about public companies can make unsecured loan transactions considerably less expensive than comparable secured transactions.’). 171See Kabbage(2019) (‘Kabbage, Inc., a global nancial services, cash ow technology and data platform for small businesses, closed the largest asset-backed securitization (ABS) by a small business online lending platform to date for $700 million.’) 172See Reader(2015).

189 “e company delivers verdicts on business and consumer loans in six minutes. Kabbage considers a business’s banking information and ickBooks data, as well as circumstantial data–like customer interactions on a Facebook business page–in determining whether to grant up to $100,000 credit lines to small businesses. In total, it refers to an average of ve data points per loan applicant, though it has relationships with 23 data providers including Amazon, Bigcommerce, eBay, Etsy, Facebook, Google Analytics, Intuit, PayPal, Shopify, Sage, Square, Stripe, Twier, Xero, Yahoo, and Yodlee.”

While rm-based credit origination diers materially in terms of privacy from market-based credit, in that data is disseminated condentially to the lender only, the above example clearly demonstrates (i) the benets in terms of cost, timing and transparency of ground truth data pipelines, as well as (ii) the plurality of potential ground truth data sources.

4.5.1.3.2 Structured credit transactions

In the sphere of structured credit transactions (ABS, MBS or CDOs), imagining an optimal design of ground truth pipelines is a complex task. Before we begin our enquiry into the optimal, it is important to rst shed some light on the existing frictions in the disclosure layer. Broken ground truth pipelines during the nancial crisis of 2008 and regulatory response In the wake of the nancial crisis in 2008, it appeared that many investors and other market participants, including rating agencies, had materially failed to understand the risks of private-label structured credit and were unable to properly value it.173 Among many reasons, a key reason why many of the securities turned illiquid was the lack of robust ground truth data pipelines.174 e full extent to which ground truth data was missing in the private-label ABS market prior to the nancial crisis is best illustrated through an email correspondence that was published during a U.S. House of Representatives hearing in 2008. In this email exchange a Standard & Poor’s credit analyst, in the process of preparing a rating for a particular CDO issuance, asked his line manager for the ‘collateral tapes’ (a term used to refer to loan-level data records) so that he could assess the creditworthiness of the loans backing the CDO. His manager replied as follows:

“Any requests for loan level tapes is TOTALLY UNREASONABLE. Most investors don’t have it and can’t provide it. Nevertheless, we must produce a credit estimate. It’s your responsibility to provide those credit estimates and your responsibility to devise some method for doing so.”175

As outlined in section 4.4.1.1.3, the disclosure frictions in the private ABS market ultimately caused the SEC to develop asset-level disclosure requirements in Regulation AB II, which became eective in 2016.176 Materially, Reg- ulation AB II requires sponsors of SEC-registered ABS securities through Schedule AL177 to provide loan-level credit data through Asset Data Files in XML format (a machine-readable language). e full ABS XML technical specications clearly dene the data schemata and each required loan-level data point.178 However, in the meantime it has become clear that the implementation of this Regulation AB II has eectively shut down what was le of the private SEC-registered ABS market post-GFC.179 Against this backdrop, it seems that the design of this disclosure system remains to be suboptimal. If one would start looking for frictions in the existing

173See Judge(2014) (‘Investors realized that they did not have a good understanding of the actual value of many of the MBSs and other securitized assets they were exposed to and that those assets had signicantly greater downside risks than previously recognized. Investors responded by pulling back, in unison, from such assets and investments backed by them.’). 174See Hu(2012) (‘Regarding pool assets, the depiction is at a highly diuse level: the depiction is required only for the pool assets in the aggregate. No depictions whatsoever are required at the level of the individual assets that make up the pool. is lack of granularity is especially problematic because very subtle dierences in pool characteristics can make huge dierences in the possibility of default. And the depictions at the aggregate or subset level are subject to wide issuer discretion. In general, even the particular characteristics to be described are up to the good judgment of the issuer.’). 175See Commiee on Oversight and Government Reform (COGR)(2008). 176Securities and Exchange Commission (SEC). 2014. Asset-backed securities disclosure and registration, Final Rule. 17 C.F.R. Parts 229, 230, 232, et al. Federal Register 79 (185). 17717 C.F.R § 229.1125. 178Available under hps://www.sec.gov/info/edgar/specications/absxml.htm 179As recently noted by SEC Chairman Jay Clayton, following the enactment of the new disclosure rules in 2014, not a single SEC-registered RMBS oering has been made. See Clayton(2019).

190 disclosure architecture, one may realize that banks operate with proprietary data structures, which make compliance with the SEC’s disclosure schemata burdensome and costly. However, going one step deeper, one could question whether structured credit issuers are indeed the right entry point for disclosures. As Hu(2012) notes, Regulation AB relies on the traditional ‘intermediary depiction model’ where banks issuing ABS and CDOs are required to ‘cra and transmit depictions of reality’.180 While issuers may hold some ‘original data’, much of the loan-level data seems to be buried at a lower level. In the optimal regime explored below, alternative sources of ‘ground truth data’ are explored, which could relief banks from the disclosure burden and at the same time enhance investors’ ability to price credit securities. Optimal regime Given that ABS pool data is basically an aggregation of multiple loans, an optimal regime would focus on building robust ground truth data pipelines at the loan level rst and foremost. In credit transactions, acquiring information about the borrower is traditionally a costly and complicated endeavor,181 which requires the lender to assess the creditworthiness of the transaction party182 or, in secured loans, the quality and value of the collateral.183 To optimally dene a ground truth data layer, the existing best practice data retrieval processes of both market- and bank-based credit transactions have to be replicated. In going one step deeper than the issuer level, the most reliable and pure ground truth data typically sits at local registries, operated by governmental entities as well as private organizations:

• Non-bank lenders: as mentioned above in section 4.4.2.2.2, non-bank lenders, such as icken Loans, Penny Mac and LoanDepot now make up a large portion of loan-level underwriting. ey enter into the original nancing agreements with borrowers and hold all relevant data points relating to the individual loans. As such, they are an immediate ground truth source for contract-level data.

• Mortgage loans: With respect to mortgage loans, Schedule AL of Regulation AB II requires ABS issuers to disclose a number of data points related to the property, including the geographic location, property type and property valuation.184 When draing these requirements, the SEC has realized a number of challenges that may impact the usefulness of these disclosures. Public commenters to the proposed SEC regulations, inter alia, questioned the trustworthiness of valuations provided by ABS issuers,185 requested to include data points related to recent property sales186 and asked for inquiries into prior liens on the property.187 All of these questions and concerns are a direct result of the ‘intermediary depiction model’ that does not provide investors with disaggregated ground level truth data. To begin with, due to privacy concerns,188 the exact properties underlying the mortgage loan portfolios are still not disclosed, making it impossible for investors to conduct their own valuations. With respect to verifying the unencumbered title to the property without prior liens, county-level property registries, which record proper title to land and buildings, would optimally serve as ground truth data sources. With respect to recent property sales and valuations, the most reliable ground truth data source would most likely be found in the existing real estate brokers’ multiple listing services (MLS) data platforms.189

180See Hu(2012) (‘Consistent with how the intermediary depiction model operates with regard to corporations and corporate issuances of securities, the SEC identies explicit items that should be covered in such depictions of ABS and oers guidance as to both the substantive content and its narrative, numerical, and graphical presentation.’). 181See Mann(1997) (‘In the course of closing any type of lending transaction, both parties incur signicant information costs.’). 182See Mann(1997) (‘e lender incurs costs in investigating the merits of the transaction. e lender typically investigates the nancial strength or creditworthiness of the borrower with some care.’). 183See Mann(1997) (‘A secured lender relying on the value of the collateral as a signicant source of repayment also has an incentive to investigate the collateral.’). 18417 C.F.R. § Item 4. 229.1125 (d)(1)-(11). 185See SEC(2014) (‘Another commenter said there is no uniformity in how values are determined because the proposal would allow issuers to select from a long menu of valuation methods, approaches and sources for establishing property values.’). 186See SEC(2014) (‘Commenters seeking more granularity suggested expanding this group of data points to require data about recent property sales, more detail about the characteristics of the property’). 187See SEC(2014) (‘We did not propose a data point to capture the eort an originator or sponsor made to discover if the same property secures other loans, but we asked if this type of disclosure should be required.’). 188See SEC(2014) (‘In light of privacy concerns, we did not propose to require issuers to disclose an obligor’s name, address or other identifying information, such as the zip code of the property.’). 189See Uri(1985) (‘e Multiple Listing Service (MLS) is an organization of real estate brokers that evolved to facilitate the listing and sale of housing. By pooling information, each member gains access to information available to other members. Although independent multiple listing services do exist. the majority are sponsored by local real estate boards simply because the boards were the rst to recognize the obvious advantages of multiple listings.’).

191 • Auto loans: With respect to auto loans, Schedule AL of Regulation AB II requires ABS issuers to disclose a number of data points related to the vehicles that serve as credit collateral, including the vehicle manufacturer, the model year and the vehicles value.190 While the SEC proposed further data points,191 it dropped a number of them in the nal rules. With respect to vehicles, the more reliable ground truth data is currently most likely to be found in the automotive registries maintained by the state level Departments of Motor Vehicles (DMV).

• Individual obligors: With respect to individual obligors, Schedule AL of Regulation AB II requires ABS issuers to disclose a number of data points related to the obligor or lessee, most critically the credit score.192 e SEC initially proposed to only require issuers to disclose credit score ranges,193 but was convinced by public commenters to the proposed SEC regulations that more granular data was required.194 Where credit decisions relate to an individual as the main borrower or guarantor of a credit, the relevant ground truth data typically sits in multiple personal data repositories, such as the borrower’s banking histories, health records, lease records, employment status or insurance claims. In the United States, a wide range of such data is collected by the three main consumer reporting agencies (CRAs): Experian, Equifax and Transunion under the Fair Credit Reporting Act.195 ese credit bureaus then process this data and calculate a comprehensive credit score, the FICO score, using an algorithm that is kept condential.196 In other words, the credit score is itself already the outcome of a data processing step and as such not ‘ground truth data’. In fact, credit scores have oen been criticized for containing material errors, some even claiming error rates as high as 33 to 48 percent.197 us, since credit bureaus obtain their ground truth data from banks and other public records, they are subject to errors at that level too.198 While ground truth data sources would ideally link to individual borrower’s bank statements and public records, access at the level of credit bureaus would already provide a major improvement to issuer-led disclosures.

us, the relevant ground truth data for pricing mortgage and auto loans sits in governmental or private databases. As such, unlike in the case of corporate bond issuers, where relevant ground truth data is centrally hosted with one corporation, building robust data pipelines (in particular through APIs) would require substantially more engineering capabilities as well as regulatory eorts. What could such ground truth data pipelines look like in practice? In many ways, such a disclosure regime could take shape over multiple layers: with individual loan applicants linking their credit score feed at the single loan level and then loan-level underwriters (e.g. non-bank lenders) integrating the single borrower data feed into a contractual data feed, with ABS sponsors further aggregating these feeds when arranging diversied pooling vehicles.

4.5.1.4 Optimal regime: data processing and analysis layer

e optimal regime proposed in chapter 1, further looks at a separate data processing and analysis layer. Data processing refers to the preparation of raw data for end users. In particular, this can entail the aggregation of ground truth data entries into consolidated information sets. Wallman(1997) refers to this aggregation function as ‘compiling’ in the

19017 C.F.R. § 229.1125 Item 3. (d)(1)-(7). 191See SEC(2014) (‘We proposed several data points that only apply to ABS backed by auto leases that relate to information such as residual values, termination, wear and tear, mileage, sale proceeds, and extensions.’). 19217 C.F.R. § 229.1125 Item 1, 3, 4. (e). 193See SEC(2014) (‘We also proposed ranges, or categories of coded responses, instead of requiring disclosure of an exact credit score.’). 194See SEC(2014) (‘We are persuaded by commenters that exact credit scores are necessary to evaluate risk and to appropriately price securities.’). 19515 U.S.C. § 1681. 196See Arya, Eckel, and Wichman(2013) (‘e Fair Isaac Corporation (FICO) developed the formula used by all three major credit reporting agencies in the U.S. e algorithm is kept secret, but most believe that it is based upon the ratio of debt to available credit; this denominator, in most cases, is a direct function of income. e score is then adjusted for payment history, number of recent credit applications, and negative events such as bankruptcy/foreclosure, as well as changes in income caused by changes in employment or family status.’). 197See Rameden(1995) (‘us, the appropriate error rate, using industry gures, is more like thirty-three percent (three million out of nine million). is should serve as an eective lower bound on the error rate of credit reports and is actually consistent with that of Consumer’s Union (48 percent) and James Williams (43 percent).’). 198See Rameden(1995) (giving an example of credit score errors caused by public records ‘No example is more illustrative of this type of error than a widely- reported incident involving the town of Norwich, Vermont. ere, ”an entire community’s good credit was wiped out” by erroneous information provided to TRW’s system. Credit bureaus not only include information from credit grantors but also include information from public records such as civil judgments, arrests, and tax liens. In fact, it was public record information inaccurately reported to TRW that caused nearly the entire town of Norwich to be ”erroneously labeled as credit deadbeats.”’).

192 context of nancial statement preparation.199 In the context of corporate credit, we can think about the accounting rms aggregating the individual accounting entries. Under data analysis, the process of using processed data as an input for a wider range of calculations and model-based inferences is referred to, oen in combination with external data sets. In the sphere of credit oerings, one can think of credit agencies using already aggregated and processed nancial data to make ‘buy’ and ‘sell’ recommendations or assign credit ratings.

4.5.1.4.1 Corporate bonds

With respect to corporate bonds, the data processing and analysis layer typically comprises these functions:

• Data processing: In the context of corporate bonds, if one assumes that the ground truth data feed is composed of raw, disaggregated accounting data, then the data aggregation layer could be understood as the analytical layer that pieces together the individual accounting entries to generate the nancial statements, namely balance sheet, income statement and cash ow statement. By disentangling the ground truth data feed from the consolidated nancial statements, market participants could granularly assess and re-assess whether tricky accounting decisions, such as expensing or capitalizing the purchase of a particular asset, are acceptable under the applicable accounting regime.200

• Data analysis: In the sphere of corporate bonds, data analysis typically entails the assignment of a credit rating to a newly issued bond. is function is traditionally carried out by credit rating agencies, such as Moody’s, Standard & Poor’s or Fitch. e process of assigning a price and rating can range from qualitative assessments, to constructing nancial statement ratios, to running more sophisticated statistical pricing models. e cost of this analysis by credit rating agencies, while previously paid for by investors, is now paid for by corporate issuers.201 Gerding(2009) argues that credit agencies should make their models open-source. 202 is would allow investors to trace individual ratings to underlying data points (data lineage) and make adjustments to the model to reect their own beliefs.

4.5.1.4.2 Structured credit

With respect to structured credit transactions, the data processing and analysis layer has been subject to much scrutiny following the global nancial crisis. Albeit with dierent incentives, both structured credit underwriters and credit rating agencies actively engage in data processing and analysis of ABS and CDO securities.203

• Data processing: with respect to asset-backed securities, the data processing layer consists of pricing individual loans (at the ‘non-bank lender’ level) and aggregating loans to a pool (at the ABS sponsor level). Although the pricing of individual loans is fairly standardized, it requires a credit underwriting model that is trained with data from recent credit transactions. Obtaining such credit data may be dicult for smaller underwriters, as rms do not generally share such data publicly.

• Data analysis: this typically entails (i) the pricing of pooled vehicles (ABS or CDOs) and (ii) assigning a credit rating to credit securities. In the realm of structured credit, the costs of such ratings are typically borne by sponsors.204 e ability of credit rating agencies to eectively perform this function has led to much criticism in

199See Wallman(1997) (‘By “compiling” then, I mean making data useful by taking data bits that are not useful in their raw form, tracking and aggregating the data by categories over time periods, and presenting the results of the compilation in accordance with a standard language—in this instance GAAP - to make them usable.’). 200See Wallman(1997) (‘As any one standard compilation system, such as GAAP, or international accounting standards, becomes just one of many that users may employ, the need for aestation will be drawn to the underlying data itself, and the means for preparing the elements of the database that make it useful to users when accessing the data.’). 201See Darcy(2009) (‘While these CRAs originally charged subscription fees, they switched to the issuer pays model in the mid-1970s.’). 202See Gerding(2009) (‘e SEC should require that registered rating agencies, NRSROs, fully disclose the ”source code” of every model (including algorithms and assumptions) used to rate securities.’). 203See Darcy(2009) (‘Many of the failures concerning risk management found in the rating agencies can also be found in the investment banks. For instance, both these entities used similar models to assess the risk of assets held.’). 204See Gerding(2009) (‘In many securitizations, rating agencies are paid by the S1V to issue credit ratings of the asset-backed securities.’).

193 the aermath of the nancial crisis.205 Depending on the pool structure, the complexity can vary considerably: in simple pass-through ABS, incoming cash ows are ‘passed through’ to creditors as they are received.206 In more complex structures, such as CDOs, cash ows are typically ‘tranched’, such that dierent classes of securities can be created and investors receive payouts according to a synthetic cashow ‘waterfall’.207 With respect to valuing more complex tranched structures, the Gaussian copula model, which was developed by Li(2000) as an application of the actuarial ‘heartbreak problem’ to CDOs, has received much scrutiny and has even been referred to as the ‘formula that killed Wall Street’.208 e application of these complex models in the data analysis step described here typically takes place behind the closed walls of issuers and credit rating agencies. As it turned out, Gaussian copula functions severely underestimated correlation risk, but there was no direct traceablility of model risk for outside investors. Gerding has thus argued that both issuers209 and credit rating agencies210 should make the nancial models open-source. Such a radical transparency would allow creditors at large to comment and rene the models, by allowing them to ‘fork’ existing models and retrieve errors and bugs independently.

4.5.1.5 Least cost avoider

A strict separation of the ground truth data layer from the data processing and analysis layer, allows us to identify a least cost avoider for each one and assign the costs between issuers and investors in a more nuanced manner. Under the least cost avoider regime proposed in chapter 1, it is held that generally speaking, the costs of producing ground truth data is most eciently assigned to the decit agents, while the costs of data processing and analysis would ideally be borne by creditors.

4.5.1.6 Ground truth data

If we start from the premise that decit agents are the most ecient cost avoiders with respect to the production of ground truth data, then we must look to the individual borrowers to evaluate how such a regime could be implemented most eciently:

205See Hu(2012) (‘ere were many reasons why ABS transactions did not perform as expected, including those associated with the conicts of interest and modeling failures associated with credit ratings agencies.’). 206See Qian, Jiang, Xu, and Wu(2012) (‘A pass-through mortgage-backed security is the simplest MBS, whose cash ows, both principal and interest, are passed through to the investor via an intermediary who retains a portion of the interest on cash ows as compensation for services rendered including guaranteeing these pass-through payments.’); Judge(2012) (‘A pass-through structure provides each investor holding an MBS issued in the transaction a pro rata share of the interest and principal payments made on each of the home loans underlying the transaction. In terms of securitization structure, these transactions are relatively simple. e cash ows coming in mirror the cash ows going out, and each investor holds an equivalent set of rights with respect to those cash ows.’). 207See Hu(2012) (‘Tranching-the prioritization of claims-was the animating force that allowed such a designation. e so-called waterfalls dened precisely the cash-ow rights of each of the tranches. e holders of the junior tranches would be the rst to suer if the cash ow from the pool assets proved insucient to meet all of the promised interest payments.’); Qian et al.(2012) (‘A collateralized mortgage obligation (CMO), diers from a pass through in that the underlying mortgage pool is separated into dierent maturity tranches, and each tranche’s holder receives interest payments as long as the tranche’s principal amount has not been completely paid o. e senior tranche receives all initial principal payments until it is completely paid o, aer which the next most senior tranche receives all the principle payments, and so on.’); Judge(2012) (‘Both credit risk and prepayment risk are allocated among the dierent tranches of MBSs issued through ”waterfall” provisions that set forth the rights of each of the dierent tranches. e general idea is to create a hierarchical structure in which losses on the underlying loans are allocated rst to the sub-ordinate tranches.’). 208See MacKenzie and Spears(2014) (‘providing a detailed account of the origins and application of Gaussian copula functions ‘e Formula that Killed Wall Street : that was how Wired’s editors introduced the Gaussian copula to the readers of a February 2009 article by journalist Felix Salmon. e model that had ’devastated the global economy. […] Although it was also used to measure banks’ overall credit risk, the most consequential modelling problem to which the Gaussian copula was applied was the evaluation of CDOs, a new class of securities becoming increasingly popular in the late 1990s and 2000s. […] In a typical CDO, if correlation among the bond or loans in the pool was low, only the holders of the lowest tranche would be at substantial risk of losing some or all of their investment. If, however, correlation was high, many of the bonds or loans might default, and losses could aect even the holders of the most senior tranche. So modelling correlation was the most crucial problem in CDO evaluation, and Gaussian copulas became – and still are – the canonical way of doing this.’); Judge(2012) (Li’s Gaussian copula enabled market participants to use a single number to capture the eect of these relationships among the underlying assets for each tranche of securities issued in a CDO, subject to certain assumptions and the availability of relevant data. is radically reduced the eort required to evaluate the expected default rate and return on the securities issued. Views dier on the extent to which market participants understood the limitations inherent in this device. ere is lile disagreement, however, on how widely used it became, and how critical its use was in enabling the growth of the MBS and CDO markets.’). 209See Gerding(2016b) (‘Regulators might consider requiring that issuers, particularly nancial institutions, disclose much greater detail on the methodologies and assumptions behind the models used to price asset- backed securities and derivatives and to measure and manage an issuer’s overall risk. A more radical approach would force disclosure of the algorithms in these models, in essence making this soware open-source.’). 210See Gerding(2009) (‘e SEC should require that registered rating agencies, NRSROs, fully disclose the ”source code” of every model (including algorithms and assumptions) used to rate securities.’).

194 • Corporate borrowers: with respect to corporate bond issuers, it is rather straightforward to see that corporate issuers are the least cost avoiders for disaggregated company-level nancial accounting data. As this data is directly processed by the issuer for ordinary operations and other regulatory lings (such as taxes), the issuer is clearly the least cost avoider when it comes to providing access to this data.

• Structured credit: for structured credit, the situation is far more complex. Since ABS and CDOs are essentially pools of individual small lot sized loans, there is no central entity that could provide a ground truth data feed.211 Gerding(2009) has pointed to the potential ‘information gaps’ that can arise along the securitization chain. 212 In the above section, we have outlined a wide variety of ground truth data sources. e question that arises when thinking about the least cost avoider is how to piece all these elements together.

– Individual borrowers: At the boom of the securitization chain are individual borrowers, which take out mortgage or car loans. Such borrowers oen shoulder part of the disclose costs associated with such loan applications. For example, mortgage applicants typically pay for appraiser reports. In an optimal regime, the role of individual loan applicants could be to provide and pay for such loan level disclosures, including credit scores and excerpts from the land and vehicle registers. In practice, this could mean that individuals pay a public or private register, which in turn provides (digital) data access to the initial lender and the structured credit underwriters, e.g. through API access points.

– Loan level underwriters: Once a loan is originated by a ‘non-bank lender’, new data related to the specic credit contract is created. As the original credit originators, these loan-level underwriters are the most ecient cost avoider for providing access to contract-level data.

– ABS/CDO underwriters: Structured credit sponsors, in particular investment banks, arrange individual loans in pooled credit securities. is pooling in itself creates new data that relates to the pooling vehicle. However, the underlying economic exposure is largely determined by the lower level data points of the individual borrowers, the collateral assets and the contract-level data. Regulation AB, by relying on the traditional ‘intermediary depiction model’, imposes all these information production costs on the sponsors of asset-backed securities (Hu, 2012). In contrast, in an optimal regime, the role of structured credit sponsors would solely be to provide access to the novel data created at the pooling layer. us, they would provide an aggregation layer that (i) sets out the structure of the security, (ii) identies the pool assets and (iii) links in the data feeds from both individual borrowers (through third-party providers) and loan-level underwriters (in particular non-bank lenders).

4.5.1.7 Data processing and analysis

In contrast, under an optimal regime, the costs of data processing and analysis would be assigned to investors. Judge (2012) questions the extent to which creditors are willing to invest in obtaining and processing information.213 Indeed, assigning these costs to creditors is a non-trivial task, as monitoring incentives of individual creditors may be limited:

• Specialized rms: as we have seen above, data processing and analysis is typically performed by the issuer or specialized rms, such as accounting rms and credit rating agencies, which are compensated by the issuer. With respect to credit rating agencies, the fact that issuers typically pay for the such data analysis has been subject to much critique, both with respect to traditional corporate bond oerings and more recently with respect to

211See Hu(2012) (‘With ABS, there was no business or management. Instead, information about the characteristics of the asset pool, the servicing of the assets, and the transaction structure was oen what was most important to investors.’). 212See Hu(2012) (‘Assuming that originator models were highly robust and based on correct information and extensive data, other nancial institutions down the securitization chain oen lacked access to these models or their data. Each separate stage in the securitization process creates information gaps; as mortgages are transferred from borrower to originator to SIV to investors, information on the risk of those mortgages is progres- sively ”destroyed. As in a child’s game of “telephone,” the end investors of a securitization receive poor information about the underlying assets.’). 213See Judge(2012) (‘Because of the costs to investors of obtaining and processing information, it may be rational for an investor to make an investment with far less than perfect information. e challenge for policymakers is that the level of resources that an investor seeking to maximize its own returns will invest in gathering information may well be less than is socially optimal. Accordingly, disclosure reforms can reduce but will not eliminate this mismatch.’).

195 structured credit transactions.214 Nevertheless, there does exist some tradition with respect to assigning these costs to creditors, as credit rating agencies were historically paid for by creditors under a subscription model.215 However, due to the free-rider problem experienced in the dissemination of ratings, credit rating agencies have since switched to an issuer-nanced model.216 us, assigning these costs to investors would practically require an incentive structure or pooling mechanism to balance out the free rider problem. In practice, this would still require further coordination at the level of the corporate issuer or the ABS security. One could imagine, for example, that auditors and rating agencies are paid for by creditors, but remunerated by a fractional charge derived from the bond’s coupon payments.

• Creditor-led analysis: in a fully transparent data regime, where both (i) disaggregated ground truth data is available in the sense of Wallman(1997), and (ii) nancial models are open-source in the sense of Gerding(2009), one could theoretically imagine that individual creditors could individually (or as a collective) take over the monitoring of credit assets. An illustrative example of such credit-led analysis is the case of Lending Club. Lending Club is a person-to-person credit market place, which maintains public data repositories for all originated individual loans. Although anonymous, the data contains multiple loan level data points for each loan application. In 2016, an individual investor retrieved all this data and ran a proprietary big data analysis on this dataset.217 rough this analysis he uncovered that the rm’s executives had engaged in a somewhat misleading self-dealing tactic in the early days of the rm. In particular they boosted growth gures and loan performance by taking out and quickly repaying multiple loans themselves.218

4.5.2 Investment and liquidity layer

4.5.2.1 Illiquidity externality

At the investment and liquidity layer, the relevant negative externality is illiquidity. As outlined in more detail in the rst chapter of this thesis, the illquidity externality results from the absence of potential buyers and sellers of the issuer’s securities, which are willing to provide bid and ask quotes in the market. us, liquidity provision costs constitute the relevant transaction costs of this externality. e key insight of the second part of the eorem is that in the absence of transaction costs related to the market externalities, the legal assignment of rights and obligations under security laws does not aect the eciency of the nal allocation. e second part of the eorem holds that with liquidity provision costs approaching zero, whether security laws require (i) the issuers or (ii) the investors to compensate such intermediaries does not aect the eciency of the nal allocation. In a positive transaction cost seing, however, the assignment of these costs by the law (whether explicitly or implicitly) maers. In this respect, an optimal regime is one that minimizes the costs of the market and assigns them to the least cost avoider, the party that is best positioned to reduce the costs of the market. us, in the context of the

214See Keering(2008) (‘at the rating agencies are compensated by the issuers of the rated securities, rather than by the investors who rely on the ratings, entails an inherent conict of interest: it creates an inducement for a rating agency to award the rating that the issuer wants, lest the issuer decline to seek (and pay for) a rating from that rating agency at all.’). 215See Gudzowski(2010) (’In the 1970s, the major CRAs switched from a subscriber-pays system to an issuer pays system. One reason for this switch was that the issuer pays model was the only means of nancing that would enable CRAs to issue ratings across a broad spectrum of securities. us, when mutual funds demanded broader ratings coverage in the 1970s, the CRAs accommodated them by switching to the issuer pays model. Another possible reason is that advances in photocopying in the 1970s made it easier to disseminate ratings. us, the greater ease and frequency of the rendered the collection of subscription fees increasingly dicult for the CRAs.’); Niamh Moloney(2015) (’e aws in the ‘subscriber-pays’ model are clear. In large part these are due to the public goods nature of many gatekeeper services, and the consequent free-rider issue, discussed above. Indeed, it has been argued that it was this free-rider problem that led the major CRAs in the 1970s to shi to their present issuer-pays model. In addition investors are likely to resist strongly the requirement that they pay for these services directly.’). 216See Darcy(2009) (‘e decision to do so stemmed largely from the nature of their business-CRAs produce information, and information is a public good. e subscription-based model created a “free rider” problem because the agencies could not feasibly stop paying subscribers from sharing the information with non- subscribers. rough the issuer pays model, the agencies can eectively charge all users of their product since the issuer can pass the cost of the rating on to investors in the form of a slightly reduced interest rate.’). 217See Chakin and Buhayar(2016). 218See Chakin and Buhayar(2016). (‘And yet evidence of lingering issues can be found in Lending Club’s database les, which are still available online. Sims has discovered dozens of other loans he suspects were made to company insiders, as well as lending practices that seem to have been designed to push growth above all else.’).

196 investment and liquidity layer, the objective is to reduce liquidity provision costs both in the primary and secondary markets and assign these costs to the party best positioned to reduce them. In the below chapter, a (hypothetical) optimal regime for both primary markets and secondary markets is sketched out that could minimize liquidity provision costs and allow for a more nuanced assignment of costs to the least cost avoider.

4.5.2.2 Optimal regime

Dening an optimal regime for primary credit markets at the investment and liquidity layer is a daunting task. Primary markets are dominated by the underwriter model, which is essentially an allocation through a specialized broker-dealer rm structure. Economically, we can think of this as a transitory banking rm allocation, where the bank assumes temporary credit risk on its balance sheet. Furthermore, the existing secondary market allocation of credit is rst and foremost an intra-institutional market with hierarchical liquidity levels. Identifying the optimal regime is thus not only complex, but also highly exploratory in nature as the existing micro-structure has existed and has ossied over multiple decades, if not centuries. However, given that the investment and liquidity layer is arguably the most expensive functional market layer and constitutes the very essence of the market, it is a crucial node within a market-based system and deserves close aention.

4.5.2.3 Optimal regime: Primary markets

Optimizing the primary credit market is all about reducing friction for the initial ow of funds from creditors to credit issuers. e primary markets must address a hybrid of informational frictions, which are in part already addressed at the disclosure and information layer, and liquidity concerns, which will be the focus here. As outlined under the rst part of the eorem, the primary market is dominated by the underwriter model. e underwriter model involves a specialized broker-dealer that is nancing the originated credit and pre- and re-selling it to credit markets. Essentially, the underwriter model can be thought of as a transitory banking rm allocation. is leads to the same structural challenges, in particular maturity transformation, encountered under the traditional banking rm allocation. During times of economic distress, this can lead to bank runs in the wholesale nancing markets, liquidity squeezes and systemic failures. us, in the optimal regime proposed below, the emphasis lies on reducing the extent to which underwriters take credit assets as inventory on their balance sheets. In particular, it is suggested that creditors access credit markets directly, both in the spot markets and on a forward basis.

• Direct market access: An optimal regime would involve credit issuers accessing credit markets directly in the spot markets, also known as direct public oerings (DPO).219 A further suggestion would be that, following the direct listing model in equity markets, such oerings are best executed through electronic secondary market infrastructure platforms.

• Pre-nancing vehicles: Pre-nancing refers to the matching of present credit supply with future credit demand. is goes even one step further than providing direct market access, allowing agents to allocate credit over time. Under the banking rm allocation, depositors pre-nance bank originated credit by depositing funds with the banking rm. e banking rm ‘raises funds’ from depositors in advance on a blind pool basis and, once a loan application is received, the bank can almost instantly deploy funds. In other words, there exists an implicit pre-raise. is ensures a commied pool of funds and speed-of-execution. Under the optimal regime, a directly accessible to- be-announced (TBA) forward order book for credit markets is proposed, which mimics the pre-nancing property of the banking rm in the credit market.

4.5.2.3.1 Bond markets

Primary bond markets have largely remained privy to analog inter-institutional dealings. To this day, investment banks coordinate with institutional buy-side fund managers through phone calls, instant messages and emails in the primary 219See Sjostrom(2001) (’Now a company can market its stock directly to the public by posting its oering document on the Web, making it accessible to hundreds of millions of potential investors.4 ese types of oerings have been termed Internet direct public oerings (DPOs).’).

197 bond markets. As a result of these institutional ineciencies, a consortium of leading bond underwriters, including Bank of America, Citi, Goldman Sachs, J.P. Morgan and Morgan Stanley, recently announced the creation of a new technology platform ‘Directbooks’, which aims to ‘ease the cumbersome process of selling corporate bonds, as the increasing electroni- cation of markets start to seep into debt issuance’.220 However, while such digitization initiatives may increase eciency at the existing institutional level – with eciency gains potentially being passed on, at least in part, to credit issuers via reduced underwriting fees and bond underpricing – they cannot mask the fact that the pre-eminent underwriter model is still a heavily intermediated process, economically relying on a transitory banking rm allocation.

• Direct market access: Direct market access refers to the process of credit issuers accessing credit markets directly, without engaging an underwriter. Sjostrom(2001) describes how, already in the late 90 ies, companies would conduct so-called internet ‘direct public oerings’ (DPOs) by selling shares and credit securities directly to the public without an underwriter.221 In bond markets, a successful example of the practice of such internet direct public oerings (DPOs) can be found in the treasury markets. TreasuryDirect is a website run by the Bureau of the Fiscal Service under the United States Department of the Treasury, which allows investors to buy Treasury securities directly from the U.S. government. Its website allows money to be deposited and withdrawn to personal bank accounts, as well as the rolling repurchase of government securities (from proceeds received as bonds mature and are repaid). For corporate bonds, Anand(2003) reports that seasoned corporate bond issuers, such as Ford Motor and Dow Chemical, have conducted bond oerings without the use of underwriting rms through Internet- based Dutch auctions.222 However, it seems that such bond oerings have not caught on, as most corporate bond oerings are still being underwrien by investment banks, albeit at decreasing underwriting fees.223

• Secondary market auction platforms: A key challenge of internet direct public oerings is the identication and shortlisting of creditors for a given bond issuance. In equity markets, so-called ’direct listings’ have recently become a successful means to ‘circumvent’ the traditional underwriting process.224 Key to the functioning of direct listings is the use of the New York Stock Exchange’s (NYSE) auction order book infrastructure, which allows companies to list existing secondary shares ‘directly’. rough automated auction infrastructure, secondary marketplaces can provide a ready pipeline of market creditors willing to back newly issued credit. In the realm of traditional bond markets, only ve percent of bonds are listed on regulated national exchanges.225 Instead, a large part of the secondary bond market trading activities are routed through electronic trading platforms. Most notably, the electronic trading platform MarketAxess has been reported to have an 85% share of the corporate bonds market.226 On the other hand, the electronic trading platform Tradeweb has come to dominate the government and treasuries market.227 us, with respect to running auctions for primary bond markets, these incumbent xed- income trading platforms seem well-positioned to line up public market creditors with new credit issuers. While the existing electronic platforms currently operate as institutional OTC markets only, the institutions executing trades through their systems already include the most signicant xed-income investors, ranging from the largest banks, ETFs, mutual funds to the major insurances. us, these secondary market participants represent the

220See Rennison(2019) (’Several years aer technology came to dominate the way stocks and currencies are traded, corporate debt is now catching up, with more than one-h of all trades now conducted on electronic trading platforms. But issuance — the point at which new bonds hit the market — has largely remained a ddly, paper-intensive exercise.’). 221See Sjostrom(2001) (‘Spring Street completed the oering in March 1996 raising roughly $1.6 million by selling approximately 900,000 shares to some 3,500 investors at $1.85 per share. Internet DPOs have received a lot of aention in the popular media following Spring Street’s oering. 3 Perhaps encouraged by this publicity and the growth of the Internet generally, DPO oerings jumped from 336 in 1995 to 498 in 1996, and in 1999, DPO oerings totaled 562.’). 222See Anand(2003) (‘Seasoned issuers are increasingly recognizing the power of the Internet to reach investors other than through underwriters. Ford Motor Credit and Dow Chemical have sold bonds directly to investors through Internet-based Dutch auctions.’). 223See Manju(2018) (‘In the U.S., underwriting fees, in recent years, have averaged about 0.7 percentage point on investment-grade corporate bonds, meaning that for a $1 billion bond issue, companies would pay about $7 million to banks arranging the sale.’). 224See Nickerson(2019) (‘In April 2018, music streaming giant Spotify disrupted the traditional initial public oering model and became a publicly traded company through a novel process known as a direct listing. Eschewing standard Wall Street practice, Spotify did not raise new money through the oering and instead simply made its existing shares available for purchase by the public.’). 225See Edwards et al.(2007) (reporting that fewer than 5 % of all bonds are listed on the NYSE). 226See Dugid(2019). 227See Staord(2019) (‘Tradeweb itself handles about $80bn a day in deals related to Treasuries, mainly between banks and large investors, using prices from roughly 30 dealers.’).

198 existing buy-side actors, which currently ll the order books in traditional bond oerings. Hence, these OTC platforms already serve the key credit market actors and should thus, under favorable regulatory conditions, be able to run direct credit auctions.

• Pre-nancing vehicles: Under the banking rm allocation, deposits pre-nance a blind pool of future bank loans. In the context of traditional bond markets, unlike for asset-backed securities, such means of pre-nancing do not yet exist. However, under an optimal market regime, one could imagine an ecient design of forward- seling bond markets, similar to the to-be-announced (TBA) MBS markets (discussed below). ese forward markets could specify generic pools of corporate or treasury bond issuances on a parametric basis. Investors would buy these pools on a forward, blind pool basis, much like bank depositors are typically nancing future loans. Parametric basis refers to a set of pre-specied nancial metrics for the bonds. ese could include nancial statement gures and ratios, such as the credit issuer’s revenue or net prots, or nancial ratios, such as net debt/EBITDA or interest coverage ratios. In an optimal open order book infrastructure, credit issuers could access these pre-nancing markets directly, originating loans directly against specic nancial metrics.

e proposals presented above bear the potential to reduce balance sheet exposure of bank underwriters in credit markets. While traditional corporate and treasury underwriting activities have been rather stable businesses in the recent past, they do induce the systemic risks associated with bank nancing. us, security laws which would promote direct market access, either in the spot or forward markets, could have tangible benets in terms of systemic resilience. In chapter 5 of this PhD thesis, a stress test model for a market-based credit regime is proposed. is model demonstrates how, in the absence of government backstops in credit markets, a market allocation can be more resilient than a banking rm allocation in times of exogenous credit shocks. To sum up the above, an optimal regulation regime could (i) enable direct market access in the spot markets and (ii) promote nancing over the time spectrum in the forward markets. With respect to direct access in the spot markets, this could be eected through direct listing regulations for the leading electronic bond trading platforms. Notably, these OTC electronic platforms currently operate as broker-dealers, not national nancial exchanges. us, such rules would have to be either implemented through revised broker-dealer surveillance rules or through a novel inclusion and re- regulation of the incumbent secondary market platform providers as national securities exchanges. With respect to the forward markets, the to-be-announced (TBA) bond markets would require specic exemptions under existing security laws. In particular, given the nature of pre-nancing, the identity of the oered bond issuers would not be specied at the time they are sold. However, under existing securities regulations, new credit issuances must specify the underlying credit assets in the oering prospectus. Instead, much like in the TBA agency MBS markets, only certain loan parameters would be pre-specied. is would require sui generis disclosure regulations under existing security laws.

4.5.2.3.2 Asset-backed securities (ABS)

Unlike bond markets, asset-backed securities markets have a more complex multi-layered underwriter structure. is makes the design of an optimal regime more complex as well. As the largest ABS market, the residential agency mortgage-backed securities markets (RMBS) encapsulates two or even three underwriting layers. At the rst layer, loan-level credit origination is dominated by non-bank lenders who underwrite loans and re-sell them to GSEs (Fan- nie Mae and Freddie Mac). At the second underwriting layer, GSEs then resell the bundled loans as ‘pass through’ mortgage-backed securities to institutional investors. At a third layer, ‘pass through’ securities can be bundled into tranched collateralized debt obligations. e eects of the discussed solutions below have the eect to reduce balance sheet exposure of underwriters in the asset-backed securities (ABS) markets. Notably, as outlined in the rst part, underwriting activities by investment banks in private ABS markets have been at the center of the nancial crisis in 2008. erefore, security laws which would promote such optimal direct market solutions, either in the spot or forward markets, could have tangible benets in terms of systemic resilience. In chapter 5 of this PhD thesis, a stress test model for a market-based credit regime is

199 presented. is model demonstrates how, in the absence of government backstops in credit markets, a market allocation can be more resilient than a banking rm allocation in times of exogenous credit shocks. It should also be noted that private ABS markets have collapsed to very low levels since the global nancial crisis and that most of the at scale mortgage-backed securities underwriting is now routed through government-sponsored entities (GSE), Fannie Mae and Freddie Mac. ese are still under government conservatorship to this day. However, under the Trump administration, there currently exist plans228 to re-privatize the GSEs, which will re-surface systemic risk concerns in private ABS markets.

• Direct market access: To oer direct market access within the largest ABS market, the RMBS market, we should look at the two dierent underwriting layers: (i) non-bank lenders and (ii) GSEs.

– Loan level: At the loan level, non-bank lenders nance mortgage loans of individual home mortgage borrowers with the intention of re-selling them to GSEs. ese non-bank lenders are oen nanced through wholesale funding. ey hedge themselves by pre-selling loans through the to-be-announced (TBA) MBS market on a forward-seling basis (see below under pre-nancing). It has been noted that due to the signicant underwriting activities of non-bank lenders, such as icken Loans or Freedom Mortgage, these mortgage originators could end up posing a systemic risk.229 In an optimal credit regime, mortgage applicants would transact directly with the credit market. However, there currently do not exist any at scale examples of individual credit issuers accessing credit markets directly. Only in the peer-to-peer loan niche, there currently exists a retail market microstructures where retail credit issuers directly access ‘credit markets’. In the leading peer-to-peer marketplace, LendingClub, borrowers issue requests for notes directly to the ‘credit markets’ via an online platform. ese notes are only issued once they are fully backed by creditors, without any traditional underwriting. However, due to mandatory disclosure obligations under existing securities regulations, the issued loan notes are co-issued with the lending platform. – Pooled credit vehicles: With respect to the second underwriting level, the situation is similar to bond markets. However, a further design challenge that arises comes from the pooled nature of asset-backed securities (ABS). Given that the securities involve large pools of individual mortgages, which are issued over time, spot market order books seem more dicult. One possibility would be to batch originated mortgages into daily batches, which are sold through specic securities in the spot market. Similar to the procedure outline above for bonds, the daily auctions could be run by electronic exchanges. For agency mortgage- backed securities, Tradeweb, seems to be the leading platform, which would be well-place to implement such auctions.

• Pre-nancing vehicles: A large part of the mortgaged-backed securities market is pre-nanced through the so-called to-be-announced (TBA) forward markets.230 e issuances of newly-issued agency MBS are excluded from securities regulation and would not be possible to legally exist without such an exemption. is is because, at the time of a TBA trade, the securities that will eventually be delivered do not yet exist. Notably, the agency MBS to-be-announced markets, are some of the most liquid xed-income markets. In fact, only treasury bond spot markets have more market liquidity. is is because the mortgage credit market ‘thickens’ at the TBA level. As such, the TBA markets are a great example of a market microstructure, which eectively mirrors pre-nancing properties of the banking rm allocation. However, while non-bank lenders are the most active sellers to the TBA markets, the TBA markets are currently not accessible to individual mortgage borrowers. Opening access to TBA forward seling markets to individual borrowers would thus be a special case of the direct market access proposal above. 228See Kiran(2019) (‘e Trump administration has taken the rst step towards what it hopes will be the eventual privatisation of the two biggest mortgage guarantors in the US, allowing Fannie Mae and Freddie Mac to keep millions of dollars’ more of their own prots.’). 229See Ackermann(2019). 230See Wright and Vickery(2013) (‘More than 90 percent of agency MBS trading volume occurs in this forward market, which is known as the TBA (to-be-announced) market.’); P. Gao et al.(2017) (‘Agency mortgage-backed securities (MBS) trade simultaneously in a market for specied pools (SPs) and in the to-be-announced (TBA) forward market. TBA trading creates liquidity by allowing thousands of dierent MBS to be traded in a handful of TBA contracts.’).

200 Under the optimal regime discussed above, asset-backed securities (ABS) markets would be enabled without traditional bank underwriting. us, the potential eects of such a regime would go far beyond traditional security laws. Notably, as outlined in the rst part, underwriting activities by investment banks in private ABS markets have been at the center of the nancial crisis in 2008 and the existing GSE structure is not a pure market allocation. erefore, security laws which would promote such optimal direct market solutions, either in the spot or forward markets, could have tangible benets in terms of systemic resilience. In terms of existing security laws, it should rst be noted that the agency RMBS market discussed above is currently fully exempt from existing securities regulations. Both non-bank lenders as well as GSEs do not fall under the SEC’s mandate. However, if these assets are to be moved into a private market seing again, it is important to contemplate how they could be ed under existing security laws. To sum up the above solutions, the optimal regulation regime would rstly enable direct market access avenues in the spot markets. From a securities law perspective, a particular challenge would be to enable individual borrowers to issue credit directly to the market. In particular, the mandatory disclosure obligations would need to be tailored such that individual borrowers can issue credit securities independently. Furthermore, for direct access on the pool level basis, these could be eected through direct listing regulations for the leading electronic bond trading platforms, such as Tradeweb. As mentioned in the bond section, these OTC electronic platforms currently operate as broker-dealers, not national nancial exchanges. us, such rules would have to be implemented through broker-dealer surveillance rules. Secondly, the to-be-announced (TBA) bond markets would require specic exemptions under existing security laws. In particular, given the nature of pre-nancing, this is because the identity of the bonds is not specied at the time they are sold. Instead, much like in the TBA agency MBS markets, only certain loan parameters are pre-specied, which would require a sui generis regulation under existing security laws.

4.5.2.4 Optimal regime: Secondary markets

For decades, secondary credit markets have been structured as over-the-counter (OTC) ‘principal’ markets were SEC- registered dealers hold bonds on inventory to make a market. ey were compensated for market-making activity through bid-oer spreads. ese markets were historically operated largely over the phone between dealers. However, over the last decades, electronic OTC platforms have emerged in secondary credit markets as powerful intermediaries. In particular, MarketAxess has been reported to have a 85% share of the corporate bonds market.231 On the other hand, the electronic trading platform Tradeweb has come to dominate the government and treasury bond market.232 Currently, trading over these OTC markets is primarily conducted via the request-for-quote (RFQ) method, whereby a trader from the buy-side will place a buy/sell order to a dealer and ask the dealer for a price. Essentially, these platforms have digitized the previously analog inter-dealer market. However, this market microstructure means that dealers are holding substantial credit exposure on their balance sheets. us, like underwriting in the primary markets, this trading inventory places these assets in a transitory banking rm structure, which may induce systemic risk. erefore, the proposed optimal regime hereunder entails a purer market allocation, where economic beneciaries trade directly with each other. Closed limit order book In comparison to the RFQ protocol, under a central limit order book (CLOB), which is one of the primary protocols used in the equity markets, buyer and sellers can be matched up directly through the order book. e central limit order book market microstructures can be designed in a way that liquidity providers are given rebates if they provide liquidity by placing limit orders, which are paid for by liquidity takers, which execute market orders. Closed limit order book protocols are still a rarity in credit markets. e New York Stock Exchange (NYSE) operates NYSE Bonds, which trades in a similar manner to the NYSE stock exchange through a CLOB protocol. However, the NYSE Bonds trading

231See Dugid(2019) (‘Over a quarter of all corporate U.S. bonds are now traded electronically, with MarketAxess holding 85% of market share over peers like Tradeweb Markets and Bloomberg, according to Greenwich Associates.’). 232See Staord(2019) (‘Tradeweb itself handles about $80bn a day in deals related to Treasuries, mainly between banks and large investors, using prices from roughly 30 dealers.’).

201 segment has only limited volume of largely small-sized trades. Electronic CLOB credit markets would constitute a pure form secondary market, which would allow trade fragmentation, also at the market maker level (see below). Algorithmic market-making Like stock markets, operating through electronic limit order books would enable many participants to place limit orders and thereby provide liquidity to credit markets. For equity markets, there is a growing literature233 that shows that a large part of today’s market making activity is already provided algorithmically through high-frequency trading rms (HFT) like Citadel and Virtu. e execution of trades is, however, algorithmically driven and does not require any active human transaction. us, if the market maker’s function is reduced to a piece of code, the question arises why other creditors, even ordinary retail investors, could not run the same piece of code. In other words, opening up credit markets in such a way, would enable a wide range of institutional and retail investors to become active algorithmic market makers. Instead of placing their assets in static credit investments, they could take up the role of algorithmic market makers. Optimal regulation Optimal securities regulation in secondary credit markets should start with the realization that dealer markets impose similar systemic risks as the underwriter model. us, promoting a pure-form market regime, whereby liquidity is provided by a wide range of market participants, should be promoted. With respect to electronic closed-limit order book (CLOB) credit marketplaces, these could either be regulated as broker-dealers. is is how the dominant electronic OTC markets, such as MarketAxess and Tradeweb, are currently regulated. However, this could limit the wider accessibility of these platforms. Alternatively, they could be regulated as national securities exchanges. Optimally, they would receive a specic carve-out of the national securities exchanges regulation that tailors them to the requirements of credit markets. Given that these electronic exchanges are essentially soware code, which could be run by multiple nodes in the nancial system, securities regulation could also whitelist certain code packages. Security laws could then specify certain capital and operational system requirements that have to be met to run platforms. is would enable a wider range of market participants to operate such electronic marketplaces. Furthermore, with respect to market making activities, under the current securities regulation regime, such algorithmic market making would not be possible, as they exclude unregistered agents from carrying out these functions. Currently, individual investors are fully excluded from accessing electronic OTC marketplaces. is could be somewhat changed under a regime with electronic closed limit order book exchanges, where individual investors could place limit orders. An optimal securities regulation would go one step further, by enabling a wider set of agents to engage in electronic market making. Like for electronic closed limit order book exchanges, this could mean that the SEC species certain market making codebases. Investors could then either run the whitelisted codebase directly, or they could purchase securities where the issuer executes such market making codebases on the backend.

4.5.2.5 Least cost avoider

4.5.2.5.1 Primary markets

Under the underwriter model, the credit issuer typically pays an underwriting fee. rough a rm commitment underwriting, the credit issuer receives price certainty for a set underwriting fee and an underpricing discount. While credit underwriting fees are smaller than equity markets, they can still be substantial. In the optimal models discussed above, in particular the direct market access and pre-nancing models, there would be no such underwriting fees, instead there would be access fees for the auction or order book infrastructures. It is also not clear whether the credit issuer is indeed the least cost avoider. As mentioned above, the credit issuer essentially pays for price certainty. Under the direct listing model in equity markets, the issuer assumes the price uncertainty. In the proposed direct market access model and the pre-nancing model, the price uncertainty could be passed to credit market investors. In particular, under the direct access model, issuers could dene a minimum price acceptable to

233See Aldridge and Krawciw(2017).

202 them through an auction mechanism to that eect. On the other hand, pre-nancing order books naturally ‘pre-price’ the issuance.

4.5.2.5.2 Secondary markets

Traditionally, the costs of regulated public secondary markets, comprising exchange listing and market maker fees, are borne by credit issuers. In equity markets, such secondary market costs can be prohibitively large for issuers. Given that the main beneciaries of secondary market liquidity are investors and not credit issuers, it appears that these costs would optimally be assigned to them instead. In modern credit markets, both traditional bond and asset-backed credit markets, most securities are not listed nor do they have any assigned market maker. us, there exist no public market fees for credit issuers. Instead, credit securities are traded mostly OTC on an inter-dealer basis or on electronic trading platforms, such as Tradeweb and MarketAxess. On these electronic platforms, it is investors who implicitly pay for liquidity through the bid-ask spreads under the dierent protocols, either through request for quote (RFQ) or central limit order book (CLOB) protocols. As such, these market costs already seem to be assigned to the most ecient cost avoiders. However, in the current market microstructure, much of the liquidity is still provided by a few larger OTC dealers. Under an optimal secondary market regime, with a more fragmented number of liquidity platforms and secondary market liquidity providers, the costs could instead be assigned among a wider number of agents. Furthermore, in more pronounced central limit order book markets, liquidity providers (limit orders) are compensated by liquidity takers (market orders), thus allocating the costs of the market eciently among the investors.

4.5.3 Diversication layer

4.5.3.1 Misallocation externality

At the diversication layer, the relevant negative externality is misallocation. As outline in more detail in chapter 1, the misallocation externality can be understood as excessive idiosyncratic risk exposure in the absence of suitable pooling partners. us, pooling costs constitute the relevant transaction cost related to this externality. e misallocation externality can be further divided into two types:

• Market-induced misallocation: this misallocation results from the structure and costs of the market, in particular from frictions at the disclosure and investment layer, which limit the exposure to non-public assets. In the sphere of credit, this misallocation may result from an underallocation to privately-held credit, such as mortgage, auto or consumer loans.

• Firm-induced misallocation: this misallocation results from the dominance of the rm, which nances assets through the rm-structure that could otherwise be nanced through the market. In the sphere of credit, this relates to bank-nanced loans. As a result, the investor’s portfolio is underweight with respect to bank-nanced credit.

e key insight of the second part of the eorem is that in the absence of transaction costs related to the market externalities, the legal assignment of rights and obligations under security laws does not aect the eciency of the nal allocation. e second part of this eorem thus holds that with transaction costs (pooling costs) approaching zero, whether security laws require (i) the credit issuer (ii) or the creditor to bear the costs of pooling does not aect the eciency of the nal allocation. In a positive transaction cost seing, however, the assignment of these costs by the law, whether through explicit or implicit assignment, does aect the nal outcome. In this respect, an optimal regime is one that minimizes the costs of the market and assigns them to the least cost avoider, the party that is best positioned to reduce the costs of the market. In the context of the diversication layer, the objective is to reduce pooling costs and assign these costs to the party best positioned to reduce them. In the below chapter, an optimal regime with respect to both market-induced misallocation and rm-induced misallocation costs is sketched out for the sphere of credit.

203 4.5.3.2 Optimal regime

So far, we have analyzed a number of key challenges related to the disclosure and investment layer when it comes to allocating credit through the market. e diversication layer is quite interesting in this regard, as public investment vehicles can help to overcome many of the woes encountered at the ‘lower’ layers. By being able to give retail investors access to a wide range of credit assets on a diversied basis, they can in a sense leapfrog over many of the hurdles encountered at the disclosure and investment layer. For example, in public credit markets, most credit assets trade more actively at the pool level, rather than at the single-issuer asset and security level. For example, while corporate bonds may be rather illiquid on a single issuer basis and only very few asset-backed securities (ABS) are oered to non- institutional investors in the rst place, xed-income ETFs can provide highly liquid exposure to both types of credit assets on a pooled basis. Given that trading ‘thickens’ at the pool level, such ETFs can oer high levels of liquidity, oen times exceeding the liquidity of the underlying basket securities by a wide margin.234

4.5.3.2.1 Economic diversication

Economic diversication relates to the degree to which surplus agents are – in the absence of legal guarantees (legal diversication) – economically exposed to the idiosyncratic risks of individual decit agents (credit issuers). Market- induced and rm-induced misallocation describes two distinct types of economic diversication, which diverge from an optimum portfolio allocation. Market-induced misallocation Under this type of misallocation, the investor’s portfolio is underweight with respect to privately held credit assets. In other words, there exists a delta between the invested credit assets, n, and the full universe of credit assets, N, which would be comprised in a mean-variance ecient market portfolio in the sense of Markowitz.235 is delta can result from frictions at the disclosure and investment layer, through which a portion of credit assets are placed outside the reach of public market investors. In the area of credit, this traditionally relates to small-lot sized credit that is traditionally allocated through the banking rm, such as mortgages, SMB credit, auto loans or consumer credit. An optimal diversication regime would be able to ‘cure’ this under-allocation through pooling vehicles that give investors access to such forms of credit. us, an optimal regime would provide for eective pooling vehicles that could provide credit exposure to assets traditionally nanced through the bank balance sheet. As will be further explained below, such hypothetical bank-like diversication vehicles would ideally be closed-end, maturity-matched vehicles that invest on an evergreen basis. While open-end investment funds, such as mutual funds and ETFs, are well-positioned to invest in (relatively) liquid xed-income securities, such as investment grade bonds and treasuries, they are ill-equipped to invest in illiquid securities. Given the extensive redeemability and liquidity requirements, the extent to which they can take such positions in private credit is limited. Where they do invest in traditional bank-nanced credit through asset- or mortgage- backed securities (ABS and MBS), such investments are (i) subject to stringent liquidity requirements and (ii) structured through multiple intermediation layers. For example, the BlackRock managed iShares CMBS ETF invests in commercial mortgage-backed loan portfolios through (relatively) liquid commercial mortgage-backed securities only236 and is expressly restricted from investing in less liquid, privately held securities.237 Even if the underlying portfolio assets may be subject to some residual liquidity risk,238 open-end investment funds typically avoid taking substantial upfront ex-

234See Ben-David et al.(2018) (empirically analyzing and comparing the liquidity of ETFs and basket securities and nding that ‘Along all three dimensions, the average ETF is signicantly more liquid than its basket stocks. In particular, the bid-ask spread is lower by about 20 bps.). 235See Markowitz(1952) and Markowitz(1959). 236See iShares(2014) (‘e Underlying Index includes investment-grade CMBS that are ERISA eligible with $300 million or more of aggregate outstanding transaction size. In addition, the original aggregate transaction must be $500 million or more and the tranche size must be $25 million or more.’). 237See iShares(2014) (‘Excluded from the Underlying Index are non-ERISA eligible securities, agency transactions and privately-issued securities, including those which may be resold in accordance with Rule 144A under the Securities Act of 1933, as amended (the “1933 Act”).’). 238See iShares(2014) (describing the liquidity of the CMBS potfolio ‘CMBS issued by non-government entities may oer higher yields than those issued by government entities, but also may be subject to greater volatility than government issues. In recent years, the market for CMBS experienced substantially lower valuations and greatly reduced liquidity. Ongoing economic and market uncertainty suggests that CMBS may continue to be more dicult to value and to dispose of than in the past.’).

204 posure to illiquid securities. If they were to invest in illiquid private credit at scale, this would expose them to bank-like investor ‘runs’, as they may not be able to liquidate their positions in time to meet investor redemption obligations. In other words, short-term investor redeemability requirements result in a bank-like maturity mismatch. is makes it dicult for open-end investment funds to eectively address market-induced misallocation. In contrast, closed-end investment funds are not subject to the same set of rigid redeemability and liquidity requirements and can therefore aord to make substantial investments in private credit assets. For example, the Franklin BSP Private Credit Fund is a closed-end fund that invests primarily in private debt of middle market corporate issuers that have both (i) trouble accessing capital markets directly and are (ii) under-served by banks.239 Closed-end funds fundamentally dier from open-ended funds in that they raise a pre-determined amount of capital upfront. us, the fund’s capital is not increased or decreased as investors enter or exit the investment pool. Instead, the fund’s structure is ‘closed’ and investors trade shares in a xed asset pool. With respect to addressing market-induced misallocation, closed-end fund structures could provide for a very eective solution because of the following factors:

• Maturity-matched: given that closed-end funds invest a xed asset pool, which is not increased or decreased dynamically, they are maturity-matched investment vehicles. Investors seeking liquidity trade with other pool investors at the pool level, rather than with the fund (as is the case for open-end funds). us, investor ‘redemp- tions’ do not typically result in a sale of the underlying pool assets, which allows them to make long-term, illiquid credit investments.

• Diversication stack: closed-end funds can make direct investments in private credit and do not have to get exposure through other ‘lower level’ pooling securities, such as asset-backed securities (ABS). is means they can purchase small lot sized loan portfolios directly from non-bank lenders that would typically sell to structured credit sponsors. e opportunity to make direct investments reduces the ‘diversication stack’ and could reduce the costs associated with an additional intermediation layer.

• Evergreen structures: closed-end funds could ‘recycle’ repaid debt back into the investment pool, thus ensuring a continuous ‘evergreen’ fund structure. is would allow them to reduce both the (i) pre-payment risk typically encountered in bank-based debt and (ii) avoid the costs of having to periodically raise new fund structures. In contrast, structured credit securities (ABS, MBS, CDOs) have to be ‘re-raised’ or ‘re-issued’ aer each credit cohort has been repaid. e evergreen nature would allow closed-end funds to operate more like traditional banking rms.

• Optimal size: the optimal size for such diversication vehicles would need to be large enough for there to be (i) adequate secondary market liquidity between fund shareholders and (ii) eective economic diversication across a broad universe of private credit assets.

• Risk dierentiation: closed-end funds that act as quasi-bank balance sheets, could establish a distinct ‘feature advantage’ over a traditional bank-based allocation, in that they could oer dierent risk-return menus. In contrast, bank deposits receive an interest rate which is eectively nanced by all credit assets on the bank’s balance sheet. As such, the interest rate paid on deposits is a fully blended rate, not taking into account the varying risks and returns of dierent credit segments on the bank’s balance sheet. Due to the bank’s legal diversication, holders of demandable bank debt have no incentive to actively monitor the bank’s credit assets and require a higher rate of return, even if the bank invests excessively in high-risk assets (‘asset substitution’).240 As a result, deposits

239See Benet Street Partners(2019) (‘e Fund aims to target investments presented by the large, persistent and aractive market opportunity created by a structural supply/demand imbalance for private credit. is imbalance, in part, has been driven by substantial long-term changes in the debt capital markets following the credit crisis in 2008. Middle market companies oen have specialized requirements that limit their ability to employ conventional loan structures and their access to broader capital markets. eir ability to raise capital has been further restricted as banks retreated from middle market lending due to changes in the regulatory landscape over the last decade.’). 240See Ronn and Verma(1986) (‘In the absence of deposit insurance, riskier banks will be able to aract deposits only at higher rates, and these higher costs of funding serve as built-in market-regulated incentives to limit excessive risk-taking by banks. As introduction of deposit insurance makes deposits equally risk-free across banks, these incentives disappear, and regulation and close supervision of the banking industry must necessarily replace them as deterrents to excessive risk-taking.’); F. Black, Miller, and Posner(1978) (arguing in this vein with respect to increasing the deposit insurance ‘It is sometimes argued that 100% deposit insurance would cause bank management to be less diligent, because the watchful eye of the large depositor would no longer be trained on the bank.’).

205 may not be optimally matched to the risk-return preferences of depositors. Closed-end funds could address this shortcoming by establishing pooling vehicles for dierent risk categories. e risk pools could be dened through credit origination criteria, such as credit ratings, credit scores or loan-to-value (LTV) ratios.

While such closed-end credit vehicles would surely come with higher expenses, as private credit pools would have to be either acquired or originated by the pool itself, they could be a very eective way to provide creditors with exposure to private credit assets. In addition, given the maturity-matched funding structure, there may exist some macro-level advantages in terms of system risk. In chapter 5 of this PhD thesis, credit shocks are modelled for an economy where credit is entirely provided by such maturity-matched pooling vehicles. It is found that, given a set of stylized assumptions, such closed-end pools may provide for a more resilient nancial architecture. Optimal regulation Although existing securities regulation already provides for closed-end fund structures, the provisions relating to such fund vehicles have not been created with bank-based debt in mind. us, SEC-regulated closed-end funds are not well-tailored to replace the balance sheets of banks at scale. In practice, such closed-end funds oen trade at considerable discounts or premiums to their net asset value (NAV) due to informational frictions at the disclosure layer. us, an optimal regulation would tailor closed-end funds to the requirements of the underlying credit asset class. For example, much like Regulation AB II has set out a proprietary disclosure regime for private-label RMBS markets, closed-end private credit funds could be subjected to specic asset-level and loan-level mandatory disclosure obligations. Firm-induced misallocation Firm-induced misallocation arises from the dominance of the banking rm within a certain credit market segment. is rm dominance leads to the investor’s portfolio being underweight with respect to bank-nanced credit. While we have naively assumed above when discussing market-induced misallocation, that in the absence of frictions at the disclosure and investment layer, credit issuers would willingly access credit markets, this assumption is questioned for rm-induced misallocation. In particular, rm-induced misallocation assumes that there may exist a lack of investment access, due to the banking rm’s unique positioning. ere may be a number of reasons why banks are uniquely positioned, including long-term relationships with borrowers,241 idiosyncratic loan properties that are not well understood by market-based creditors or geographic reach. For example, Hirtle(2007) nds that despite technological innovation in the delivery of retail banking services,242 the size and reach of a bank’s branch network still remains a main competitive factor that determines a bank’s ability to both accept deposits and originate loans.243 It is thus easy to see how rm-induced misallocation could, for example, arise in rural areas where the only way in which borrowers can access credit is through their local bank branches. As a result, all loans originated in these remote areas will be missing from an ecient credit market portfolio. erefore, the investor’s portfolio may be under-allocated with respect to a material portion of the credit market. Even were security laws would not restrict creditors from investing in such illiquid loans, banking rms may still be beer positioned to serve a portion of the market. Since the presence of specialized banking rms restricts creditors’ ability to optimally contract with a portion of the market, an optimal market regime would be able to encourage (or oblige) borrowers or banking rms to provide market-based nancing providers with investment access. Optimal regulation From a legal perspective, whether a borrower chooses to contract through a banking rm or through the credit market is principally subject to contractual freedom. However, if it appears that the dominance of banking rms systematically restricts market-based creditors from constructing well-diversied credit portfolios, positive measures could be

241See Boot(2000) (‘e modern literature on nancial intermediation has primarily focused on the role of banks as relationship lenders. In this capacity, banks develop close relationships with borrowers over time. Such proximity between the bank and the borrower has been shown to facilitate monitoring and screening and can overcome problems of asymmetric information. In this view, relationships emerge as a prime source of an incumbent bank’s comparative advantage over de novo lenders.’). 242See Hirtle(2007) (‘e advent of Internet banking, the proliferation of automatic teller machines (ATMs), and the increasing reliance on centralized call centers, combined with post-merger pushes for eciency, all seemed to challenge the traditional branch method of delivering banking services. Yet the number of full-service branches in the United States has increased steadily since the early 1990s. Further, consistent with the general trend toward consolidation in the banking industry, these branches have become increasingly concentrated within the large branch networks of a limited number of institutions.’). 243See Hirtle(2007) (‘Our ndings suggest that banks with mid-sized branch networks may be at a competitive disadvantage in branching activities.’).

206 warranted to deal with such market failures. In particular, to level the playing eld between banking rms and market- based credit providers, one option would be to mandate banking rms to oer new loan originations to a competitive credit marketplace. In such a credit market, both banks and market-based credit providers could provide competitive bids. us, banks would have to bid on their own loans before taking them on their own balance sheets. Where the market outprices them, they would only receive an origination fee. While such pro-active measures would surely receive a lot of pushback from the banking industry, they could limit the extent to which market-based credit suers from rm-induced misallocation.

4.5.3.2.2 Legal diversication

With respect to means of legal diversication, credit markets currently seem to be materially disadvantaged compared to the allocation through the banking rm. While depositors of banking rms can enjoy the explicit legal diversication that comes with deposit insurance schemes, creditors in xed-income markets are to a large part fully exposed to the virtues of the markets. With the exception of a small segment of credit securities, namely treasuries and agency MBS credit securities, which enjoy explicit or implicit governmental guarantees, legal diversication is fully absent from credit markets. is seems somewhat disturbing, given that similar economic transactions receive materially dierent treatment, depending on the allocation structure. A bank loan taken out by a rm enjoys dierent protection compared to a bond issuance of the same credit issuer. While the bank loan may be fully nanced by bank depositors and thus enjoy material legal diversication, the bond issuance fully exposes market-based creditors to the idiosyncratic risks of said borrower. In other words, the substantial means of legal diversication under the bank-based allocation can be viewed as an implicit subsidy for banking rms.244 As a result, it has been widely discussed in the nance literature, whether such deposit insurance schemes should be subjected more to the rigidity of the market, for example through a risk-sensitive deposit insurance premium.245 Here, the opposite question is posed, namely how could market-based credit be more removed from the rigidity of the price mechanism and set on par with bank credit. Optimal regulation An optimal regulation strategy for legal diversication would aim to level the playing eld between a bank and a market allocation. One regulatory strategy could entail oering very broad government guarantees to credit oered through the market. As for agency RMBS credit,246 a requirement catalogue could apply, which sets out the criteria for eligible credit issuers to fall under such guarantee schemes. Over time, as the market-based credit allocation becomes more competitive to the banking rm allocation, these guarantees could be scaled back.

4.5.3.3 Least cost avoider

244See F. Black et al.(1978) (‘As to the feasibility of nongovernment deposit insurance, clearly no private insurance could be expected to pay o in the event of a wave of failures of the kind that occurred in the 2 or 3 years aer the onset of the Great Depression in 1929.’). 245See Chan and Mak(1985) (‘Many proponents of bank deregulation suggested that the soundness of the deposit insurance system would be beer served by a risk-sensitive premium. It is generally perceived that the constant (risk-insensitive) premium policy is inecient because of the incentive for banks to hold ”riskier” assets.’); McCulloch(1985) (‘e FDIC despairs altogether at the prospect of premia which accurately reect exposure to risks, even interest rate risk which they grant should be relatively easy to measure. Such a system would, it is claimed, entail ‘more advanced risk quantication techniques than are currently imaginable’.’); Ronn and Verma(1986) (‘us, when insurance is oered at a at premium, regulation is designed to ensure that the risk posed to the insurer-both asset and nancial risk-is appropriately uniform so that it corresponds with uniformity on the premium side. Risk-adjusted deposit insurance, on the other hand, can be readily seen to reintroduce incentives to limit excessive risk-taking, thus combining the benets of deposit insurance (avoiding bank runs) with those of deregulation (higher competition).’). 246See Fannie Mae(2019) (seing out the Fannie Mae eligibility requirements for single family home mortgages ‘e Eligibility Matrix provides the comprehensive LTV, CLTV, and HCLTV ratio requirements for conventional rst mortgages eligible for delivery to Fannie Mae.’).

207 4.5.3.3.1 Economic diversication

As for the other functional layers, the question arises which party is best-positioned to reduce pooling costs at the diversication layer. Under the current regime, the costs are fully borne by the creditors, who pays for the allocation through fund vehicles, such as pension funds or xed-income mutual funds and ETFs. Market-induced misallocation As held in chapter 1, surplus agents are the ultimate economic beneciaries of optimal diversication, as they receive the direct economic rents from owning a mean-variance ecient portfolio. As such, they are directly incentivized to drive allocation to the optimum. It thus seems logical that they should be the least cost avoider when it comes to the market-induced misallocation. In the sphere of credit, where their portfolio is underweight with respect to private debt, they can always ‘vote with their feet’ by reallocating funds to pooling vehicles that provide them with more optimal exposure or demand the creation of such pooling vehicles from their investment managers. Against this background, it appears to be most ecient solution to assign the cost of such diversication to creditors. However, as suggested in chapter 1, a full assessment requires us to consider the alternative of assigning these costs to the credit issuers, in particular through either (i) issuer-sponsored diversication vehicles or (ii) internal diversication. In the area of credit, however, neither of the two options seem very promising. Firstly, with respect to issuer-sponsored diversication vehicles, the ‘index addition eect’ that may incentivize issuers to carry the costs of pooling vehicles is likely to be subdued for credit issuers compared to equity issuers, in particular in low-interest environments. Similarly, internal diversication by the credit issuer does not appear to be an alternative to creditor diversication, given that it eectively would turn credit issuers into banks. Firm-induced misallocation Firm-induced misallocation results from the dominance of rm-based nancing for a specic segment of the market. In this respect, as suggested by the proposed regime above, either (i) decit agents (issuers) or (ii) banks appear to be best positioned to reduce such misallocation. Given that their contractual relation is at the center of this misallocation, it appears that these two parties are also the lowest cost avoiders. In particular, they can be either incentivized or obliged to provide more investment access to market-based nancing providers. Again, chapter 1 asks us to also consider the alternative cost assignment. In particular, we should ask whether and how creditors could minimize this type of misallocation and at what costs. In the context of credit, the costs of the creditor to compete with the bank allocation can vary substantially depending on the credit segment. If we think again about the example above, where the unique position of the bank stems from its bank branch network, then the creditor’s costs of building a competing branch network would surely be prohibitively large. However, depending on the credit segment, this may vary considerably. rough innovative ways of credit distribution, creditors may in the future become the least cost avoiders for some segments of the credit markets where banks have previously prevailed.

4.5.3.3.2 Legal diversication

Under means of legal diversication, the costs are typically assigned to society at large. No specic rm or individual is held nancially liable, if the government provides guarantees or intervenes in the crisis. In chapter 5, the presence of government guarantees is modeled as a future tax on all surplus and decit agents of the economy. Under a more nuanced least cost avoider regime, legal diversication mechanisms could assign such costs to surplus agents only.

4.6 Conclusion

A longstanding policy debate revolves around the question of whether bank-based or market-based nancial systems should be promoted. is chapter oers a novel perspective on this perennial debate among scholars and policymakers by applying the Coase eorem of Securities Regulation on the credit asset class. rough the rst part of the eorem, the chapter sheds light on the costs of competing regulatory regimes that can govern credit transactions, depending on whether credit is allocated through the rm or the market. In particular,

208 the chapter looks at the costs of arranging credit through either banking institutions or nancial markets. In the allocation through the banking rm, transactions are subject to industry-specic banking regulation. is is compared and contrasted to the costs of transacting through nancial markets, where transactions are governed by securities regulation. At the disclosure layer, the qualication of individual loans as securities triggers substantial mandatory disclosure obligations under security laws. ese disclosure obligations make the market costlier than the alternative origination of loans through the regulated banking system, where disclosures are made privately and bilaterally between borrowers, depositors and the bank. At the investment and liquidity layer, where credit is allocated through the market, it typically passes through transitory bank-like allocation structures, such as underwriters and secondary market makers. In contrast, the bank internalizes these functions through the operation of its balance sheet. Bank regulation traditionally imposes high costs at this functional layer. As shown in this chapter, securities regulation has been too lax at this layer prior to the nancial crisis, aracting a large swath of credit transactions into the transitory bank-like allocation structure of broker-dealers. Lastly, the diversication layer plays a pivotal role in the allocation of credit assets: in the banking rm allocation, credit assets are pooled, rstly, through the operation of the balance sheet and secondly, through government-backed deposit insurance schemes. In contrast, the pooling of credit assets, when allocated through nancial markets, is prone to complexity and fragility. Also, for most market-based credit assets, there exist no government guarantees. From this it appears that bank-based systems have a competitive advantage over market-based systems at the diversication layer, which is mainly driven by legal diversication. In summary, the rst layer of the eorem nds that the regulatory costs of arranging credit through nancial markets are substantially higher across all three functional market layers. As banking regulation is designed to operate at the rm level, it regulates loans in the aggregate, thus beneting from economies of scale that put a lower per unit regulatory price on individual loans. e second part of the eorem dives deeper into the architecture of credit markets. eoretically rooted in the tradition of the original Coase eorem, it re-conceptualizes the costs of the market, including security regulations, as an externality that has to be borne by either borrower or creditor. us, in a positive transaction cost environment, it tries to imagine an optimal regime where the costs of the market are structurally and legally optimized and assigned to the least cost avoider. At the disclosure layer, the chapter proposes an optimal regime that challenges the concept of a registered security as a traditional form-based disclosure. Instead, it re-imagines mandatory disclosures in the context of credit as a set of robust ground truth data feeds, on the basis of which creditors can calculate custom credit scores. By placing the raw data feed into the control of the borrower, the issuance of an individual ‘credit security’ may be rendered as frictionless as providing access to a private API. Creditors, in turn, can run multiple credit scoring models on the clean data feed to price the securities. e chapter proposes an optimal securities regulation that encourages such an open architecture. Furthermore, it proposes to assign the costs of the data feed to the borrowers or data subjects as the least cost avoiders, while placing the costs of the credit and underwriting models on creditors. At the investment and liquidity layer, the chapter proposes that in the primary markets, credit issuers should be given direct access to credit markets and that means of pre-nancing in the sense of the to-be-announced (TBA) forward seling mortgage markets are encouraged. Both of these measures aim to limit the role and systemic risks of transitory bank-like allocation structures, which are typically associated with underwriting activities in both bond and ABS markets. With respect to secondary market activities, the chapter proposes a transition from a dealer-dominated OTC market microstructure to a closed limit order book infrastructure, which opens access to a wider range of liquidity providers. Lastly, at the diversication layer, under the second part of the eorem, the chapter proposes that diversication vehicles should properly reect the characteristics of the underlying assets. us, to curtail market-induced misallocation in illiquid credit assets, the chapter proposes the promotion of evergreen closed-end fund structures. Secondly, with respect to rm-induced misallocation, which arises where the credit portfolio available to public market investors

209 diverges from the optimal market portfolio, due to the dominance of bank-based loan origination, a regulatory mandate is explored, whereby credit issuers and/or their originating intermediaries are mandated to submit credit oerings to a market-based credit allocation platform, which allows both rms (banks) and market participants (public market investors and pooling vehicles) to a bidding process. In summary, the chapter aims to oer a novel perspective on the allocation of credit through competing allocation structures, namely banks or markets, and to highlight both technical and legal means through which credit markets can be ‘re-priced’ to become more competitive to a bank-based allocation.

210 Chapter 5

Too-distributed-to-fail Credit Systems

211 5.1 Introduction

A longstanding policy debate revolves around the question of whether we should promote bank-based or market-based nancial systems. Economists since the 19th century have argued that bank-based systems are beer positioned to mobilize retail savings, identify sound borrowers and exert corporate control in underdeveloped economies with weak institutional environments. On the other hand, proponents of market-based systems have emphasized the advantages of markets in managing credit risks, eciently allocate capital, produce information and mitigate the eects of powerful banking systems. Economists have produced a vast number of theoretical insights into the comparative advantages of dierent nancial systems. Reecting these schisms, this chapter introduces a network-based stress test model for assessing competing theoretical perspectives through the lens of systemic risk and loss absorption channels. us, the objective of this chapter is to produce a simple theoretical model that (i) distinguishes among competing allocation regimes and that could (ii) assist regulators and policy makers as a mental model in the design of nancial sector reform strategies. Under the existing bank-based architecture, banks largely act as central intermediaries between depositors and borrowers. is generally entails that short-term deposits nance long-term loans, with the bank playing the part of the central operator in a complex nexus of contracts. is imperfect match of assets and liabilities subjects banking institutions to failure risk and can trigger systemic risks if one bank’s failure induces contagion and triggers the failure of many others. At the heart of bank regulation lies the concern for the social and economic costs of such systemic crises, in particular taxpayer-funded bailouts. In the aermath of the global nancial crisis of 2007–2009 (GFC), a plethora of regulatory measures have been introduced to improve prudential regulation. ese measures included, inter alia, counter-cyclical capital buers, enhanced capital requirements and bail-in regimes. However, while these may have been eective short- term xes, these regulatory measures have not addressed the root causes of bank fragility. In particular, these root causes are widely understood to be situated in the binary architecture of bank balance sheets. rough the deliberate mismatch of long-term credit assets (bank loans) and short-term liabilities (bank deposits), banking institutions are ex ante designed to ourish in environments of economic growth, but on the other hand fail in economic stress scenarios. At the same time, the economic allocation of rents are designed to extract excess interest yields for equity holders and bank employees during times of macroeconomic growth, while potentially externalizing losses to society at large during an economic contraction. is imbalance is reected by the ‘gains are privatized and losses are socialized’ narrative, which was popularized in the aermath of the global nancial crisis (GFC). e developed network-based model in a rst instance provides an analytical tool for assessing wealth transfers related to government bailouts. Given the parsimony of our framework, it further allows us to sharpen our understanding of the regulatory measures that were taken in the aermath of the GFC. rough dierent simulations in a stylized ‘one bank economy’, the model highlights how government bailouts can exacerbate wealth dierentials among agents. Furthermore, it demonstrates how recent regulatory measures remain ‘point solutions’ only, which do not address the core architectural challenges associated with bank-based economies. In particular, the inherent fragility associated with the allocation of credit through centralized bank balance sheets by way of maturity transformation. On the other hand, the allocation of credit through markets is generally believed to be more resilient. In markets, losses are absorbed by creditors instead of bank balance sheets. In this chapter, a pure-form market-based nancial system is explored where depositors nance borrowers directly. A theoretical exploration of such a system is of greatest relevance from a policy perspective, as such market-based systems could, at least in theory, reduce the risks of bank runs, public bailouts and existing institutional contagion channels. Our network model allows us to simulate a stylized, too- distributed-to-fail credit market seing where credit is originated and held in a fully distributed nancial architecture with no single point-of-failure. Under this regime, in contrast to bank-based economies, nancial institutions only facilitate the origination of loans, notably without taking any principal risk. e model maps out a stylized distributed credit market system, designed to promote nancial stability under economic stress scenarios by spreading credit losses evenly among creditors and leaving systemically relevant market operators fully functional. However, once government

212 intervention is introduced to the model, the positive eects of too-distributed-to-fail credit systems vanish and the economic situation looks very similar to the bank-based allocation.

5.2 Bank-based vs. market-based systems

5.2.1 Bank-based systems

Proponents of the bank-based view have highlighted the cost advantages of banks in acquiring information about rms and agents, which allow them to improve corporate governance and optimize capital allocation.1 Other proponents have pointed to benets of banking rms when it comes to managing cross-sectional credit and liquidity risks over time (intertemporal risk smoothing)2 and their ability to shi the composition of savings to growth-promoting segments.3 Another stream of the literature has focused on the economies of scale when mobilizing capital.4 Bank-based proponents have also stressed the shortcomings of market-based systems. Bencivenga and Smith(1985) point to the quick dissemination of information in markets, which lets market participants free-ride on market information, thus reducing the incentives for individual investors to acquire such information.5 Banks, on the other hand, can mitigate this problem due to their opaque nature and their long-term relationships with agents.6 Boot and akor(1997) furthermore argue that by pooling investors, banking rms are beer positioned than markets to coordinate and monitoring rms, in particular when it comes to reducing post-lending moral hazard (in particular asset substitution).7 Bank-based arguments also regularly stress the lack of strong governance in liquid capital markets.8 In perfectly liquid capital markets, investors can always sell their assets, such that fewer incentives exist to exert corporate control. In other words, bank-based views stress that market-based systems are ill-positioned to foster corporate control and economic growth. Rajan and Zingales (1998) make the argument that in countries with weaker contract enforcement capabilities, dominant banking rms can be more eective at enforcing debt repayment of rms compared to the credit markets.9 Markets may be too atomistic,

1See Diamond(1984) (‘An intermediary (such as a bank) is delegated the task of costly monitoring of loan contracts wrien with rms who borrow from it. It has a gross cost advantage in collecting this information because the alternative is either duplication of eort if each lender monitors directly, or a free-rider problem, in which case no lender monitors’); Ramakrishnan and akor(1984) (‘is tendency to centralize information production can be viewed as an explanation for the emergence of the traditional pure broker as described in the literature on nancial intermediation.’); See F. Allen and Gale(1990) (‘Intermediated nance is best when costs of information are high and there is not much diversity of opinion. e project is not nanced if there is diversity of opinion and costs of information are high. […] On the other hand, bank-nanced projects will be characterized by uniformity of opinion and the technologies they use will be relatively expensive to asses.’). 2See F. Allen and Gale(1997) (‘A commonly heard argument is that nancial markets are desirable because of the risk-sharing opportunities they provide. It is well known that this is correct as far as cross-sectional risk-sharing opportunities are concerned, but the results of the preceding sections suggest that this argument ignores the possibilities for intertemporal risk smoothing. We have shown in the context of a simple OLG model that an intermediated nancial system can make every generation beer o than it would be with nancial markets alone.’). 3See Bencivenga and Smith(1991) (‘Conditions are provided under which the introduction of intermediaries shis the composition of savings toward capital, causing intermediation to be growth promoting.’). 4See Baltensperger(1972) (‘Exploitation of economies of scale due to uncertainty is in some sense “raison d’etre” for banks. Banks are nancial intermediaries consolidating risk by having as assets the debt of a large number of dierent people independent in their solvency, and having as liabilities deposits of a large number of independently acting depositors. is permits them to hold relatively small amounts of liquid reserves against their liabilities and the associated risk of cash-drains, and relatively small amounts of capital account against their assets and the corresponding risk of capital losses and bankruptcy.’). 5See Bencivenga and Smith(1985) (‘On the other hand, it is not in the interests of any shareholder or small lender to devote much aention to the performance of a rm; for any gains that accrue to him as a result of his actions accrue to all similarly situated suppliers. ere is the free-rider problem which we discussed earlier.’). 6See Boot et al.(1993) (‘e modern literature on nancial intermediation has primarily focused on the role of banks as relationship lenders. In this capacity, banks develop close relationships with borrowers over time. Such proximity between the bank and the borrower has been shown to facilitate monitoring and screening and can overcome problems of asymmetric information.’). 7See Boot and akor(1997) (‘Banks arise as coalitions of agents who coordinate their actions to resolve asset-substitution moral hazard.’). 8See Bhide(1993) (‘e analysis in this paper suggests that enhanced market liquidity has come at a price. Rules that now fragment intermediaries’ holdings prevent them from playing a meaningful shareholder role and may actually have increased the concentration of power.’). 9See Rajan and Zingales(1998) (‘is paper suggests that relationship-based systems work well when contracts are poorly enforced and capital is scarce. Power relationships substitute for contracts, and can achieve beer outcomes than a primitive contractual system.’).

213 lacking the monitoring capabilities of powerful banks. us, external investors may be reluctant to nance industrial expansion in countries with underdeveloped institutions through the credit markets. To summarize the above, the bank-based view holds that banks may be beer positioned than markets to allocate credit, due to scale economies in information processing, long-term credit relationships that reduce informational frictions, beer reduction of moral hazard and asset substitutions through tighter monitoring capabilities. As a result, they hold that bank allocation can boost economic growth more eectively than credit markets.

5.2.2 Market-based systems

In contrast, proponents of the market-based view point to the growth-enhancing role of well-functioning capital markets through a range of channels. Holmstrom and Tirole(1993) have pointed to the fact that deep, liquid capital markets serve as an incentive mechanism for investor to acquire information, since investors can nancially benet from proprietary research.10 Others have pointed to the benets of capital markets when it comes to facilitating risk management.11 M. Jensen and Murphy(1990) have argued that capital markets enhance corporate governance through takeovers and by tying managerial compensation to rm performance.12 Like the bank-based view, the market-based view also stresses problems with the allocation through the banking rm. One set of market-based proponents have argued that powerful banks may stymie innovation by providing large borrowers that have close bank ties with non-competitive rates and by extracting informational rents.13 Others have argued that in the absence of regulatory restrictions, powerful banks may engage in collusive behaviour with rm managers against other creditors.14 Market-based views hold that in contrast to opaque banking systems, transparent capital markets are more ecient at diusing market signals to investors, which can boost aggregate nancing levels and economic growth.15 us, proponents of the market-based view stress that markets will reduce the inherent ineciencies associated with banks and enhance economic growth.

5.3 Systemic risk

5.3.1 Bank failures

Banks are inherently fragile. is is not because of asset bubbles or bank runs - these only expose a bank’s vulnerability, but are not their actual root cause. Rather, it is the underlying nexus of contracts with lenders, borrowers, depositors and shareholders that denes a bank’s assets-liability-structure and thereby its resilience. ese contracts set out heterogeneous credit risks, liquidity proles and maturity terms and it is their mismatch that creates imbalances in the banking institution’s balance sheet. e theory of nancial intermediation sets out a number of activities as the core function of banks, commonly referred to as qualitative asset transformation, whereby these credit risk, liquidity and

10See Holmstrom and Tirole(1993) (‘Like any successful institution, the stock market serves several purposes, many of them unforeseen at the time the institution was created. ere is lile doubt that the stock market was set up for other reasons than managerial monitoring; in particular, risk sharing and acquisition of capital were major benets. But it seems equally clear that the stock market today performs an important role as a monitor of management, both directly by assessing past contributions to value and indirectly as a market for corporate control.’). 11See R. Levine(1991) (‘Stock markets arise in this model to help agents manage liquidity and productivity risk, and, in so doing, stock markets accelerate growth.’); Obstfeld(1994) (‘is paper has demonstrated that international risk sharing can yield substantial welfare gains through its positive eect on expected consumption growth.’). 12See M. Jensen and Murphy(1990) (e threat of takeovers also provides incentives since managers are oen replaced following a successful takeover’). 13Hellwig(1991) (‘As cartelization increases the gross returns owing from industry to its nanciers, it will also improve the position of the enforcing banks in the competition for funds. In the absence of countervailing eects, the process of competition for funds should thus give rise to a banking industry which is concentrated or coordinated enough to impose cartel behaviour on its industrial clients, using the returns to aract deposits’) Rajan (1992) (‘is paper argues that while informed banks make exible nancial decisions which prevent a rm’s projects from going awry, the cost of this credit is that banks have bargaining power over the rm’s prots, once projects have begun. e rm’s portfolio choice of borrowing source and the choice of priority for its debt claims aempt to optimally circumscribe the powers of banks.’). 14Wenger and Kaserer(1998)) (‘Obviously, banks may use their voting power to appoint bank representatives as members of the supervisory board, and thereby exercise control over the management.’). 15See F. Allen and Gale(1990) (‘e model implies market-nanced projects will be characterized by considerable diversity of opinion about their likely commercial success and the technologies they are based on will be relatively cheap to assess.’); Boot and akor(1997) (‘A key aribute of the nancial market, and one that delineates its role from that of a bank, is that there is valuable information feedback from the equilibrium market prices of securities to the real decisions of rms that impact those market prices. is information loop provides a propagation mechanism by which the eects of nancial market trading are felt in the real sector. Bank nancing does not have such an information loop.’).

214 maturity characteristics are transformed.16 In terms of maturity structure, a traditional bank operates within the nexus of three basic types of contracts:

• Long-term loan contracts with bank borrowers;

• Short-term debt contracts with depositors/creditors; and

• ‘Innite-term’ equity contracts with shareholders.

e arrangement and operation of time in-congruent contract portfolios is commonly known as ‘maturity transformation’ and constitutes a core economic function of banking rms.17 Diamond and Dybvig(1983b) highlight that banks engaging in maturity transformation can be beer at risk sharing compared to capital markets, as banks facilitate long-term investment projects and at the same time serve investors’ liquidity needs.18 Calomiris and Kahn(1991b) and Parlour and Rajan(2001) point out that nancing long-term assets through short-term deposits can act as a disciplining mechanism for bank managers.19 While this contractual setup creates economic welfare by connecting surplus agents with decit agents over the time continuum, it is also a primary cause of bank instability.20 In particular, in times of crisis, as asset prices decline, it exposes banks to the risk of bank runs, whereby depositors withdraw short-term funding and banks are forced to liquidate assets in re sales.21 Several authors22 have thus argued that nancing may be excessively short-term, exposing nancial institutions to signicant roll-over risk. e recent global nancial crisis has exposed this problem23 and forced banks and policymakers alike to come up with workable solutions to beer deal with this perennial problem of banking in order to avoid loss socialization through public bailouts going forward. While there exist a variety of well-known approaches to address maturity gaps on the bank’s balance sheet, the nancial crisis demonstrated that the regulatory toolkit had to be extended to give supervisory bodies more exibility during times of market disruption.

5.3.2 Credit market failures

A market-based nancial system architecture is generally believed to have beer risk-sharing properties.24 Indeed, as will be demonstrated within the scope of this chapter, in a pure market-based credit system, credit losses can be

16See Bhaacharya and akor(1993) (holding that the major aributes modied through qualitative asset transformation by nancial institutions are term to maturity, divisibility, liquidity and credit risk). 17See Entrop et al.(2015) (‘Maturity transformation evolves in most cases as a consequence of the provision of liquidity when xed-rate long-term loans are nanced using short-term deposits’); Diamond and Dybvig(1983b) (‘Banks are able to transform illiquid assets by oering liabilities with a dierent, smoother paern of returns over time than the illiquid assets oer.’). 18See Diamond and Dybvig(1983b) (‘is paper shows that bank deposit contracts can provide allocations superior to those of exchange markets, oering an explanation of how banks subject to runs can aract deposits.’). 19See Calomiris and Kahn(1991b) (‘Given these costs, demandable debt seems inferior to both maturity-matched debt and equity contracting. However, in this paper, we show that demandable debt has an important advantage as part of an incentive scheme for disciplining the banker.’). 20See Calomiris and Kahn(1991b) (‘By issuing demandable debt, banks created a mismatch between the maturity of assets and liabilities. is mismatch le them exposed to the possibility that depositors would aempt to withdraw more funds than a bank could supply on short notice. When this occurred, the consequences were costly. Individual banks that did not meet their obligations were forced into expensive procedures (liquidation or receivership) that would not have arisen in an equity-based or maturity-matched contracting structure’). 21See Diamond and Dybvig(1983b) (‘During a bank run, depositors rush to withdraw their deposits because they expect the bank to fail. In fact, the sudden withdrawals can force the bank to liquidate many of its assets at a loss and to fail’). 22See Brunnermeier and Oehmke(2013) (showing that extreme reliance on short-term nancing may be the result of a what they refer to as a maturity rat race: borrowers shortening the maturity of debt contracts to dilute other creditors and in turn, other creditors opting for shorter maturities as well); Diamond(1991) (‘Short-term lenders liquidate (are unwilling to renance current management) too oen from the borrower’s point of view because there are constraints on pledging future rents to lenders: the amount that can be pledged to lenders may be less than the value they receive from liquidation, yet the total future rents exceed the liquidation value’). 23See Brunnermeier and Oehmke(2013) (‘One of the central lessons of the nancial crisis of 2007–2009 is the importance of maturity structure for nancial stability: the crisis vividly exposed the vulnerability of institutions with strong maturity mismatch—those who nance themselves short-term and invest long-term—to disruptions in their funding liquidity.’); Brunnermeier(2009) (‘In summary, leading up to the crisis, commercial and investment banks were heavily exposed to maturity mismatch both through granting liquidity backstops to their o-balance sheet vehicles and through their increased reliance on repo nancing.’); Shin(2009) (‘e Northern Rock episode oers an opportunity to revisit some of the economic principles behind the use of short-term debt to nance long-term assets – which is of course essentially the classic model of how banks work. When the nancial system as a whole nances long-term, illiquid assets by short-term liabilities, not every institution can be perfectly hedged in terms of its maturity prole. Northern Rock could be seen as such a ‘pinch point’ in the nancial system, where tensions would nally be manifested’). 24See Hmida and Brahmi(2016) (‘Bonds and securitised nance generally are thought to have beer risk-sharing characteristics. Risks can be more eciently diversied when they are spread across a large number of individual security holders. is spreading of risks and the existence of liquid secondary markets in standardised securities encourages creditors to make long-term commitments and allows debtors to borrow for extended periods of time.’).

215 eectively absorbed by a large number of creditors and across national borders.25 us, unlike under an allocation through banking rms, credit losses should not result in institutional failures. However, in reality, credit markets are highly intermediated. Both traditional underwriting activities in bond markets, and the ‘originate-to-distribute’ model in asset-backed securities (ABS) markets require nancial intermediaries which assume credit risk. In this sense, credit markets typically require ‘transitory’ bank allocation of originated credit assets. is means that the operation of credit markets entail maturity transformation of market-enabling institutions, such as investment banks, similar to traditional commercial banking operations. Such maturity transformation can either take place directly through the balance sheets of investment banks,26 which underwrite and originate credit securities, or through o-balance sheet special purpose vehicles (SPV), such as asset-backed conduits.27 As the recent credit crisis has demonstrated, the inventory risk associated with such underwriting activities can lead to substantial systemic risks, similar to traditional bank risks. If broker-dealers nance credit securities through short-term wholesale funding and the market liquidity of these credit assets dries out, there is a risk of institutional bank runs, similar to traditional commercial bank runs. During the nancial crisis, this happened most famously in the case of Lehman Brothers, which nanced illiquid subprime mortgage credit assets through short-term repurchase agreements in the so-called ‘third-party repo markets’.28 e spillover eects from the failure of Lehman Brothers to more traditional banking rms eventually led to the largest government bailout of nancial institutions through the Troubled Asset Relief Program (TARP).29 In particular, this also included a c. $180bn bailout of mortgage credit markets via the bailout of the government-sponsored enterprises (GSE) Fannie Mae and Freddie Mac.30 While these rms were theoretically supposed to operate under the transitory ‘originate-to-distribute’ credit origination model, they ended up holding substantial credit exposure on their books.31 is eectively rendered them into bank-like allocation structures, which exposed the nancial system to similar institutional risks.

5.4 Regulatory responses

5.4.1 Bank regulation

e illiquidity of the bank’s long-term loan portfolio, nanced by short-term deposits, is the crux of the banking regulation puzzle.32 Traditionally, regulators have addressed the contractual maturity mismatch problem by prescribing banks a certain design of their ‘contractual portfolio’. In particular, the regulators manage the banking rm’s balance sheet composition, most notably the bank’s equity portion. ese measures are referred to as ex-ante regulatory tools.

25See Homann and Nitschka(2012) (nding that risk-sharing in mortgage-backed securities involved a unilateral ow of funds ‘Finally, in inter- preting our results, it is important to bear in mind that the bulk of the international trade in mortgage-backed securities before the recent nancial crisis was unidirectional, with the United States predominantly selling and the rest of the industrialized world buying these assets - a stylized fact that we document in detail in the remainder of the paper.’). 26See Brunnermeier(2009) (‘Another important trend was an increase in the maturity mismatch on the balance sheet of investment banks. is change was the result of a move towards nancing balance sheets with short-term repurchase agreements, or “repos.”’). 27See Schroth, Suarez, and Taylor(2014) (estimating for asset-backed commercial paper (ABCP) conduits that before the recent nancial crisis of 2008–2009, long-term assets with an average duration of around 5.8 years were nanced with short-term ABCP securities with an average maturity of around 37 days). 28See Copeland, Martin, and Walker(2014) (‘e repo market has been viewed as a potential source of nancial instability since the 2007 to 2009 nancial crisis, based in part on ndings that margins increased sharply in a segment of this market. […] While we do not observe a signicant tightening of funding conditions in the tri-party repo market, we document a large and precipitous decline in the tri-party repo book of Lehman Brothers in the days preceding the bankruptcy of its holding company.’). 29See Farruggio, Michalak, and Uhde(2013) (‘e 2007–2009 global nancial crisis triggered a unique liquidity shock aecting a number of banks worldwide. As a response, comprehensive governmental capital assistance programs have been introduced in many countries. As regards the US, under the “Troubled Asset Relief Program” (TARP) the Department of the Treasury provided USD 204.9 billion in capital to 707 institutions in 48 states helping banks to absorb losses from toxic and illiquid assets.’). 30See Jeske, Krueger, and Mitman(2013) (‘In September 2008, the US Treasury took conservatorship of Fannie Mae and Freddie Mac aer huge losses following the collapse of house prices. Since then, the U.S. government has provided about $180 billion to help GSE’s remain solvent.’). 31See V. V. Acharya et al.(2013) (‘Securitization was traditionally meant to transfer risks from the banking sector to outside investors and thereby disperse nancial risks across the economy. Because the risks were meant to be transferred, securitization allowed banks to reduce regulatory capital. However, in the period leading up to the nancial crisis of 2007–2009, banks increasingly devised securitization methods that allowed them to retain risks on their balance sheets and yet receive a reduction in regulatory capital, a practice that eventually contributed to the largest banking crisis since the Great Depression.’). 32See Diamond and Dybvig(1983a) (‘Illiquidity of assets provides the rationale both for the existence of banks and for their vulnerability to runs.’).

216 5.4.1.1 Ex-ante regulatory tools

To this day, these are still the main tools within the banking regulation toolset. ere exist multiple points of entry for regulating a bank’s balance sheet. Regulating bank capital essentially means that regulators prescribe the bank to enter into contracts with its shareholders in a certain proportion to the outstanding notional of its contracts with creditors (liabilities) and borrowers (assets). Similarly, the introduction of the liquidity coverage ratio by the Basel III framework prohibits banks to deploy a certain portion of their funds to long-term, illiquid contracts. Lastly, the acceptance of contingent convertible bonds as regulatory tier one capital provides banks with incentives to enter into debt contracts with a stress scenario conversion contingency. e below list provides a non-comprehensive menu of such ex-ante regulatory measures in a contractual framework:

• Capital Buers: Since equity does not mature per se, but is retired/re-paid only upon the liquidation of the rm, increasing a bank’s equity ratio naturally adds to the resilience of the bank’s contractual system. In the terms of our contract framework: long-term loans on the asset side are matched by a higher proportion of ‘innite-term’ equity on the liability side.

• Liquidity Reserves: the maturity mismatch can also be curtailed via the asset side of the balance sheet, namely by transforming the maturity prole of assets, in particular by forcing banks to hold more ‘liquid’ assets.33 Liquid assets either have a maturity of zero (cash or reserves held with the central bank), are short-term (commercial paper) or can be transformed quickly into cash due to the risk prole of the borrower (sovereign or inter-bank debt).

e regulatory overhaul in the aermath of the global nancial crisis has put most of the initial focus on these ex- ante regulatory tools. Notably, the Basel III framework35 has (i) increased the minimum capital buer, (ii) introduced a liquidity coverage ratio 36 and (iii) has made it aractive for banks to issue CoCos.37

5.4.1.2 Ex-post regulatory tools

In the aermath of the nancial crisis, a new set of bank regulatory tools have been introduced, which mark a fundamental departure from traditional nancial regulation, by shiing the focus from ex-ante to ex-post contractual design regulation. As part of these tools, the nancial regulator amends or overrides previously entered contracts ex post. In particular, these ex-post regulatory tools entail:

• Special resolution mechanisms: ese measures, inspired by the Federal Deposit Insurance Corporation (FDIC)

33See Calomiris and Kahn(1991a) (‘If depositors en masse aempted to withdraw funds from the entire banking system, banks as a group were forced to suspend convertibility of their liabilities into specie on demand. Such suspension was also disruptive and costly. To defend against either of these undesirable consequences, banks had to hold a proportion of their assets in idle reserves to insulate themselves from excessive withdrawals.’); Aldasoro and Faia(2016) (‘Equity requirements are meant to control and prevent the spread of losses on banks’ asset side. Liquidity requirements, newly introduced in Basel III and subsequent regulations (CRD IV and CRR), aim at mitigating the impact of liquidity freezes.’). 34See Jang et al.(2018) (‘One remarkable evolution in the capitalization of banks under this new regulation is the emergence of a new hybrid asset class called contingent convertible bonds or CoCos for short. CoCos are a type of bond that is automatically converted into equity or wrien down when the issuer’s capital-ratio falls below a specied level. is automatic conversion characteristic means that CoCos are expected to reduce the economic costs of bankruptcy for the benet of all debt and equity holders.’); McDonald(2013) (‘A frequently discussed reform is to have banks issue claims that behave like debt during normal times and which convert to equity during a crisis. Such claims are variously referred to as “reverse convertibles” and “contingent capital”. Because these claims convert to equity, contingent cap-ital is a buer against default.’). 35Bank of International Selement (BIS)(2010). 36Bank of International Selement (BIS)(2017). 37Bank of International Selement (BIS)(2011).

217 receivership regime,38 provide an alternative to the traditional bankruptcy process,39 giving a resolution author- ity40 the rights to separate a bank into a robust part, the ‘good bank’, and a fragile or non-viable part, the ‘bad bank’.41 Basically, such good-bank/bad-bank measures aim to eliminate the crisis elements that have led to a disruption in the maturity transformation process, e.g. through hard-to-value assets that have experienced a price shock and led to a funding squeeze on the liability side.

• Public bail-in measures: rough public bail-in measures, private bank debt is restructured by way of a debt- equity-swap triggered by a public sector resolution body.42 In order to complete this process in a timely manner in response to an imminent crisis, ordinary contractual rights of both shareholders and creditors are waived for the time of the resolution.43 In contrast to bailouts, which aect public debt, losses are absorbed by existing creditors.44 Bail-in measures address the maturity mismatch problem by converting medium- or long-term bank debt into ‘innite-term’ equity.45

5.4.2 Credit market regulation

As a reaction to the nancial crises, a number of regulations were introduced to address the shortcomings in credit markets, in particular with respect to asset-backed securities (ABS) markets:

• Measures to increase transparency: As a result of the failure of investors to price pre-crisis credit assets, in particular residential mortgage-backed securities (RMBS), higher disclosure requirements have been introduced in the aermath of the crisis. In the US, for example, Regulation AB II 46 now requires sponsors of SEC-registered ABS securities to provide loan-level credit data through comprehensive Asset Data Files.

• Increased credit risk retention: Following calls by the IOSCO to require originators and/or sponsors of asset- backed securities (ABS) to retain a long-term economic exposure to originated ABS assets, US legislators intro-

38See Morrison(2009) (‘e Federal Deposit Insurance Corporation (“FDIC” or “the Corporation”) has authority to seize control of a commercial bank that is approaching (or has entered) insolvency or has engaged in conduct signaling fraud or unsound risk management practices. Once it intervenes, the FDIC has broad power to succeed to the institution, operate it, revoke its charter, remove management, and choose whether to liquidate the bank or reorganize it.’). 39See Morrison(2009) (discussing why the ordinary bankruptcy process is insucient ‘[…] the bankruptcy process is managed by a judge. ough federal regulators are subject to political pressure, they possess expertise that is generally beyond the ken of judges. When a systemically important institution suers distress, rapid decision making is necessary. Federal law permits this kind of speed when the FDIC seizes a bank.’). 40Under Dodd-Frank, an “Orderly Liquidation Authority” (OLA) was created that shares many features of the traditional bankruptcy procedures. 12 C.F.R. Part 380. 41See Armour(2014) (‘e rst generation of such procedures, which generally were based on the Federal Deposit Insurance Corporation (FDIC) receivership regime in the US, involve a waiver of creditors’ ordinary property rights in order to complete the process extremely rapidly. ‘Good’ assets and depositors’ claims are transferred to a purchaser literally overnight, and the ‘bad’ assets that remain in the rump entity are wound down gradually in a way that does not transmit a shock.’); Baird and Morrison(2011) (‘e recently enacted nancial reform legislation empowers the Secretary of the Treasury to appoint the Federal Deposit Insurance Corporation (FDIC) as receiver for troubled nancial companies when their failure poses a systemic risk. Previously, the resolution process for these companies was le to the bankruptcy process. By common account, the new law reects a repudiation of traditional bankruptcy law when it comes to the collapse of giant corporations that threaten the economy as a whole. Instead we have a mechanism that brings the regime used to liquidate failed commercial banks to a broader range of institutions.’). 42See Armour(2014) (‘However, the ‘bail-in’ powers are very dierent from the rst-generation resolution mechanisms: they are, in eect, expedited reorganisation procedures, as opposed to liquidation procedures. at is, they envisage the same corporate entity remaining, but with a restructuring of the terms of its nancing.’). 43See Avgouleas and Goodhart(2015) (‘Essentially, bail-in provisions mean that, to a certain extent, a preplanned contract replaces the bankruptcy process, giving greater certainty as regards the suciency of funds to cover bank losses and facilitating early recapitalization. Moreover, the bail-in tool can be used to keep the bank as a going concern and avoid disruptive liquidation or dismembering of the nancial institution in distress.’). 44See Barucci et al.(2019) (‘By replacing the bail-out of a bank with a bail-in, the new rules try to break the direct link between bank troubles and public debt and to improve the bank governance, which should benet from a more accurate oversight from shareholders and bondholders.’). 45See Dewatripont(2014) (stressing the importance that eective bail-in regimes should ensure that banks have ‘sucient long-term securities that can be bailed-in before deposits start to face risk’.). 4617 C.F.R Parts 229, 230, 232.

218 duced Article 15G of the Securities Exchange Act of 193447 through section 941 of the Dodd-Frank Act. rough the amended regulations, ABS sponsors are now required to retain at least 5 per cent of any credit risk they sell or transfer by way of an asset-backed security.

• New regulations on credit rating agencies: In the United States, the Dodd-Frank Act,48 through Title IX, Sub- title C, ‘Improvements to the Regulation of Credit Rating Agencies’, has imposed new requirements on nationally recognised statistical rating organisations (‘NRSROs’), such as ‘look-back’ policies and procedures to review the credit ratings, including upgrades and downgrades.

5.5 A distributed systems perspective

e network model, simulation and conclusions developed in this exploratory study draw inspiration from other research elds where distributed systems have been explored in more detail already, such as computer sciences or biology. In conceptualizing potential architectural design choices and underlying network dynamics, the eld of distributed computing oers particularly interesting insights that have not yet been incorporated in the law and economics literature.

5.5.1 Distributed systems in computer sciences

In computer science, a distributed system can be dened as (i) a collection of independent computers that appears to its users as (ii) a single coherent system (Tanenbaum & van Steen, 2007). To expand on the rst aspect, namely the multiplicity of independent components: in distributed computing these are heterogeneous computer components with dierent computational processes or computing devices.49 A distributed system makes no assumptions on the individual nature or behaviour of these devices. However, it requires both a multiplicity of these components and thereby a duplication of critical functions of the system, also known as replication or redundancy.50 is redundancy is required to increase reliability of the system. is aspect of distributed systems is here referred to as architectural distribution. With respect to the second aspect, namely coherence, this requires that the individual nodes communicate and coordinate their actions to achieve a common goal. Such coherence requires collaboration and a common language to achieve uniformity of outcome. us, while it leads to an uncoupling in terms of control from central components, it requires a common protocol or logic. is property of distributed systems is referred to as logical centralization.51 To give a concrete example of such a distributed system, imagine a computer network running dierent applications on dierent operating systems. In this context, a distributed system can be thought of as the middleware layer52 that allows applications to run on multiple machines, oering each application the same interface. Users can thereby assign computational tasks to any of these connected machines and are therefore no longer bound by the limitations of their local processing power.

4715. U.S.C. 78o-11. 48Pub. L. No. 111-203, 124 Stat. 1376, H.R. 4173 (July 21, 2010). 49See Singhal and Shivaratri(1994) (‘A collection of computers that do not share common memory or a common physical clock, that communicate by a messages passing over a communication network, and where each computer has its own memory and runs its own operating system. Typically the computers are semi-autonomous and are loosely coupled while they cooperate to address a problem collectively’); Goscinski(1991) (‘A term that describes a wide range of computers, from weakly coupled systems such as wide-area networks, to strongly coupled systems such as local area networks, to very strongly coupled systems such as multiprocessor systems’). 50See Tanenbaum and van Steen(2007) (‘e key approach to tolerating a faulty process is to organize several identical processes into a group. e key property that all groupshave is that when a message is sent to the group itself, all members of the group receive it. In this way, if one process in a group fails, hopefully some other process can take over for it.’); Kshemkalyani and Singhal(2008) (‘Replication (as in having backup servers) is a classical method of providing fault-tolerance. e triple modular redundancy (TMR) technique has long been used in soware as well as hardware installations.’); Neuman(1994) (A service or resource is replicated when it has multiple logically identical instances appearing on dierent nodes in a system.’). 51See Levin, Wundsam, Heller, Handigol, and Feldmann(2012) (on logically centralized distributed systems ‘In essence, SDN gives network designers freedom to refactor the network control plane, allowing network control logic to be designed and operated as though it were a centralized application, rather than a distributed system – logically centralized.’). 52See Tanenbaum and van Steen(2007) (‘In order to support heterogeneous computers and networks while oering a single-system view, distributed systems are oen organized by means of a layer of soware-that is, logically placed between a higher-level layer consisting of users and applications, and a layer underneath consisting of operating systems and basic communication facilities, as shown in Fig. 1-1 Accordingly, such a distributed system is sometimes called middleware.’); Kshemkalyani and Singhal(2008) (‘e distributed soware is also termed as middleware. […] e middleware is the distributed soware that drives the distributed system, while providing transparency of heterogeneity at the platform level.’).

219 e logic that decides which tasks to send to which machine is commonly referred to as the load balancing algorithm.53 ere exist dierent load balancing algorithms with the common goal of optimizing the distribution of workloads across multiple nodes, thereby improving the performance of each node and the overall system. ere are many technical and architectural advantages of using a distributed system with load balancing instead of relying on a single central node. e most noteworthy advantages to highlight for the purpose of this chapter are:

• Resilience: due to the redundancy of critical functions in the distributed system, there exists no single-point-of- failure. By relying on many separate components performing a similar function, partial failure is possible without disrupting the entire system. is makes a distributed system more resilient and reliable than a centralized system.

• Scalability: well-designed distributed systems can handle the addition of users and resources without suering loss of performance or any increase in administrative complexity (Neuman, 1994).54

5.5.2 Distributed systems in the eld of biology

From the molecular to the cellular to the organism level, many biological systems operate as distributed systems without central control.55 Collective natural bio-systems oen solve information processing and messaging problems similar to the ones faced by computational systems. ey are composed of multiple bio-entities, which interact in the physical environment through complex collective behaviors. Examples of such distributed systems, which are coordinated through the laws of nature, include the coordinated motion of sh swarms,56 the election of leader cells during the development of a y’s nervous system,57 or the maintenance and repair of arboreal ants’ trail networks.58 At a high level of abstraction, the behavior of such systems can be modeled as distributed computational processes. is has given rise to a number of computational algorithms in the eld of distributed systems that have been inspired by bio-systems. Such nature-inspired computing algorithms include the mapping of ant colony optimization behaviour to computational processes using multi-agent distributed middleware59 and the honeybee mating optimization algorithm for the task assignment in distributed systems.60 Such interdisciplinary applications between the eld of biology and computational sciences give rise to the hope that applications of computational algorithms in the eld of nance could provide us with novel insights and mechanisms to construct more resilient nancial architectures.

53See Gopinath and Vasudevan(2015) (‘Load balancing is a method that distributes the workload among diverse nodes in the given environment such that it ensures no node in the system is over loaded or sits idle for any instant of time. An ecient load balancing algorithm will make sure that every node in the system does more or less same volume of work. e responsibility of load balancing algorithm is that to map the jobs which are set forth to the cloud domain to the unoccupied resources so that the overall available response time is improved as well as it provides ecient resource utilization’). 54See Tanenbaum and van Steen(2007) (‘In principle, distributed systems should also be relatively easy to expand or scale. is characteristic is a direct consequence of having independent computers, but at the same time, hiding how these computers actually take part in the system as a whole.’); Kshemkalyani and Singhal(2008) (‘As the processors are usually connected by a wide-area network, adding more processors does not pose a direct boleneck for the communication network.’). 55See Navlakha and Bar-Joseph(2014) (‘In biology, networks depict how molecules (metabolites, proteins), cells (bacteria, neurons), or organisms (ants) interact to jointly solve problems and coordinate responses. In computer science, they depict how processors, machines, and devices communicate and process information. On the biological side, systems that involve dynamic networks and message passing (either within and between cells or between members of a population) are oen well suited for ’distributed thinking.’ From the computational point of view, mobile and sensor networks are ideal candidates that can benet from new models and algorithms.’). 56See Ioannou, Gual, and Couzin(n.d.) (‘Movement in animal groups is highly varied and ranges from seemingly disordered motion in swarms to coordinated aligned motion in ocks and schools.’). 57See Afek et al.(2011) (‘Maximal independent set (MIS) selection is a fundamental distributed computing procedure that seeks to elect a set of local leaders in a network. A variant of this problem is solved during the development of the y’s nervous system, when sensory organ precursor (SOP) cells are chosen.’). 58See Chandrasekhar, Gordon, and Navlakha(2018) (‘We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and nding alternative paths to route around broken links in the network.’). 59See Ilie and Badica(2013) (‘is paper presents a congurable distributed architecture for ant colony optimization. We represent the problem environment as a distributed multi-agent system, and we reduce ant management to messages that are asynchronously exchanged between agents. e experimental setup allows the deployment of the system on computer clusters, as well as on ordinary computer network.’). 60See Kang, He, and Deng(2012) (‘Eective task assignment is critical for achieving high performance in heterogeneous distributed computing systems. However, there is a possibility of processor and network failures and this can have an adverse impact on applications running on such systems. is paper proposes a new technique based on the honeybee mating optimization (HBMO) algorithm for static task assignment in the systems, which takes into account both minimizing the total execution and communication times and maximizing the system reliability simultaneously.’).

220 5.5.3 Distributed systems perspective in economics and nance

Despite the many potential applications, theoretical research at the intersection of distributed computational systems and economics/nance is still nascent. Huberman and Hogg(1995) and Huberman(1998) have analyzed how distributed computational systems could benet from economic approaches and an understanding nancial market mechanisms.61 Axtell(2003) has espoused the reverse by looking at ways in which economics and social sciences more generally can be understood as a distributed computational system.62 In contrast to the scarcity of theoretical work at the intersection of nance and computing, at an application level, the advances of distributed computing have already shaped the nance literature and paved the way for a number of novel practical applications. Garcia, Chau, and Spiteri(2011) rely on parallel iterative synchronous and asynchronous distributed computing algorithms to calculate American and European options derivatives.63 Furthermore, through blockchain-based64 virtual currencies, such as Bitcoin and Ethereum,65 which combine concepts of cryptography and distributed computing, distributed systems architectures have even shaped a new nancial asset class.

5.5.4 Distributed systems perspective in this chapter

In the tradition of Axtell(2003), this paper proposes that nancial systems, at a high level of abstraction, can be viewed as distributed systems. Like a distributed computing system, the nancial system is composed of a multiplicity of independent nodes. Both in bank-based and market-based systems, the nodes in a nancial system can be represented by heterogeneous economic agents. ese economic agents dier in terms of their asset endowment, risk tolerance and investment behavior. In the stylized model developed within this chapter, economic agents are divided into surplus and decit agents. Surplus agents are further divided into risk-averse nodes, i.e. depositors/savers, and risk-seeking nodes, i.e. equity holders. Similarly, decit agents can be divided into high-risk and low-risk borrower nodes. Each node, by providing or seeking credit, duplicates a critical economic function in the system and thus increases redundancy. Whereas in distributed computing, computational tasks are assigned to connected machines, the equivalent in a credit system are interest payments and/or credit losses, which are passed from borrowers to credit providers. e logical coherence of the distributed credit system is provided through the prevalent economic and legal system. It is important to note that the current system of nancial regulation is not an open system, which allows nodes and node connectors (banks or market platforms) to play around freely with dierent network topologies, rewiring the network at ease until resilience is maximized. e legal system provides a limited number of acceptable forms of intermediation, which prohibits an experimental exploration of distributed systems. It draws geographic and demographic boundaries, denes the set of participating nodes and sets out the logic by which gains and losses are transmied within the network. Within a distributed nancial system, corporate laws and bankruptcy codes set out heterogeneous ‘load balancing algorithms’, whereby credit losses are allocated over surplus agents. e goal, like for all distributed systems, is to

61See Huberman and Hogg(1995) (‘is paper will explain this analogy and explore one of its main implications: that economics may oer new ways to design and understand the behavior of these emerging computational systems. In particular, markets provide examples of methods to deal successfully with coordinating asynchronous operations in the face of imperfect knowledge. A computational system set up along market rules can allow the system as a whole to adapt to changes in the environment or disturbances to individual members.’); Huberman(1998) (‘is paper describes how computers have evolved to a point where economics approaches are useful for designing them and understanding their dynamics. Examples are given of existing computer systems that use market mechanisms and of novel phenomena, such as clustered volatility, that we uncovered when studying their evolution.’). 62See Axtell(2003)(‘Viewing human society as a large-scale distributed system for the production of individual welfare leads naturally to agent computing. Viewing human society as a large-scale distributed system for the production of individual welfare leads naturally to agent computing. Indeed, it is argued that agents are the only way for social scientists to eectively harness exponential growth in computational capabilities.’). 63See Garcia et al.(2011) (‘is paper deals with the numerical solution of nancial applications, more specically the computation of American and European options derivatives modeled by boundary value problems. In such applications we have to solve large-scale algebraic linear systems. We concentrate on synchronous and asynchronous parallel iterative algorithms carried out on Grid’5000, by using an experimental peer-to-peer platform.’). 64See Yong and Fei-Yue(2018) (‘Broadly speaking, blockchain can be viewed as a novel decentralized architecture and distributed computing paradigm, which stores data with encrypted chained blocks, veries data with distributed consensus algorithms, guarantees security and privacy in data access and transmission with cryptography, and manipulates data with self-executed program scripts (i.e., smart contracts).’). 65See Yong and Fei-Yue(2018) (‘Bitcoin is in essence an electronic cash generated in the distributed systems. e issuance of Bitcoin relies on a consensus competition among distributed network nodes, known as proof-of-work (PoW)-based mining, instead of a specic centralized authority.’).

221 optimize the distribution of workloads across multiple nodes, thereby improving the performance of each node and the overall system.

5.5.4.1 Load balancing through the banking rm

Under a bank-based system, banks are the dominant middleware layer between surplus and decit agents. ereby, each bank acting as an intermediary runs a local distributed system ‘within the rm’. rough the bank’s balance sheet, credit supply and demand of a limited number of participating nodes is orchestrated through a rm-based load balancing mechanism. e participating nodes, i.e. shareholders, depositors, borrowers and other banks, rely on the central node to connect their capital through the banking system. e dominant load balancing algorithm of bank-based systems entails that (i) losses from borrower nodes are rst passed on to shareholder nodes and, once the absorption capacity of equity holders has been fully exhausted, the remaining losses are then passed on to (ii) inter-bank lenders (iii) commercial lenders, such as bond or commercial paper holders, and – as the ‘nal sink’ – (iv) depositors. However, in practice, loss absorption through creditors is rarely ever played out in full, as governments oen step in and rescue failing banks before the full loss cascade has materialized. In other words, there is a deviation from the ex-ante legally codied load balancing algorithm and the ex-post realization. In the model proposed in this chapter, both the dominant ex-ante and ex-post load balancing algorithms are mirrored and contrasted with a fully distributed market-based system.

5.5.4.2 Load balancing through the credit markets

Under a market-based system, nancial or credit markets can be viewed as the middleware layer between surplus and decit agents. ereby, creditors provide direct nancing to participating borrower nodes. In a pure market-based credit system, the prevalent load balancing mechanism that governs the transmission of credit exposure (performance or non-performance of credit obligations) from borrower nodes to creditors can sit at two ends of a diversication spectrum:

• Disintermediated market-based system: within the scope of this chapter, a ‘disintermediated credit system’ is understood as a credit system were creditors provide nancing to individual borrowers in a fully undiversied manner. In particular, for the purpose of the stress test model, it is assumed that each creditor nances exactly one borrower.

• Distributed market-based system: within the scope of this chapter, a ‘distributed credit system’ is understood as a credit system were creditors provide nancing to all borrowers in a fully diversied manner. In particular, for the purpose of the stress test model, it is assumed that all creditors pool their savings and provide nancing to all borrowers through one pooled credit vehicle.

e above credit systems, whether fully concentrated or fully diversied, assume a pure-play market-based allocation. However, in practice, market-based credit exposure is oen held through a complex web of nancial institutions. In addition, the eective load balancing mechanisms that play out in credit markets can also involve the intervention of governments (see below).

5.5.4.3 Load balancing through the state

Government involvement, in particular bailouts of banking rms or credit markets, can be viewed as an alternative ‘all-encompassing’ distributed load balancing mechanism. In the model presented in this chapter, it is assumed that such government actions are nanced by all nodes in the credit network. bailouts are characterized by the government incurring present debt, which the state can nance through future tax revenues. In other words, government-based load balancing mechanisms allow losses to be spread among a wide range of economic agents.

222 e main benet of these actions is that they (i) can be executed more quickly than private load balancing mechanisms and, that (ii) due to the government’s unique ability to spread losses across all agents, they are more likely to calm down markets and banking rm stakeholders.

5.6 Stress test model

e stress test model developed in this chapter is a multi-period network model with dierent economic agents, including banks, borrowers, shareholders and depositors. In order to study systemic risk and its prudential regulation under dierent nancial architectures, the model incorporates (i) discrete deterministic credit asset states (ii) economic externalities from the default of credit assets on banking rms and their agents (iii) regulatory incentives and (iv) the interaction of these dynamic eects.

5.6.1 Related Literature

Dewatripont and Tirole(1993) and Freixas and Rochet(1997) provide a comprehensive discussion of the seminal papers in banking regulation. In particular, there exists a burgeoning literature on models of bank contagion, with the most notable ones being Freixas and Rochet(2008), Kiyotaki and Moore(1997), Freixas and Parigi(1998), and F. Allen and Gale(2000a). e standard approach to the design of bank regulation proposed in the dominant theoretical frameworks considers a “representative” bank and its response to particular regulatory measures, such as taxes, bankruptcy rules or capital requirements. In this seing, banks are generally viewed as playing a strategic Nash game in responding to economic and regulatory events. ese partial equilibrium approaches reect the status quo of bank-based intermediation under dierent institutional parameters. As such, their aim is not to consider alternative designs of credit systems with maturity-matched credit allocated through nancial markets. However, the framework developed within this chapter aims to do exactly this. In particular, by building a deterministic network-based credit stress model to assess the comparative advantages of bank-based and market-based credit systems respectively. is chapter builds both upon F. Allen and Gale(2000a), who develop a one- period model of risk-shiing, and Nier, Yang, Yorulmazer, and Alentorn(2007), who apply network theory to construct a banking system and then analyse the resilience of the system to contagious defaults.

5.6.2 Construction of the nancial system

e nancial system is constructed as a network of nodes, where each node represents an economic agent and each link represents a directional credit or equity relationship between two nodes:

• Nodes: Nodes can represent banks B, borrowers L, depositors/creditors D and shareholders S, whereby the capital leers denote the number of nodes of the respective entity. e total number of surplus and decit agents, excluding banks, is denoted as A = L + D + S. While the model treats banks economically as a single node, bank nodes are visually represented as four distinct nodes in the network graphs to dierentiate between the asset, liability and equity portion of the balance sheet.

• Edges: Each edge e represents a directional credit or equity relationship between two nodes. Credit relationships can exist (i) between banks B and borrowers L, (ii) between depositors D and banks B, and (iii) between borrowers L and depositors D. Equity relationships can exist only between shareholders S and banks B.

e realized nancial network graph G is thus based on the following exogenous parameters that describe the number of the respective nodes and edges in the graph: G(B, L, D, S, e). e model delivers realizations of this graph that can be represented by this set of structural parameters. Except for the banking rms, each agent can at any given point be classied either as a surplus or decit agent, meaning the agent can either have a positive asset balance or be a

223 negative one (i.e. being indebted). For any realization of the nancial network, the individual agent’s surplus and decit accounts are populated in a manner consistent with their bank-level or aggregate wealth-level identities. In a no default scenario, decit agents make periodic interest payments and repayments of the principal at the maturity of the loan. e periodic interest payments are used to pay for the operation of the banking rm (in the bank-based allocation) or the operation of credit originators (in the market-based allocation) respectively.

5.6.2.1 Bank-based system

Where a loan is originated in the banking system, two kinds of edges e are created, one between banks and borrowers E(B,L) and one between depositors and banks E(D,B). Modeling a bank-based nancial system requires the construction of a bank’s balance sheet according to the established accounting logic.66 e asset side of an individual bank’s balance sheet, denoted by a, is made up exclusively of bank loans, denoted by l. e bank loans lij, are loans between bank i and borrower j, where i = 1, ..., B and j = 1, ..., L. us, for bank i, the asset side of the balance sheet can be represented as ai = li, where i = 1, ..., B. A bank’s liabilities, denoted by l, are composed of the bank’s equity, denoted by e, and deposits, denoted by d. e bank’s equity eij, are shareholdings between shareholder i and bank j, where i = 1, ..., S and j = 1, ..., B. e deposits dij, are deposits held by depositor i in bank j, where i = 1, ..., D and j = 1, ..., B. Hence for bank i, we can denote the liability side of the balance sheet as li = ei + di, where i = 1, ..., B. In the bank-based system, credit is originated through the bank. e bank incurs operating costs o, which are paid for by borrower’s periodic interest payments. A fraction of the periodic interest payments are further periodically passed on to depositors. e remainder goes to the bank’s shareholders.

5.6.2.2 Market-based credit systems

Where a loan is originated in the credit market system, only one kind of edge e is created, namely the one between the borrower and the creditors E(L, D). Credit market loans, denoted by fij, are direct loans between borrowers i and creditors j, where i = 1, ..., L and j = 1, ..., D.

• Disintermediated nancial system: Within the model, a disintermediated nancial system refers to a nancial system where every borrower exclusively receives funding from one creditor and every creditor funds only one borrower. In other words, every loan is underwrien by exactly one creditor. A total of 1,…,L edges between the borrower and the depositor(s) are created E(L, D). As a result, there is no diversication and every creditor in a disintermediated credit system is fully exposed to the idiosyncratic risk of one nanced borrower.

• Distributed nancial system: On the other hand, within the model, a distributed nancial system refers to a nancial system where every borrower exclusively receives funding from every creditor. In other words, every loan is syndicated on an equal pro rata basis to the entire pool of creditors in the market. us, this network can be represented by a complete bipartite graph and a total of 1,…,L ∗ D edges between the borrower and the depositor(s) are created E(D,L). As a result, there is full diversication and every creditor has exposure to the idiosyncratic risk of every borrower.

In the market-based system, credit is originated through credit originators. One can think of these as representing non-bank lenders, investment banks or government-sponsored enterprises (GSEs), which all engage in the originate-to- distribute model at dierent levels of market-based credit nancing. Within the model, these credit market originators are modeled as fully equity-nanced entities, which charge a loan origination fee o equivalent in size to the operating costs under the bank-based allocation structure. e origination fees are paid for by the periodic interest payments of the borrowers, while any excess returns fully accrete to market creditors.

66See Kobayashi(2014) (‘Basically, nancial network models require researchers to construct bank balance sheets.’).

224 5.6.2.3 Disintermedation level

e total amount of nanced external credit assets of the nancial system is denoted by C. ese credit assets represent the total loans made out to ultimate borrowers and thus relate to the total size of the ow of funds from savers to borrowers, either directly through the credit markets or through the banking system. e aggregate amount of assets nanced directly through the credit market is denoted as F , while the amount of credit assets held on bank balance sheets is denoted as T . us, the level of disintermediation can be denoted as δ = F/C, representing the percentage of credit assets nanced through direct lending between agents. Note that the aggregate assets of the whole nancial system can be wrien as G = F + T . Within the scope of this chapter, only nancial systems, which are either strictly bank-based systems, δ = 0, or strictly market-based systems, δ = 1, are considered.

5.6.3 Economic shocks

e model looks at the economic endowments of surplus and decit agents over time. In a no-default scenario, borrowers

L make periodic interest payments i in time period one t1, time period two t2 and repayment of principal p at the end of the loan’s maturity.

5.6.3.1 Negative shocks: credit defaults

To test the resilience of the nancial system, one or multiple loans li made to borrower(s) are hit with an idiosyncratic credit shock. Let si be the size of the initial credit shock, such that the size of the shock can be denoted by:

i X si = li (5.1) i=1 e eect of the credit shock is that the borrower defaults on his interest and principal repayment. is credit default is modeled by seing the defaulting borrower’s i and p to zero. We then observe how the credit market or the banking rm absorbs this credit shock and how this can be related to the structural parameters of the observed credit system. us, the focus lies exclusively on credit shocks to the bank’s external assets or credit defaults of loans originated to the market respectively. While for credit risk in particular, external credit shocks can trigger liquidity shocks on the liability side of a bank’s balance sheet or trigger correlated credit shocks in the market that may also be relevant in practice, the model reduces these complexities to the idiosyncratic shocks as a clean starting point for studying the wealth eects.

5.6.3.2 Positive shocks: no credit default scenario

While the purpose of stress tests is mostly to identify the eects in negative shock scenarios, in particular to identify spillover eects and loss contagion channels, this chapter also models the ‘up scenario’ for every realisation of a nancial system. is allows us to identify economic eects of a given nancial architecture that may only arise in benign economic scenarios, such as the distribution of economic rents during stable times and associated risk-taking incentives. Such a ‘positive shock’ is modeled by assuming that none of the borrowers default on either their interest payments i or their principal repayments p.

5.6.4 Loss absorption mechanisms

Rosas(2006) distinguishes between two extreme forms of government response to credit crunches, which he refers to as ‘Bagehot’ and ‘Bailout’ respectively.67 Under the ‘Bagehot rule’,68 governments uphold the market mechanism by providing liquidity to solvent banks and withholding nancial support to insolvent banks, i.e. allowing them to go into

67See Rosas(2006) (‘Instead, it is appropriate to consider bailouts as ranging in the abstract from absolutely no government help to complete government absorption of all losses. […] I refer in this section to two extreme forms of government response: Bagehot and Bailout.’). 68Going back to Sir Walter Bagehot, who has pioneered the systematization of last resort lending more than a century ago. See Bagehot(1873).

225 bankruptcy. us, the ‘Bagehot’ government response minimizes public outlays, moral hazard and avoids the socialization of losses. e private loss absorption mechanism presented in the context of our model below can be understood to be on the ‘Bagehot’ end of the government response spectrum. However, as we do not model any response by third-party lenders aer a credit shock, our private loss mechanism is more reductionist in that it assumes that banks and markets are le completely on their own, without any interaction with the sovereign state or private lenders. us, neither wholesale (interbank) funding, nor market-based government loans form part of our private loss absorption cascade.

5.6.4.1 Private loss absorption mechanism

We refer to private loss absorption mechanisms as the credit loss transmission process taking eect in the absence of any government intervention. In particular, in the absence of bank bailouts, private loss absorption mechanisms take place under ordinary bankruptcy proceedings, such as the U.S. Chapter 11 bankruptcy proceedings.69 ese private bankruptcy proceedings set out loss absorption or load balancing mechanisms through a denition of creditor priorities in the event of borrower or bank defaults.70

5.6.4.1.1 Bank-based systems

Within the model, banks are shocked by wiping out a certain percentage of their external credit assets. Let si be the size of the initial credit shock. Under the private loss absorption mechanism, this loss is rst absorbed by the bank’s equity 71 holders ei and then by its depositors di. If the bank’s equity buer is not sucient to absorb the initial shock, si > ei, then the bank defaults and the residual is transmied to depositors. If this happens, all depositors receive an equal share of the residual shock [(si − ei)/B]. Depositors, as surplus agents, will absorb this residual shock through their positive net worth. us, in the absence of state intervention through deposit insurance, depositors are the ultimate ‘sink’ for losses. Furthermore, a loss cascade in bankruptcy proceedings is assumed, which grants priority to customer deposits over shareholders.72 Although the present model assumes one class of bank creditors, an extension of the model could provide for two classes of bank creditors, with depositors taking priority over other creditors.73

5.6.4.1.2 Market-based systems

Private loss absorption under the market-based system is straightforward, as there is no loss cascade between dierent institutions and stakeholders. Under the disintermediated market system, where every borrower is nanced exactly by one creditor, every defaulting borrower imposes a credit loss on exactly one creditor in the full amount of the default. Under the distributed market system, every borrower is nanced in equal shares by every creditor. us, the partial credit loss p is spread in equal parts over all creditors D:

i P si = li p = i=1 (5.2) D

5.6.4.2 Public loss absorption mechanism

A core contribution of this model lies in the explicit inclusion of public loss absorption mechanisms, which allow us to compare and contrast bank-based systems with market-based systems not just under ordinary bankruptcy proceedings,

6911 U.S.C., also known as the ‘United States Bankruptcy Code’. 70For example, 11 U.S.C. § 507 sets out creditor priorities for creditors and claimants of U.S. corporations in bankruptcy. 71Given that our model does not incorporate interbank credit and any creditors other than depositors, this is in line with the shock transmission assumptions of Nier et al.(2007). 72Priority of creditors over shareholders is typically part of ordinary bankruptcy proceedings, such as 11 U.S.C. § 507 for the U.S. context. 73In most jurisdictions, depositor preference over other creditors is governed by explicit banking regulations. In the US, for example, bank depositor preference was introduced at the federal level in 1993. Notably, this introduction was mainly for the benet of the FDIC, which acquires all the claims against the bank including the priority status of deposits in the event of a bank failure. us, depositor preference was introduced mainly for the benet of public loss absorption mechanisms further outlined below. See Osterberg(1996) (‘On August 10, 1993, Congress passed the Omnibus Budget Reconciliation Act. is legislation contained an amendment to section 11(d)(11) of the Federal Deposit Insurance Corporation Act that changed the priority of claims on failed depository institutions. It gave depositors, and by implication the FDIC, claims on a failed bank’s assets that are superior to those of general creditors.’).

226 but also in scenarios where the sovereign state intervenes. Public loss absorption mechanisms can either result from explicit government guarantees, such as those encountered in deposit insurance schemes, or from implicit government guarantees. In practice, public loss absorption mechanisms can take many forms, ranging from the extension of government guarantees, to capital injections, to outright purchases of defaulting assets. Public loss absorption can pertain to both bank-based systems, where banks are bailed out, and market-based systems, where market-based assets are stabilized by the state. In the model, public loss absorption is modeled by spreading the sum of credit losses equally over the total number of surplus and decit agents, excluding banks, denoted as A = L + D + S.

i P si = li p = i=1 (5.3) A As public expenses have to be paid for by future tax revenues, the rational behind this allocation is that every economic agent is equally aected by the rise of future taxes. us, even decit agents, who are already ‘in the red’, are paying for a bailout. While such an equal spreading is somewhat reductionist, it allows us to model the wealth eects of government intervention and to compare and contrast such interventions in the context of dierent system architectures.

5.7 Simulation Parameters

e realized nancial network graph G is based on the following exogenous parameters that describe the number of the respective nodes and edges in the graph: G(B, L, D, S, e). e model delivers realizations of this graph that can be represented by this set of structural parameters. Recall that all constructed nancial systems can be described by a network graph G with the following set of structural parameters G(B, L, D, S, e). In this section, the simulation experiments are described by which the robustness and resilience of the dierent systems are studied for these key parameters. e below table summarises the values of the benchmark parameters of the model.

Summary of benchmark parameters of the model Parameter Description Value L Number of debtors 10 B Number of banks 1 D Number of depositors (bank-based system) 9 C Number of creditors (market-based system) 10 S Number of shareholders 2 s Credit shocks 10-40 i Interest rate paid by debtors 20% n d Interest rate paid on bank deposits 20% of P i i=1 o Operating Expenditure (OpEx)/ Origination fee 8

roughout the simulations, the number of debtors (L) are xed at 10 with a principal loan amount per borrower of 10 monetary units. In other words, the total credit supply to the real economy is kept constant and the focus is instead put on the variation in the intermediation architecture. Furthermore, the analyzed bank-based system consists of only a single bank (B = 1). In contrast to this reductionist approach, the network-based literature on nancial contagion in bank-based economies typically looks at larger networks (B > 1) of interconnected banks and contagion channels related to interbank credit exposure.74 e simplied

74See Nier et al.(2007) (‘In studying these questions we focus on the role of direct interbank connections as a source of systemic risk and study the potential for knock-on defaults that are created by such exposures.’); Gai and Kapadia(2010a) (‘is paper models two key channels of contagion

227 economy studied here can be thought of as a basic ‘building block’, which could theoretically be extended to an interconnected banking system. However, given that the focus of this study lies on a comparison of competing nancial architectures, modeling interbank loss contagion would distract from the core institutional mechanism that is already observable when the single institution is the main unit of analysis. For simulations with bank-based economies, the aggregate number of depositors (D) is set by default to 9 depositors, holding a principal amount of 10 monetary units in bank deposits. us, the remainder of the bank’s balance sheet is nanced by equity, represented by 2 shareholders holding 5 monetary units each. erefore, the bank’s equity represents 10% of the bank’s total assets. is is in line with the proposal of Calomiris(2013), 75 but lower than the 25% proposed by Admati and Hellwig(2013) and higher than common regulatory capital charges of 8% 76 and assumptions of 4-5% made in other nancial contagion models.77 On the other hand, under the market-based credit system, the total number of creditors (C) is assumed to directly match debtors (L) with the same notional loan amount of 10 monetary units. In the market-based regime, the two shareholders hold an equity stake in a market-enabling rm that facilitates the origination of credit securities without taking them on the balance sheet. As discussed in chapter 4 of this thesis, the origination of credit securities typically involves a transitory bank-like allocation whereby the investment bank takes proprietary credit exposure. In contrast, the stylized market-based regime presented here assumes that all credit securities are originated through ‘best-eort’ underwritings78 and without proprietary positions taken on the underwriter’s balance sheet. In every time period, each debtor pays 2 monetary units interest (i) on the bank loan or credit security, respectively. is represents a gross yield of 20% on the principal amount. If we assume an annual time period, this would be signicantly higher than average interest rates in the current low interest rate environment.79 While the results would also hold in a simulation with lower interest rates, a high interest rate environment has been chosen here to numerically highlight the eects more clearly. Of the total interest payments made in each period, 20 percent is paid in bank deposit interest (d). us, if all bank loans remain solvent, a total interest income of 20 monetary units is realized and total interest expenses of 4 monetary units are incurred. is corresponds to an ‘interest expense margin’80 or ‘funding costs’81 of 4.4%,82, which is slightly higher than what has been reported empirically.83 Furthermore, it is assumed that the operational costs (o) are xed at 8 monetary units per period. ese operational in nancial systems by which default may spread from one institution to another. e primary focus is on how losses can potentially spread via the complex network of direct counterparty exposures following an initial default.’); Kobayashi(2014) (‘In this leer, I show the equivalence between a model of nancial contagion and the widely-used threshold model of global cascades proposed by Was (2002). Basically, nancial network models require researchers to construct bank balance sheets. e inuence of a bank failure is then examined by sequentially undermining the interbank assets of the lenders. Second-round defaults occur if the number of defaulted borrowers among total borrowers exceeds a certain threshold.’); Gai, Haldane, and Kapadia(2011) (‘In broad terms, our model explores the resilience of the nancial system to liquidity shocks aecting a subset of banks under dierent network congurations, degrees of connectivity between nancial institutions, haircut assumptions, and balance sheet characteristics. We start by describing how the network of interbank exposures and balance sheets are constructed, before discussing how shocks to haircuts which trigger liquidity hoarding at some institutions may potentially propagate across the system.’); Lenzu and Tedeschi(2012) (‘In this paper we develop an interbank market with heterogeneous nancial institutions that enter into lending agreements on dierent network structures.’). 75See Calomiris(2013) (‘My approach to reform would raise required equity to roughly 10% of assets, and would also ensure that banks maintain that ratio in actual equity (not just book equity). My approach would also involve providing banks with strong incentives to limit their risks so that a 10% equity ratio would be adequate.’). 76See E. Jones and Zeitz(2017) (‘Under Basel I and II, banks had to hold a minimum of 8 per cent of RWA, and this remains unchanged under Basel III.’). 77See Gai and Kapadia(2010a) (‘Banks’ capital buers are set at 4 per cent of total (non-risk-weighted) assets, a gure calibrated from data contained in the 2005 published accounts of a range of large, international nancial institutions.’); Gai et al.(2011) (assuming a capital buer K of 4% of balance sheet assets); Nier et al.(2007) (assuming a default ‘net worth’ to total assets of 5%, but varying it from 0% up to 10%). 78See Sherman(1992) (describing ‘best-eort’ underwriting in the context of equity oerings ‘ere are two types of oerings for new issues in the U.S., rm commitment and best eorts. For both types, the issuer must set in advance both the price per share and the maximum number of shares to be sold. With rm commitment oerings, the underwriter guarantees the proceeds by buying all of the shares at a given price and then aempting to resell them at the oering price. With best eorts oerings, the underwriter agrees only to put its ”best eorts” into selling the issue’). 79See Del Negro, Giannone, Giannoni, and Tambaloi(2019) (on the low interest rate environment ‘Ten years aer the most acute phase of the nancial crisis, the world economy remains mired in a low-interest-rate environment. At the time of this writing, the nominal yields on ten-year government bonds are below 3% in the United States, a bit above 1% in the U.K., around 40 basis points in Germany, and essentially zero in Japan.’). 80See Entrop et al.(2015) (e interest expense margin ‘captures interest expenses to total interest-paying liabilities, which consist of interbank and non-bank funding, deposit accounts and securities issued.’). 81See Dietrich and Wanzenried(2011) (‘Funding costs are dened as interest expenses over average total deposits and are mainly determined by a bank’s credit rating, competition, market interest rates, and by the composition of the sources of funds and its relative importance.’). 82Calculated by dividing the total interest income of 4 monetary units by total deposits of 90 monetary units. 83See Entrop et al.(2015) (reporting an average interest expense margin of 2.85% for a sample encompassing the German universal banking sector between 2000 and 2009); Dietrich and Wanzenried(2011) (reporting average funding costs of 2.3% for a sample of 372 commercial banks in Switzerland over the period from 1999 to 2009).

228 costs can either represent operating expenditures (OpEx) in the bank-based system or origination fees in the market- based system. ese operating costs are held constant across all scenarios, irrespective of the direction or size of the credit shocks. For bank-based systems, Maudos, Pastor, Perez,´ and esada(2002) have reported total-cost-to-asset (TC/A) ratios84 in the range between 5.64% to 8.94%, with an average of 6.69% for a sample of ten countries of the European Union in the period between 1993–1996. Assuming OpEx of 8 monetary units and interest expenses of 4 monetary units, the TC/A ratio of our bank-based economy can be calculated as 12% and would thus appear to be higher than what has been observed empirically. On the other hand, the cost-income ratio, calculated as operating expenses divided by net interest income, would come in at 50%, which is slightly lower than what has been observed empirically.85 In the stress test scenarios, the nancial system is perturbed with a credit shock that varies in magnitude, ranging from one or two defaulting debtors, for small-scale credit shocks, up to four debtors for large-scale shocks. In particular, for bank-based systems, dierent credit shock scenarios can help us to identify institutional resilience levels and breaking points in the capital structure. Whereas a large stream of network-based nancial stress test literature has investigated liquidity shocks, either in isolation86 or in combination with credit shocks,87 this study focuses exclusively on exogenous credit shocks. While the choice of parameters used for simulation purposes is always somewhat arbitrary in nature, the aim was to set parameters largely in line with either empirical results or with theoretical model assumptions. Given the reductionist nature of the model, which does not reect many of the complexities of real-world nancial networks, including interbank interactions and liquidity shocks, this discrete choice of parameters can be considered a further limitation of the model outcomes.

5.8 Results

5.8.1 Banking-based systems

5.8.1.1 Positive shock: economic upswing scenario

e rst scenario considered is that of an economic upswing under the bank-based allocation. As outlined in the model section, this ‘positive shock’ is characterized by the absence of creditor defaults. In other words, everything ‘works as it should’ without major disruptions in the credit system. In the one bank economy model, this means that all ten borrowers remain completely solvent and pay interest in full on their loans. As outlined above, the modeled banking economy consists of a total of 10 debtors, each one borrowing 10 monetary units, 9 depositors, each one depositing 10 monetary units with the bank, and 2 shareholders, with shares valued at 5 monetary units each. With i set at 20%, every borrower pays 2 monetary units in interest. Of this, a portion d = 20% or 4 monetary units is paid on the demand deposits. us, for every one of the 9 depositors, a periodic interest of 0.44 monetary units is paid out. is increases the wealth of depositors between period t0 and t1 from 10 to 10.44 monetary units, and from 10.44 to 10.88 monetary 88 units between t1 and t2. Furthermore, it is assumed that the bank incurs an operating expenditure (OpEx) of o = 8 monetary units.89 us, if we look at the overall prot & loss statement of the bank, aer interest expenditure and

84With total costs including both nancial and operating costs. 85See Dietrich and Wanzenried(2011) (reporting an average cost-income ratio of c. 65% for a sample of 372 commercial banks in Switzerland over the period from 1999 to 2009). 86See Gai et al.(2011) (‘Suppose we randomly perturb the network by assuming that a single bank suers a haircut or idiosyncratic liquidity shock which is suciently large to causeit to start hoarding liquidity.’); Lenzu and Tedeschi(2012) (‘To perturb the system, we generate a random aack via a liquidity shock. e hit bank is not automatically eliminated, but its failure is endogenously driven by its incapacity to raise liquidity in the interbank network.’). 87See Gai and Kapadia(2010b) (considering both credit and liquidity shocks ‘e primary focus is on how losses can potentially spread via the complex network of direct counterparty exposures following an initial default. […] We now incorporate liquidity eects into our analysis. When a bank fails, nancial markets may have a limited capacity to absorb the illiquid external assets that are sold. As a result, the asset price may be depressed.’); Nier et al.(2007) (rst considering credit shocks in isolation and then extending the model to incorporate liquidity eects). 88is assumes that the deposit interest is periodically disbursed in full and not re-allocated in the banking rm. e laer would make the second period interest marginally higher. 89is operating expenditure of 8 monetary units is held constant across all scenarios, including the market-based scenarios.

229 OpEx, there remains a net prot of 8 monetary units. e net prot of 8 monetary units is disbursed aer each period to the bank’s shareholders. In the model, it is assumed that the bank has 2 shareholders. us, aer period t1, each shareholder receives a dividend of 4 monetary units. is increases each shareholder’s wealth between period t0 and t1 from 5 monetary units to 9 monetary units and between period t1 and t2 from 9 monetary units to 13 monetary units. Starting with this simplistic model, we can clearly see that shareholders capture the bulk of the value in an economic boom scenario. is asymmetric value capture is a well-known property of the banking rm allocation or, more generally, any debt-nanced rm structure. e asymmetry in value capture can induce the shareholder-led rm to increase the size and risks of the balance sheet. M. C. Jensen and Meckling(1976) have described this as ‘agency costs of debt’, whereby the shareholder-led rm is naturally inclined to substitute low-risk assets for higher risk assets.90 By substituting low-risk assets for high-risk assets, so-called ‘asset substitution’, shareholders are able to capture more value on the upside.91 In substance, this simple model also reects a structural Merton model of bank capital and debt (R. Merton, 1974). ereby, the shareholder’s equity has the payo structure of a long position in a call option on the bank’s assets. Shareholders can only lose the nominal value of their shares, but they can gain asymmetrically more in an upside scenario. us, they have a tendency to favor high-volatility projects and more highly levered bank balance sheets. On the other hand, the creditor’s payo structure is that of a short position in a put option on the bank’s assets. In the best case, they only receive a xed fee for underwriting the option, but they can loose substantially more in the downside scenario. Overall, the results of this positive shock scenario inform us about the banking system in ‘calm times’ and about the incentive structures that drive rm-level asset and risk allocation. e benign economy scenario can serve as a baseline for both bank-based systems under stress and for market-based systems, which provide for dierent incentive mechanisms.

Bank-based Economy: Economic Upswing Event No Default No Default No Default

Period t0 t1 t2 Bank income statement Interest Earned 20 20 Interest Paid -4 -4 OpEx -8 -8 P&L 8 8 Asset/Liability Levels Depositor 10 10.44 10.88 Borrower -10 -10 -10 Shareholder 5 9 13

90See M. C. Jensen and Meckling(1976) (‘e agency costs associated with the existence of debt […], are composed mainly of value reductions in the rm and monitoring costs caused by the manager’s incentive to reallocate wealth from the bondholders to himself by increasing the value of his equity claim’). 91See Green and Talmor(1986) (‘In this paper we examine explicitly the incentive for asset substitution by solving endogenously for the optimal risk policy. e results support the notion that more debt aggravates shareholders’ incentives to take risk.’).

230 Borrowers Borrowers Borrowers Depositors Depositors Depositors Bank Bank Bank Shareholders Shareholders Shareholders

(a) No Default t0 (b) No Default t1 (c) No Default t2

Figure 5.1: Bank-based Economy: Economic Upswing Simulation

231 5.8.1.2 Negative shock with private loss absorption

e rst negative shock scenario we look at under the stress test model is the private loss absorption mechanism. In other words, the loss absorption mechanism under ordinary bankruptcy proceedings, absent any government guarantees and state intervention. e base case at t0 looks like under the positive shock scenario above, with the same number of borrowers and depositors and the same asset allocations. We can then consider credit shocks at t1 with variable magnitudes:

• Minor shock scenario: Under the ‘minor shock scenario’, a negative exogenous credit shock is induced to a

single borrower in t1, which wipes out the loan of that borrower. As a result, the borrower defaults on both principal and interest payments in full and the borrower’s net wealth goes from negative 10 to 0 monetary units. is is in line with a theoretical view of the bankruptcy protection mechanisms as a way for individual borrowers to restructure their personal balance sheets.92 e credit shock negatively aects both the bank’s balance sheet and income statement. us, the aggregate interest income in the rst period is reduced by 2 monetary units to 18 monetary units. Of this, a portion d = 20% or 3.6 monetary units (instead of previously 4) is paid on the demand deposits. us, every depositor receives a periodic interest of 0.4 monetary units (instead of previously 0.44). is

increases the wealth of depositors between period t0 and t1 from 10 to 10.4 monetary units. In the bank’s prot & loss statement, in addition to interest expenditures d and OpEx o, there is now a position related to the complete write-down of the defaulted loan of 10 monetary units, resulting in a net loss of 3.6 monetary units. is net loss is small enough for the shareholders to be able to absorb it. e loss is spread equally among the two shareholders, resulting in a reduced shareholder value of 3.2 monetary units aer the loss aribution of 1.8 monetary units. In summary, under the ‘minor shock scenario’, the bank remains solvent and losses are eectively absorbed by shareholder’s equity. e ability of bank capital to eectively absorb credit losses in the manner modeled in this ‘minor shock scenario’ is at the root of the proposals following the global nancial crisis to increase the capital buer from 8% under the Basel framework.93 Admati and Hellwig(2013) have proposed a signicantly higher equity ratio of 25%, which they based on the historical equity-to-asset ratios maintained by banks prior to the introduction of public safety nets (legal diversication mechanisms). Calomiris(2013) has questioned the viability of these historical ratios in the modern banking system and has proposed to increase the equity ratio to merely 10% instead. e optimal size of the capital buer crucially depends on the ex-ante estimation of the size of the credit shock. As the major shock scenario below demonstrates, if the credit shock is large enough, even a conservative 25% capital buer can turn out to be insucient.

• Major shock scenario: Under the ‘major shock scenario’, a negative exogenous credit shock is induced to 4 94 borrowers in t1, which wipes out the loans of these borrowers. As a result, the borrowers default on both principal and interest payments in full and their net wealth goes from negative 10 monetary units to 0 monetary units as a result. Again, the shock negatively aects both the bank’s balance sheet and income statement. e overall interest income in the rst period is reduced by 8 monetary units to 12 monetary units. Of this, a portion d = 20% or 2.4 monetary units (instead of previously 3.6) is again paid on the demand deposits. us, every depositor receives a periodic interest of 0.26 monetary units (instead of previously 0.4). is increases the wealth

of depositors between period t0 and t1 from 10 to 10.26 monetary units. However, this is before the loss aribution from the write-down of the loans. If we look at the overall prot & loss statement of the bank, in addition to interest expenditures d and OpEx o, there is now a write-down of loans by 40 monetary units. In total, this leaves the bank with a residual net loss of 38.4 monetary units. is loss is now too large for the shareholders to be able to absorb it in full. e net loss rst wipes out the two shareholders, resulting in a shareholder value of 0 monetary units

92See Dobbie, Goldsmith-Pinkham, and Yang(2017) (‘In theory, bankruptcy protection benets debtors directly by improving their balance sheets and preventing the seizure of important assets such as a home or car. ese direct benets may in turn indirectly benet debtors by increasing their credit score or access to credit.’). 93See E. Jones and Zeitz(2017) (‘Under Basel I and II, banks had to hold a minimum of 8 per cent of RWA, and this remains unchanged under Basel III.’). 94 is t1 is not a consequential period to the ‘minor shock scenario’ presented above, but an alternative t1.

232 each. ereaer, a net loss of 28.4 monetary units remains. is residual loss is spread across the nine depositors on a pro rata basis. is results in a loss aribution of 3.15 monetary units for each depositor, leading to a net wealth of depositors, aer interest payments and losses, of 7.11 monetary units. In summary, under the ‘major shock scenario’, the bank becomes insolvent and losses are absorbed according to the loss cascade of ordinary bankruptcy proceedings. In this ‘private load balancing mechanism’, depositors are senior to equity holders. us, only aer equity holders have absorbed the ‘rst frontier of the credit shock’, will losses be absorbed by depositors. e credit structure modeled here is rather simplistic, with only one class of unsecured bank creditors. In practice, there typically exist multiple classes of creditors with dierent credit collateralization, seniority levels or pari passu provisions.

Overall, the results of this negative shock scenario inform us about the way in which the banking system should, in theory, react to credit distress scenarios under ordinary private bankruptcy proceedings. In particular, minor credit shocks can be eectively absorbed by the bank’s equity cushion. In contrast, in the absence of public safety nets, major shocks lead to a bank’s insolvency, which leaves depositors with a pro rata share on the bank’s net assets aer credit losses.

Bank-based Economy: Private Loss Absorption Event No Default Minor shock Major shock

Period t0 t1 t1 Bank income statement Interest Earned 18 12 Interest Paid -3.6 -2.4 OpEx -8 -8 Write-down loans -10 -40 P&L -3.6 -38.4 Asset/Liability Levels Depositor 10 10.4 7.11 Borrower -10 -10 -10 Borrower (default) -10 0 0 Shareholder 5 3.2 0

Borrowers Borrowers Borrowers Depositors Depositors Depositors Bank Bank Bank Shareholders Shareholders Shareholders

(a) Base Case t0 (b) Minor Shock t1 (c) Major Shock t2

Figure 5.2: Bank-based Economy: Private Loss Absorption Simulation

233 5.8.1.3 Negative shock with public loss absorption

e second negative shock scenario analyzed relates to the ‘public loss absorption mechanism’. In other words, a loss absorption mechanism is introduced in which government guarantees and state intervention form part of the model assumptions. e base case at t0 looks like under the positive shock scenario above, with the same number of borrowers and depositors and the same asset allocations. e simulation starts with the ‘major shock scenario’ from the private loss absorption mechanism, where a negative exogenous credit shock was induced to 4 borrowers in period t1. It is assumed that the economics from the ‘major shock scenario’ stay the same for one ‘logical crisis second’, during which the banking rm, bank regulators and public ocials can anticipate the economic repercussions of the credit shock and react with a public bailout measure in t2. During this ‘logical crisis second’, the exogenous credit shock is expected to rst wipe out the shareholders in full, forcing the bank into insolvency and leaving the nancial system with a residual loss that would have to be absorbed on a pro rata basis by the bank’s depositors. However, a logical second before the private load balancing mechanism takes eect in t1, it is now assumed that the government steps in at t2 and bails out the bank. In the simulation, the bank bailout is modeled by spreading the sum of the credit losses equally over the total number of surplus and decit agents. In other words, the public loss absorption mechanism ignores the private loss cascade, as dened by local bankruptcy laws. While bankruptcy laws commonly allocate losses among dierent classes of surplus agents (equity holders, creditors, depositors), the public loss absorption mechanism assumes that the bailout is nanced entirely by future taxes, which aect all agents equally. In other words, the total net loss of 38.4 monetary units is spread across all ten borrowers, all nine depositors and the two shareholders, resulting in a net loss per agent of 1.82 monetary units. Under the public loss absorption model, the post-bailout wealth allocation reveals a number of interesting ndings. A rst look at the surplus agents in the bank economy, namely depositors and shareholders, reveals key dierences compared to the private loss absorption mechanism (Bagehot scenario). Aer deducting the net loss per agent of 1.82 monetary units from depositor’s wealth aer interest income (10.26 monetary units), a post-bailout depositor wealth level of 8.43 monetary units results. us, compared to the depositor wealth of 7.11 in the ‘major shock scenario’ of the private loss absorption simulation, depositors are economically made beer o by a total of 1.32 monetary units under the bailout regime. Similarly, the bailout benets shareholders as they are no longer wiped out in full. Instead, shareholders now only absorb their pro rata loss share of 1.82 monetary units and are thus le with a net wealth of 3.17 monetary units aer the bailout. us, the bailout has an overall positive economic eect on surplus agents, with shareholders beneting the most (3.17 monetary units per agent). As bailouts are essentially a wealth transfer mechanism orchestrated by the sovereign, the situation is reversed when decit agents are considered. e bailout aects both defaulting and non-defaulting borrowers adversely. Firstly, the non-defaulting borrowers, which initially owe a principal of 10 monetary units, are adversely aected by the bailout as they also have to absorb the net loss of 1.82 monetary units through future taxes. As a result, despite not defaulting on their loans, they are further indebted and end up with a negative wealth level of 11.82 monetary units aer the government bailout. e defaulting borrowers on the other hand rst have a wealth level of 0 monetary units aer defaulting on their loans, as they go through the bankruptcy process. However, they also absorb net losses of 1.82 monetary units through future taxes and thus have a negative wealth of 1.82 monetary units aer the bailout. us, compared to the private loss absorption mechanism, the bailout has an adverse economic eect on both types of decit agents. Overall, the results of this bailout scenario inform us about the way in which government bailouts aect agents in a closed bank economy. Firstly, a government bailout aects every node in the network. In the model, the bailout has net positive eects on surplus agents and net negative eects on decit agents. e surplus agents beneting the most are shareholders, as the bailout completely shields them from being the ‘rst line of defense’ in an economic stress scenario. In a second instance, depositor losses are reduced as aggregate losses are now partially absorbed by decit agents. For decit agents, the bailout has negative economic consequences, making them worse o compared to the private loss absorption mechanism. us, in eect, the model dictates that state intervention can be viewed primarily as bailouts of

234 shareholders and bank depositors. At a high level of abstraction, they can be viewed as a wealth transfer from decit agents to surplus agents. From a macro-economic perspective, this appears to be sub-optimal as it negatively aects those economic agents, which are already indebted.

Bank-based Economy: Public Loss Absorption Event No Default Major shock Bailout

Period t0 t1 t2 Bank income statement Interest Earned 12 Interest Paid -2.4 OpEx -8 Write-down loans -40 P&L -38.4 Total loss -38.4 Total agents 21 Loss per agents -1.82 Asset/Liability Levels Depositor 10 7.11 8.43 Borrower -10 -10 -11.82 Borrower (default) -10 0 -1.82 Shareholder 5 0 3.17

Borrowers Borrowers Borrowers Depositors Depositors Depositors Bank Bank Bank Shareholders Shareholders Shareholders

(a) Base Case t0 (b) Major Shock t1 (c) Bailout t2

Figure 5.3: Bank-based Economy: Bailout Simulation

235 5.8.1.4 Capital Adequacy Regulation

Within the regulatory toolbox of bank regulators, capital buers are the primary regulatory tool.95 Recent regulatory reforms have further stressed the need to increase this equity buer pro-cyclically.96 To model regulatory capital adequacy measures in this simulation, the initial capital buer is increased and it is observed how this increase aects bank resilience under dierent stress scenarios.

Under the capital adequacy simulation, the bank’s capital buer in the base case at t0 is doubled from a total of 10 monetary units, held by 2 shareholders, to a total of 20 monetary units, held by 4 shareholders. In very simplied terms, not considering the heterogeneity of credit assets and asset-specic regulatory risk weights, this increase in capital buer reects the key regulatory response aer the global crisis of 2008.97 To make the balance sheet accounting work, it is assumed that the additional equity buer substitutes one depositor in the capital structure. e modeled banking economy now consists of a total of 10 borrowers, 8 depositors (instead of previously nine) and 4 shareholders, with shares valued at 5 monetary units each. For the purpose of this simulation, a private loss absorption mechanism is the used as the loss distribution kernel to identify the resilience of the bank without any state intervention. e simulation scenarios can be split again into a minor and major credit shock scenario:

• Minor shock scenario: To beer illustrate the ecacy of the capital adequacy measures, not just the equity buer, but also the magnitude of the credit defaults is doubled under the ‘minor shock scenario’. us, in period

t1, a credit shock is induced to two borrowers instead of previously just one. Under the initial capital structure, the bank would have defaulted under this credit shock scenario. However, with the increased capital adequacy requirements, the bank can now weather this shock. e borrowers again default on both principal and interest payment. Again, the shock negatively aects both the bank’s balance sheet and income statement. Firstly, the aggregate interest income in the rst period is reduced by 4 monetary units from 20 monetary units (in a ‘no default’ scenario) to 16 monetary units. Of this, a portion d = 20% or 3.2 monetary units (instead of 4 in a ‘no default’ scenario) is paid on the demand deposits. As a result, every depositor receives a periodic interest of 0.4

monetary units (instead of previously 0.44). is increases the wealth of depositors from period t0 to t1, from 10 monetary units to 10.4 monetary units. In the bank’s prot & loss statement, in addition to interest expenditures d and OpEx o, there is now a position related to the complete write-down of the defaulted loans of 20 monetary units, resulting in a net loss of 15.2 monetary units. Although this is a much larger net loss than under the previous ‘minor shock scenario’, the capital buer has also been increased as a result of the capital adequacy measures. As a result, despite the magnitude of the shock, the bank is able to remain solvent independently. In other words, the loss is small enough that the bank’s shareholders can fully absorb it. e net loss is spread equally among the four shareholders, resulting in a shareholder value of 1.2 monetary units aer the loss aribution. In summary, under the ‘minor shock scenario’, the bank remains solvent and losses are eectively absorbed by shareholder’s equity. Put dierently, the capital adequacy measurements seem to be eective in strengthening the banking system’s resilience.

• Major shock scenario: Under the ‘major shock scenario’, as before in section 5.8.1.2, an exogenous negative

credit shock is induced to four borrowers in t1. In such a scenario, the bank will not be able to withstand the increased nancial pressure. In fact, the situation under such a stress scenario does not look too dierent from the bank economy before the capital adequacy measures have been introduced.

95See Shin(2009)(‘Traditionally, capital requirements have been the cornerstone of the regulation of banks. e rationale for capital requirements lies in maintaining the solvency of the regulated institution. By ensuring solvency, the interests of creditors especially retail depositors can be protected.’). 96See Dewatripont(2014) (‘e main innovation here concerns the “Countercyclical Capital Buer”: for the rst time in Basel, the procyclical bias of a constant capital ratio is addressed explicitly, and authorities are meant to require banks to raise their capital ratio in the upside of macroeconomic cycle, in order to be able to ‘release’ capital on the subsequent downside.’); Repullo and Suarez(2004) (‘As described in BCBS (2010), the new international agreement on regulatory standards reinforces capital regulation by means of higher requirements of core Tier 1 capital and by complementing them with a capital preservation buer and a counter-cyclical buer. e idea behind these mandatory buers is to force banks to build up buers in good times and release them in bad times.’). 97Schooner(2016)(‘Given the obvious benets of capital, policy makers raised regulatory capital requirements so that banks are now required to fund more of their operations with equity than in the years prior to the crisis.’).

236 e aggregate interest income in the rst period is reduced by 8 monetary units from 16 monetary units (in a ‘minor shock’ scenario) to 12 monetary units. Of this, a portion d = 20% or 2.4 monetary units (instead of 3.2 in a ‘minor shock’ scenario) is paid on the demand deposits. As a result, every depositor receives a periodic interest of 0.3 monetary units (instead of 0.4 in the ‘minor shock’ scenario). is increases the wealth of depositors from

period t0 to t1, from 10 monetary units to 10.3 monetary units. However, this is before the loss aribution from the write-down of the loans. In the bank’s prot & loss statement, in addition to interest expenditures d and OpEx o, there is now a position related to the complete write-down of the defaulted loans of 40 monetary units, resulting in a net loss of 38.4 monetary units. is loss is now again too large for the shareholders to be able to absorb it in full. e net loss rst wipes out all four shareholders in full. ereaer, a residual net loss of 18.4 monetary units remains, which is spread across the 8 depositors on a pro rata basis. is results in a loss aribution of 2.3 monetary units for each depositor, leading to a net wealth of depositors, aer interest payments and losses, of 8 monetary units. In summary, under the ‘major shock scenario’, despite the increased capital buer, the bank becomes insolvent again and the same challenges as before under the scenario outlined in chapter 5.8.1.2 emerge.

Overall, the results of these negative shock scenarios inform us about the eectiveness of capital adequacy measures as the key regulatory tool of bank regulators. In particular, it appears that capital adequacy measures are very eective at increasing system resilience and stability under certain scenarios. However, they are only eective as long as the size of exogenous credit shocks are of a limited magnitude, not exceeding the capital buer. Secondly, to be eective, the size of credit shocks must be predicted correctly by regulators seing the capital buers. In contrast, major shocks will still result in a bank insolvencies as under the basic shock scenario outline in chapter 5.8.1.2. us, the eectiveness of capital adequacy measures lie in the ability of regulators to correctly anticipate and estimate the magnitude of nancial shocks. However, in ‘black swan’ shock scenarios, with widespread, highly correlated credit losses across the system, increased capital buers will not be able to address the structural challenges associated with a bank’s balance sheet.

237 Bank-based Economy: Capital Adequacy Regulation Event No Default Minor shock Major shock

Period t0 t1 t2 Bank income statement Interest Earned 16 12 Interest Paid -3.2 -2.4 OpEx -8 -8 Write-down loans -20 -40 P&L -15.2 -38.4 Asset/Liability Levels Depositor 10 10.4 8 Borrower -10 -10 -10 Borrower (default) -10 0 0 Shareholder 5 1.2 0

Borrowers Borrowers Borrowers Depositors Depositors Depositors Bank Bank Bank Shareholders Shareholders Shareholders

(a) Base Case (increased equity buer) t0 (b) Minor Shock t1 (c) Major Shock t2

Figure 5.4: Bank-based Economy: Capital Adequacy Regulation Simulation

238 5.8.1.5 Bail-in Regulation

Public bail-in measures are a fairly new regulatory tool within the bank regulator’s toolbox.98 Bail-in measures allow for a conversion of bank debt to equity through a public resolution body. In other words, they allow for a government- ordered debt-to-equity swap when the banking rm is in nancial distress. In this simulation, the eect of bail-in regulation on bank resilience is modeled for ‘major credit shock’ stress scenarios. e starting point is again the base case at t0, with the parameters set as before. e modeled banking economy consists of 10 borrowers, each one borrowing 10 monetary units, 9 depositors, each one depositing 10 monetary units, and 2 shareholders, with shares valued at 5 monetary units each.

• Major shock scenario: As for the bailout scenarios, the simulation starts with the ‘major shock scenario’ and a

‘logical crisis second’ at t1, during which the banking rm, bank regulators and public ocials can anticipate the

economic repercussions of the credit shock and react with a public bail-in measure at t2. Again, a negative exoge-

nous credit shock is induced to 4 borrowers at t1. In the absence of any state intervention, the same economics as before under the private loss absorption mechanism would prevail. is would eventually leave shareholders fully wiped out and bank depositors, aer the loss aribution, with a net wealth of 7.11 monetary units.

• Bail-in scenario: However, before the private load balancing mechanism takes eect at t1, it is now assumed

that the public resolution body steps in at t2 and ‘bails in’ a portion of the bank’s creditors. Given that the model assumes only one class of bank creditors (depositors), the choice of the bank creditors which are bailed-in is arbitrary in the simulation. Under most bail-in regulations, secured creditors and bank depositors are excluded from bail-ins. For example, the European Bank Recovery and Resolution Directive (BRRD),99 which has introduced bail-ins to the European supervisory toolkit, explicitly limits bail-ins to wholesale funding by excluding deposits and secured bank creditors from the mechanism.100 In a more complex model, dierent classes of creditors would be dened and the bail-in mechanism would only aect long-term loss absorbing capital.101 However, given that the present model only considers one class of bank creditors, it is assumed that the public resolution body has full discretion to bail-in an arbitrary number of depositors. e bail-in takes eect as follows: rstly, as for the private loss absorption mechanism, shareholders are wiped out in full. Aer the loss absorption by shareholders, a residual net loss of 28.4 monetary units remains. Under the private loss absorption mechanism, the bank would now declare bankruptcy and losses would be equally split across all depositors. However, under the bail-in scenario, the public resolution body steps in and converts four depositor nodes into novel bank shareholders. With the deposit interest that has accrued on these depositor nodes in period one, their aggregate wealth prior to the bail-in amounts to 41.06 monetary units. Aer absorbing the residual loss of 28.4 monetary units through the bail-in, an aggregate net wealth of 12.66 monetary units remains. us, each of the four newly issued/converted shares has a net worth of 3.16 monetary units.

Overall, the results of such a negative shock scenario inform us about the eectiveness of bail-in measures as a regulatory tool. It appears that bail-in measures can be rather eective at making bank debt ‘soer’ in a crisis scenario. However, we can also see that bail-ins are ad-hoc solutions only, not global solutions. At it’s essence, bail-in measures do not address the core challenge of bank supervision, namely that bank credit is fundamentally not maturity-matched and, as a result, recurring shocks would theoretically require multiple bail-in measures to soen the blows.

98See Dewatripont(2014) (‘More than 5 years aer the fall of Lehman Brothers, and at a time where the Banking Union will start with a Balance Sheet Assessment, several regulatory initiatives are trying to make bailouts harder, and push forward ‘bail-in’ as an alternative.’). 99Directive 2014/59/EU of the European Parliament and of the Council of 15 May 2014 establishing a framework for the recovery and resolution of credit institutions and investment rms. 100See BRRD, preamble 70 (‘It is not appropriate to apply the bail-in tool to claims in so far as they are secured, collateralised or otherwise guaranteed. However, in order to ensure that the bail-in tool is eective and achieves its objectives, it is desirable that it can be applied to as wide a range of the unsecured liabilities of a failing institution as possible. Nevertheless, it is appropriate to exclude certain kinds of unsecured liability from the scope of application of the bail-in tool. In order to protect holders of covered deposits, the bail-in tool should not apply to those deposits that are protected under Directive 2014/49/EU of the European Parliament and of the Council.’). 101See Dewatripont(2014) (‘Second, the negative impact of bail-in in terms of nancial stability can be contained by insisting that banks have sucient long-term securities that can be bailed-in before deposits start to face risk.’).

239 Bank-based Economy: Bail-in Regulation Event No Default Major shock Bail-in

Period t0 t1 t2 Bank income statement Interest Earned 12 Interest Paid -2.4 OpEx -8 Write-down loans -40 P&L -38.4 Loss aer equity -28.4 Bail-in depositors 41.06 New equity 12.66 Per shareholder 3.16 Asset/Liability Levels Depositor 10 7.11 10.26 Bailed-in depositor 3.16 Borrower -10 -10 -10 Existing shareholder -10 0 0

Borrowers Borrowers Borrowers Depositors Depositors Depositors Bank Bank Bank Shareholders Shareholders Shareholders

(a) Base Case t0 (b) Major Shock t1 (c) Bail-in t2

Figure 5.5: Bank-based Economy: Bail-in Simulation

240 5.8.2 Market-based system

5.8.2.1 Disintermediated market-based credit system: positive shock

e rst market-based system analyzed is a ‘disintermediated credit system’, whereby each borrower node is nanced by exactly one creditor. In this system, creditors have maximal idiosyncratic risk exposure with respect to a particular borrower. e disintermediated credit system is a market-based system without diversication, which can be thought of as a precursor to a fully distributed credit system. To map this system to a real world seing, one can think of credit systems with nascent pooling vehicles, such as local municipal debt markets or peer-to-peer lending systems. Before credit shocks to such a system are analyzed, an economic upswing is simulated here. e modeled market economy consists of 10 borrowers, each one borrowing 10 monetary units, and 10 creditors, each one investing 10 monetary units. e ‘positive shock’ is again associated with the absence of creditor defaults, which means that all 10 borrowers remain solvent and pay interest in full on their loans. e origination of the credit securities is orchestrated by a fully equity-nanced credit originator. is credit originator is a market-enabling rm, which can be thought of as one of the credit intermediaries detailed in chapter 4 of this thesis, such as a mortgage bank, non-bank lender or an investment bank. However, unlike these real-world credit intermediaries, which typically take proprietary credit positions in the underwrien credit, the assumption here is that these rms only act as information brokers. In other words, they are engaged solely on ‘best eorts’ oering basis, rather than a ‘rm commitment’ basis. us, they do not make use of their balance sheet to warehouse newly issued credit securities and can thus be wholly equity nanced. In the model, the loan originators are nanced by two shareholders, with shares valued at 5 monetary units each (similar to the banks in the bank-based regimes). As for the bank-based systems, every borrower is assumed to pay interest of 2 monetary units per period. is results in aggregate interest payments of 20 monetary units per period. Unlike under the bank-based system, where the residual of this interest goes to shareholders, in maturity matched credit systems, creditors can capture the full economic value aer expenses. In particular, it is assumed that loan origination fees o of an aggregate of 8 monetary units accrue, which is equal in size to the operating expenditures o under the bank-based system. ese origination fees cover the operating expenditures (OpEx) of the loan originator. It is assumed that the loan originator does not make a prot on this, leaving its shareholders at at 5 monetary units per share. e aggregate net prots of 12 monetary units, 20 units in interest income minus the origination fees, is disbursed in full to creditors aer each period. ereby, each creditor’s net wealth increases from 10 monetary units to 11.2 monetary units from period t0 to t1 and from 11.2 monetary units to 12.4 monetary units between period t1 and t2. us, the value captured by creditors under the market-based system is substantially larger than under the bank-based system. In the economic upswing scenario under the bank-based system, each depositor’s wealth has increased from 10 monetary units to 10.44 monetary units between period t0 and t1 and to 10.88 monetary units in period t2. Overall, we can see that the market-based system can enable creditors to capture more value, given our distinct model assumptions. In the bank-based system, the surplus between interest received and interest paid out to depositors – the ‘net interest margin’ – is captured by shareholders. In contrast, in the modeled market-based credit system, the market-enabling rm does not participate in the net interest margin. Instead, creditors capture the full economic upside from their direct credit exposure, net of a xed origination fee.

241 Disintermediated market-based credit system: positive shock Event No Default No default No default

Period t0 t1 t2 Income statement loan originator Origination fee 8 8 OpEx -8 -8 P&L 0 0 Aggregate Creditor P&L Interest earned 20 20 Origination fee -8 -8 P&L 12 12 Asset/liability Creditor 10 11.2 12.4 Borrower -10 -10 -10 Shareholder 5 5 5

Borrowers Borrowers Borrowers Creditors Creditors Creditors Loan Originator Loan Originator Loan Originator Shareholders Shareholders Shareholders

(a) Base Case t0 (b) No default t1 (c) No default t2

Figure 5.6: Disintermediated market-based credit system: Economic Upswing Simulation

242 5.8.2.2 Disintermediated market-based credit system: negative shock

Under the negative shock scenario for the disintermediated system, the modeled system is again subjected to an exogenous credit shock. e starting point is once more the base case scenario as before and both a minor and a major credit shock is induced to test the resilience of the system. For the purpose of this simulation, a private loss absorption mechanism without state intervention is assumed.

• Minor shock scenario: e simulation starts with a ‘minor shock scenario’, where a negative exogenous credit

shock is induced to one borrower at t1. Again, the shocked borrower defaults on both principal and interest payments in full, with the net wealth going from negative 10 monetary units to 0 monetary units. What can be observed is that, unlike under the bank-based allocation, the credit shock remains almost fully local, leaving most

creditors largely unscathed. Firstly, the aggregate interest income in period t1 is reduced by 2 monetary units from 20 monetary units (in a ‘no default’ scenario) to 18 monetary units. Since only one borrower defaults, an aggregate interest income of 10 monetary units remains aer an origination fee of 8 is paid to the loan origination rm. is

increases the wealth of creditors of non-defaulting borrowers between period t0 and t1 from 10 monetary units to 11.11 monetary units. is is slightly lower than under the ‘no default scenario’, as it is assumed here that the aggregate origination fee is paid ex-post out of aggregate interest payments received. us, the origination fee remains xed and aggregate interest payments used to cover them are lower. As a result, the non-defaulting creditors cover for the pro rated portion of the defaulting borrower’s origination fee. In an alternative setup, one could assume that the origination fees are paid ex-ante by borrowers and a default would thus not aect the return of these creditors. However, in aggregate, non-exposed creditors still emerge with a positive balance and only a marginally lower interest payment. On the other hand, the one creditor exposed to the defaulting borrower node has to write down the credit in full. us, there is no principal repayment or periodic interest payment and the

creditor’s wealth thus drops from 10 monetary units in period t0 to 0 in period t1.

• Major shock scenario: e same eect holds under the ‘major shock scenario’, where a negative exogenous

credit shock is induced to four borrowers instead of just one in t1. is means that borrowers again default on both principal and interest payments in full. Like the ‘minor shock’, the ‘major credit shock’ remains mostly local, leaving all creditors, except for the 4 with idiosyncratic credit exposure, largely unscathed. Firstly, the aggregate

interest income in period t1 is reduced by 8 monetary units from 18 monetary units (in a ‘minor shock’ scenario) to 12 monetary units. Since four borrowers default, a net aggregate interest income of 4 monetary units remains for distribution aer an origination fee of 8 is paid to the loan origination rm. Nevertheless, the wealth of the

six creditors of non-defaulting borrowers is still increased between period t0 and t1 from 10 monetary units to 10.44 monetary units. On the other hand, the four creditors exposed to the defaulting borrowers node have to write down the credit in full. us, there is no principal repayment or periodic interest payment and the creditor’s

wealth thus drops from 10 monetary units in period t0 to 0 in period t1.

Overall, we can see that the disintermediated market-based system is fairly resilient in times of economic distress. In particular, losses are absorbed locally, rather than globally, preventing the spreading of risks throughout the network. However, it also exposes the weaknesses of a credit system with weak (or in this case no) diversication. e few creditors exposed to the local defaults take the brunt of the credit loss. Because of these shortcomings in terms of risk spreading, risk-avers creditors would rationally oppose such a system. Since such a market-based allocation would force them to ‘put all their eggs in one basket’, they would rationally prefer a bank-based system instead. us, a more ecient market-based system would be a fully distributed credit system, which is explored in the next section. In such a stylized loss absorbing system, every creditor is fully diversied amongst all borrower nodes.

243 Disintermediated market-based credit system: negative shock Event No Default Minor shock Major shock

Period t0 t1 t1 Income statement loan originator Origination fee 8 8 OpEx -8 -8 P&L 0 0 Aggregate Creditor P&L of non-defaulting borrower(s)) Interest earned 18 12 Origination fee -8 -8 P&L 10 4 Aggregate Creditor P&L of defaulting borrower(s) Write-down loan -10 -40 P&L -10 -40 Asset/liability Creditor 10 11.11 10.44 Creditor (default) 10 0 0 Borrower -10 -10 -10 Shareholder 5 5 5

Borrowers Borrowers Borrowers Creditors Creditors Creditors Loan Originator Loan Originator Loan Originator Shareholders Shareholders Shareholders

(a) Base Case t0 (b) Minor Shock t1 (c) Major Shock t1

Figure 5.7: Disintermediated market-based credit system: Negative Shock Simulation

244 5.8.2.3 Distributed market-based credit system: positive shock

e second market-based credit system analyzed is a ‘distributed credit system’ where each borrower is nanced by all creditors through a system-wide pooling vehicle/mechanism. Whereas the disintermediated credit system provides for maximal idiosyncratic risk exposure with respect to a particular borrower, the distributed system lies at the opposite end of the diversication spectrum. In other words, creditors are at any point in time maximally diversied among all borrower nodes. Like a distributed computing system, this credit system is composed of a multiplicity of independent creditor nodes. e distributed middleware layer ensures resilience by spreading credit exposure evenly across the ‘redundant’ creditor nodes in the network. is is also reected in the network topology, which is that of a ‘closed bi-partite network’ with all creditor nodes individually being connected to all borrower nodes. To map this system to a real world seing, one can think of a credit systems where surplus agents access credit mostly through diversication vehicles, such as xed- income mutual funds or exchange-traded funds (ETFs). ese pooling vehicles eectively provide the ‘load balancing mechanism’ for the distributed nancial system. Before credit shocks to such a system are analyzed, an economic upswing is simulated here, which yields very similar results to the disintermediated system. e modeled market economy again consists of 10 borrowers, each one borrowing 10 monetary units, and 10 creditors, each one investing 10 monetary units. e ‘positive shock’ is again associated with the absence of credit defaults, which means that all 10 borrowers remain solvent and pay interest in full on their loans. e origination of the credit securities is again orchestrated by the same, fully equity-nanced credit originating rm. Again, every borrower pays 2 monetary units in interest. is results in aggregate interest payments of 20 monetary units per period. Aer the deduction of the origination fee o of 8 monetary units, aggregate net interest payments of 12 monetary units are available for distribution. Notably, this is the same as under the disintermediated system. e aggregate net prots of 12 monetary units, 20 units in interest income minus the origination fees, are again disbursed in full to creditors aer each period. ereby, each creditor’s net wealth increases from 10 monetary units to 11.2 monetary units from period t0 to t1 and from 11.2 monetary units to 12.4 monetary units between the periods t1 and t2. us, again, the value captured by creditors under this market-based system is substantially larger than under the bank-based system. So far, the simulation of the distributed system does not appear to dier considerably from a disintermediated system. All creditors end up with the same wealth allocation in t1 and t2. As the model economy assumes homogeneous borrowers with uniform returns, in the absence of defaults, diversication does not aect the nal wealth allocation. In a more realistic simulation with heterogeneous credit assets, wealth levels would dier in the ‘no default’ scenario as well. In particular, under a disintermediated credit system with heterogeneous borrowers, creditors would receive varying periodic interest payments. ese would reect dierent risk and return proles of the nanced credit assets. In contrast, given the systematic diversication of distributed nancial systems, interest payos would remain uniform under the distributed systems architecture.

245 Distributed market-based credit system: positive shock Event No Default No default No default

Period t0 t1 t1 Income statement loan originator Origination fee 8 8 OpEx -8 -8 P&L 0 0 Aggregate Creditor P&L Interest earned 20 20 Origination fee -8 -8 P&L 12 12 Asset/liability Creditor 10 11.2 12.4 Borrower -10 -10 -10 Shareholder 5 5 5

Borrowers Borrowers Borrowers Creditors Creditors Creditors Loan O. Loan O. Loan O. Sh. Sh. Sh.

(a) Base Case t0 (b) Minor Shock t1 (c) Major Shock t1

Figure 5.8: Distributed market-based credit system: Economic Upswing Simulation

246 5.8.2.4 Distributed market-based credit system: negative shock with private loss absorption

Under the negative shock scenario for the distributed credit system, exogenous credit shocks of dierent magnitudes are again induced to the system. e starting point is once more the base case scenario, with both a minor and a major credit shock induced at t1 to test the resilience of the system. For the purpose of this simulation, a private loss absorption mechanism without state intervention is again assumed.

• Minor shock scenario: e simulation starts again with a ‘minor shock scenario’, where a negative exogenous

credit shock is induced to one borrower at t1. Once again, the shocked borrower defaults on both principal and interest payments in full, with the net wealth going from negative 10 monetary units to 0 monetary units. What can be observed is that, in contrast to the disintermediated credit system, where loss absorption is mostly local, the

shock is now evenly spread across all creditors. Firstly, the aggregate interest income in period t1 is reduced by 2 monetary units from 20 monetary units (in a ‘no default’ scenario) to 18 monetary units. Since only one borrower defaults, an aggregate net interest income of 10 monetary units remains aer the origination fee of 8 monetary units is paid to the loan origination rm. Aer the write down of the defaulting loan of exactly 10 monetary units,

the entire class of market-based creditors stays ‘at’ at a wealth level of 10 monetary units between period t0 and

period t1.

• Major shock scenario: e same eect holds under the ‘major shock scenario’, where a negative exogenous

credit shock is induced to four borrowers instead of just one at t1. Under this scenario, the aggregate interest

income in period t1 is again reduced by 8 monetary units from 18 monetary units (in a ‘minor shock’ scenario) to 12 monetary units. Since four borrowers default, a net aggregate interest income of 4 monetary units remains for distribution aer an origination fee of 8 is paid to the loan origination rm. In addition, there is a write-down of 40 monetary units for the defaulting borrowers. us, overall losses of 36 monetary units are absorbed between

all creditors, reducing their net wealth from 10 monetary units each to 6.4 monetary units from period t0 to t1.

Overall, the distributed market-based credit architecture exhibits high systemic resiliency in times of economic distress. As a fully maturity matched system, creditors act as the only loss absorbing nodes, with loan originating rms only facilitating market transactions without taking proprietary positions. Creditors appear as redundant nodes in a distributed nancial system that can absorb both minor and larger credit shocks. While they capture more value on the upside in the market-based system than under the bank-based allocation, they also absorb a larger portion of losses in a downside scenario. e loan originating rms on the other hand, unlike banks, never default and are able to keep the market alive, even in times of economic distress. In particular, compared to the disintermediated credit system, which ‘overloads’ individual creditor nodes that are exposed to defaulting borrowers, the distributed system provides for a more resilient ‘load balancing’ mechanism. All credit gains and losses are ‘socialized’ among the entire class of surplus agents. is means that creditors uniformly prot from a ‘no default’ economic upswing scenario, but that major credit shocks can also result in all creditors’ assets ‘trading below the water mark’, i.e. losing more than their principal investment. In contrast to a bank-based system, the burden of loss absorption on market creditors is higher compared to that of bank depositors. In a bank-based system, credit shocks are absorbed by specialized shareholder nodes as a rst line of defense. In a bank-based architecture, minor shocks can be absorbed entirely by shareholders, leaving depositors unscathed with their full principal and periodic interest payments. Only when major shocks hit the system, are depositors adversely aected at all. us, in a downside scenario, the bank-based system appears more favorable to credit providers. However, from a systemic risk perspective, it is crucial to note that the market-enabling rm (loan originator) remains fully intact in the stress scenario. In addition, in the economic upside scenario, the market-based creditors capture more value. us, under a private loss absorption mechanism, a distributed market-based system appears superior to a bank- based system in terms of its ability to absorb systemic credit shocks. However, in the presence of insured bank deposits, market-based credit is unable to compete eectively with a bank-based architecture, as risk-adverse savers will prefer

247 the safety and consumption exibility of bank deposits. us, the next simulation sketches out a public loss absorption mechanism for market-based credit systems.

Distributed market-based credit system: negative shock with private loss absorption Event No Default Minor Shock Major Shock

Period t0 t1 t1 Income statement loan originator Origination fee 8 8 OpEx -8 -8 P&L 0 0 Aggregate Creditor P&L Write-down loans -10 -40 Interest earned 18 12 Origination fee -8 -8 P&L 0 -36 Asset/liability Creditor 10 10 6.4 Borrower -10 -10 -10 Shareholder 5 5 5

Borrowers Borrowers Borrowers Creditors Creditors Creditors Loan O. Loan O. Loan O. Sh. Sh. Sh.

(a) Base Case t0 (b) Minor Shock t1 (c) Major Shock t1

Figure 5.9: Distributed market-based credit system: Negative Shock Private Loss Absorption Simulation

248 5.8.2.5 Distributed market-based credit system: market bailouts

A second negative shock scenario for the market-based distributed credit system involves a public loss absorption mechanism, which is similar to the bank bailout scenario analyzed in section 5.8.1.3. In other words, a loss absorption mechanism is analyzed where government guarantees and state intervention are present. In the context of credit markets, such bailout mechanisms are not typically available ex-ante. Unlike for insured bank deposits, public loss absorption mechanisms are not typically baked into the fabric of market-based credit oerings. For corporate and asset-backed bonds, the expectation is that they can default if economic conditions of the borrowers do not allow for a repayment. Credit market bailouts, such as the government intervention in 2008 with respect to the two government-sponsored enterprises (GSEs) Fannie Mae and Freddie Mac or during the recent Covid-19 pandemic in 2020, are thus rather the exception than the rule. In credit market bailouts, the government, by way of open market intervention or through sovereign legal means (such as placing the GSEs under conservatorship), provides for an ex-post public loss absorption mechanism.

e base case at t0 looks like under the other shock scenarios discussed above, with the same number of borrowers and depositors and the same asset allocation. e simulation starts with the ‘major shock scenario’ from the private loss absorption mechanism, where a negative exogenous credit shock was induced to 4 borrowers in period t1. It is assumed that the economics from the ‘major shock scenario’ stay the same for one ‘logical crisis second’, during which market regulators and public ocials can anticipate the economic repercussions of the credit shock and react with a public bailout measure in t2. During this ‘logical crisis second’, the exogenous credit shock is expected to put all creditors under water, with their wealth decreasing from 10 monetary units in period t0 to 6.4 monetary units in period t1. However, a logical second before the private load balancing mechanism takes eect in t1, it is now assumed that the government steps in at t2 and bails out the credit markets by spreading the credit losses across all agents in the economy. In the simulation, public loss absorption is modeled by distributing the sum of the credit losses equally over the total number of surplus and decit agents. Again, it is assumed that the government intervention is nanced by future taxes, which aect all agents equally. In other words, the total net loss of 36 monetary units is spread across all ten borrowers, all ten creditors and the two shareholders, resulting in a ‘net loss’ or ‘net bailout tax’ of 1.63 monetary units per agent.

e wealth allocation observed aer the government intervention in period t2 is structurally very similar to the wealth allocation aer the bank bailout:

• Creditors: First of all, the government bailout results in a positive wealth transfer towards creditors. Deducting the ‘net bailout tax’ per agent of 1.63 monetary units from creditor’s wealth aer interest income (10.4 monetary units) results in a post-bailout creditor wealth level of 8.77 monetary units. us, market creditors are signicantly beer o than under the private loss absorption mechanism, with a wealth level that is 2.37 monetary units higher

than the 6.4 monetary units in t1 before the bailout. With a dierence of only 0.34 monetary units, this looks very similar to the wealth allocation of depositors aer the bank bailout in section 5.8.1.3 (8.43 monetary units).

• Shareholders: Secondly, shareholders of the loan origination rm are now also aected. ey absorb an equal ‘net bailout tax’ of 1.63 monetary units, leaving them with a net wealth of 3.37 monetary units aer the bailout. us, shareholders are made worse o by the credit market bailout. Compared to the private loss absorption scenario, where they do not bear any losses, they now have a post-bailout wealth level that is only 0.2 monetary units higher than bank shareholders aer the bank bailout (3.17 monetary units). However, whereas the bank bailout predominantly beneted bank shareholders, a directionally opposite eect has led to this nal wealth allocation. In summary, with respect to the surplus agents, the bailout seems to be a bailout of creditors. is is because under the fully market-based system, creditors are the main loss absorbing nodes.

• Borrowers: With respect to borrowers, one can distinguish between defaulting and non-defaulting borrowers. e non-defaulting borrowers, which initially owe a principal of 10 monetary units, also absorb the ‘net bailout tax’ of 1.63 monetary units and thus have a negative wealth level of 11.63 monetary units aer the market bailout. e defaulting borrowers on the other hand have a wealth level of 0 monetary units aer defaulting on their

249 loans. Since they also absorb the ‘net bailout tax’ of 1.63 monetary units, they have a negative wealth of 1.63 monetary units aer the bailout. us, like for the bailout under the bank-based system (negative 11.82 for the non-defaulting borrowers and negative 1.82 for the defaulting borrowers), the bailout has an adverse economic eect on both types of decit agents.

e main result from this simulation is that government intervention has a leveling eect on the modeled nancial systems. Loss allocation under the market-based credit system looks very similar to the bank-based system, once the government intervenes. is draws into question, how large the dierences between a bank-based and a market-based system really are. While the bailout of banks are a bailout of both bank shareholders and depositors, the bailout of credit markets mainly benets creditors. Under both regimes, however, decit agents are made worse o by the bailout.

Distributed market-based credit system: bailout Event No Default Major Shock Bailout

Period t0 t1 t2 Income statement loan originator Origination fee 8 8 OpEx -8 -8 P&L 0 0 Aggregate Creditor P&L Write-down loans -40 Interest earned 12 Origination fee -8 P&L -36 Total loss -36 Total agents 22 Loss per agents -1.63 Asset/liability Creditor 10 6.4 8.77 Borrower -10 -10 -11.63 Borrower (default) -10 -10 -1.63 Shareholder 5 5 3.37

Borrowers Borrowers Borrowers Creditors Creditors Creditors Loan O. Loan O. Loan O. Sh. Sh. Sh.

(a) Base Case t0 (b) Major Shock t1 (c) Bailout t1

Figure 5.10: Distributed market-based credit system: Market Bailout Simulation

250 5.8.3 Discussion

e dierent scenarios and simulations reveal vast dierences between a bank-based and market-based allocation of credit. In particular, it appears that incentive structures, loss allocation and systemic risks dier substantially and crucially depend on the direction and size of economic shocks and the dominant loss absorption mechanisms.

5.8.3.1 Economic upswing simulations

By modeling economic booms, we can get a glimpse into the dierent incentive structures that exist under the competing regimes. In particular, it appears that under the bank-based system, a large part of the value is captured by shareholder nodes. As described in the literature102 this may provide incentives for shareholder-led banking rms to increase their balance sheet to sub-optimal levels and invest in too risky assets (asset substitution). In contrast to this, under a market-based system, a large part of the value is captured by creditors. is is independent of the level of diversication and holds both for ‘merely’ disintermediated systems and fully distributed systems. Under our model, nancial institutions facilitating loan origination become infrastructure-like market platforms, without any ‘skin in the game’. On the other hand, given the direct exposure to borrowers, it is on creditors to monitor borrowers’ risk proles.

5.8.3.2 Private loss absorption simulations

Under the private loss absorption scenarios, the heterogeneous resiliency features of banking rms and credit markets are highlighted. Under the banking-rm allocation, shareholders are the rst frontier when it comes to absorbing credit losses. ey are specialized capital providers, which reap higher returns in upswing scenarios, but also take a harder hit from negative shocks. Loss absorption by shareholders works well, as long as credit losses are small. However, as losses increase in size and severity, this becomes problematic. In the case of a systemic credit crisis, shareholders get wiped out and the banking rm can face insolvency. Under ordinary bankruptcy proceedings, losses would then be distributed among creditors as a ‘second line of defense’. In theory, this private loss cascade works well, as it allocates losses among stakeholders with dierent risk proles. However, where the bank is systemically relevant, bank failure may not be an option and governments may be pressured to step in. Regulatory tools to address these shortcomings of bank-based systems have dierent levels of ecacy. From the simulations, it appears that capital adequacy measures are essentially a ‘scaling mechanism’, which enable the banking rm to absorb large credit losses through the equity buer. On the other hand, bail-in measures appear quite eective at ‘soening’ the nancing provided to banks by converting credit into equity. However, such bail-in measures still remain just ‘point solutions’, rather than global solutions. us, if credit losses continue aer an initial bail-in, additional debt tranches would need to be bailed in. On the other hand, it appears that credit markets can be quite eective at absorbing credit losses, at least under the stylized assumptions of our model. Instead of the bank’s balance sheet, all credit losses are fully borne by specialized credit suppliers. Most importantly, credit losses do not induce institutional failure: loan originators are modeled as fully equity-nanced nancial utilities, not taking any economic share in the originated loans. All credit is maturity-matched and provided directly by surplus agents. In the disintermediated credit market system, credit losses are absorbed locally by individual creditors. As this would have overly adverse eects for a few select creditor nodes, a distributed system appears more favorable. Under the modeled too-distributed-to-fail system, credit losses are equally spread across all creditor nodes. In such a system, creditor nodes appear as redundant economic surplus nodes, which can dynamically enter and exit the nancial network. 102In particular, M. C. Jensen and Meckling(1976) and R. Merton(1974).

251 5.8.3.3 Government bailouts simulations

Under the government bailout simulations, many of the dierences between bank-based and market-based systems level out. Under the bank-based system, government bailouts seem to have the eect of being primarily a bailout of shareholders and, in a second instance, of creditors. On the other hand, decit agents, whether it is solvent borrowers or defaulting borrowers, are hit hardest, as they have to absorb socialized bailout losses, despite their already negative wealth accounts. On the other hand, bailouts of credit markets appear to be mainly bailouts of creditors, disadvantaging both shareholders and borrowers. us, while the likelihood of bailouts under an ecient market-based allocation appears to be lower, once the government steps in, these dierences are marginalized.

5.8.4 Limitations

In this chapter, we have chosen a stark set of stylized assumptions to highlight the interaction of risk shiing, nancial contagion and societal costs in a distributed nancial architectures. Without being conclusive, the below list points to some of the major limitations of the model:

• Deterministic nature: a clear limitation of the developed model is that it is entirely non-stochastic. Once a network topology has been constructed and an initial credit shock has been induced, the size and propagation of the shock and the loss contagion follows a purely deterministic process, which depends on the pre-set, exogenous model parameters. While many traditional banking models have analysed regimes with deterministic exogenous elements,103 network-based nancial stress test models oen randomize the network topology.

• Partial equilibrium: in addition to self-fullling equilibria, the developed model is, like most nancial stress test models, a partial equilibrium model.104 In contrast, a general equilibrium model, like the one developed by Gersbach and Rochet(2012), could model the interaction of systemic risk and asset prices, thereby capturing the nancial amplication eects during booms and busts.

• Transaction costs: the model assumes that the transaction costs of the bank and the market are the same. Encapsulated in the operating expenditure (OpEx), in the bank-based system, and in the loan origination fee, in the market-based system, respectively, they are both set at xed and equal monetary amounts. However, in reality, stemming from a multitude of factors, including most notably regulatory costs (discussed at length in chapter 4) and scale eects, these costs can vary considerably.

• Inter-connectivity: the modeled economies provide for discrete and mutually exclusive allocative systems. Ei- ther a discretely bank-based economy or a fully market-based credit system is simulated. However, in reality, bank-based and market-based systems oen exist in parallel, with many contagion channels arising at the intersection of banks and nancial markets.

• Loan originations: under the stylized market-based system, loan originators do not hold any economic stake in the originated loans. However, in reality, credit is oen underwrien and held-to-maturity by market-enabling rms, such as investment banks. In particular, under the new ABS risk retention regulations, originators are pro-actively required to retain an economic stake aer origination.

103See Diamond and Dybvig(1983b) (modeling both deterministic and stochstastic withdrawal scenarios); F. Allen and Gale(2000b) (modeling both stochastic and non-stochastic liquidity shocks). 104See European Central Bank (ECB)(2013) (‘However, top-down stress testing frameworks (including the one presented in this paper) use at best partial equilibrium approaches. e main reason is that stress testing frameworks at their core are focused on deriving individual and heterogeneous bank level results, which is dicult to process within fully consistent general equilibrium frameworks that typically operate with a representative agent concept.’); Davis, Liadze, and Piggo(2019) (Banking/nance models, in the tradition of Diamond and Dybvig (1983) highlight how nancial contracts are aected by various incentive problems related to information asymmetry and commitment that can entail default. […] Furthermore, they tend to be partial equilibrium and thus omit key general-equilibrium eects.’).

252 • Government bailouts: the model assumes that government bailouts spread losses equally among all economic agents in the economy. In reality, decit agents may be less likely to pay an equal share of future taxes, thus decreasing the size of the eect in the model.

It is clear that in more complex models, we can expect a wider array of interactions and general equilibrium eects. Given the reductionist nature of the presented model, it is questionable whether the main phenomena outlined above will survive in a wider variety of models.

5.9 Stylized too-distributed-to-fail credit markets

In network theory, it is generally accepted that network performance, scalability and robustness to various types of perturbations critically depend on the network’s topology (Newman, Barabasi, & Was, 2006). By mimicking the redundancy observed in other network-based complex systems, a stylized too-distributed-to-fail credit market system, as modeled in this chapter, promises to bring the robustness of biological and computational systems to the realm of nancial systems. For this purpose, we have rst modeled out the existing banking network structure by breaking it into its constituent components: surplus and decit agents. is allowed us to rst identify the redundant components (architectural disintermediation). In a second step, the critical components have been re-wired to create a fully distributed nancial system with a load balancing algorithm that logically passes gains and losses to all available creditor nodes in the system (logical centralization). By turning depositors into creditors, they can provide credit to borrowers directly – in the limit in a fully diversied or distributed manner. Surplus agents thereby become redundant system components within the nancial network and credit losses can be absorbed directly by maturity-matched debt. Under the stylized model assumptions, it appears that a nancial system with direct market-based economic links between the components creates a system with higher network density, path redundancy and system resilience compared to a traditional banking system. e theoretical advantages of such too-distributed-to-fail credit markets resemble those of distributed computing systems:

• Resilience: since credit is originated and serviced on a peer-to-peer basis, there exists no single-point-of-failure. With a multiplicity of redundant credit suppliers, partial failure is possible without disrupting the entire system. is increases system resilience compared to systems that are intermediated through the balance sheets of central banking nodes.

• Scalability: in a well-designed distributed nancial system, entry and exit of nodes is possible without constrain- ing capital supply. is requires capital supply and demand to be matched dynamically as credit demand expands and contracts over time.

• Collusion resistance: in banking systems, the central banking nodes in the system are governed by shareholders and there is a high risk of collusion with regulators (regulatory capture) or key creditors. In contrast, individual nodes in a distributed nancial system lack the ability and incentive to collude.

Despite the many theoretical advantages of a fully distributed credit system, such systems remain fully theoretical in nature. In practice, there still exist many frictions at the dierent levels of credit origination and distribution. Fur- thermore, the proposed too-distributed-to-fail credit system developed within this chapter assumes that overall transaction costs under a market allocation are equivalent to a banking-based allocation structure, which neglects rm-level economies of scale and scope. Furthermore, even a perfect market allocation comes with certain unique challenges. In a banking economy, for example, the inner workings of the credit origination process and partial failures of borrower nodes are typically masked from capital providers (at least until certain regulatory thresholds are passed). In general, the ability to hide partial failure

253 is crucial for the smooth functioning of distributed system. However, masking partial system failure in a fully distributed and transparent architecture, as proposed herein, is more dicult and provides a key design challenge.

5.10 Conclusion

In this chapter the interplay between surplus and decit agents has been examined using a simple stress test model that allows for a network simulation of both bank-based and market-based economies. Although stylized and deterministic, the model set-up is suciently rich to permit the study of credit shocks under a range of dierent allocation structures and regulatory regimes. e dierent network topologies constructed and studied vary in terms of complexity, risk concentration and nancial stability. Deterministic numerical simulations are performed, which illustrate the potential fragility of a bank-based and market-based system respectively. It was established that bank-based systems are suciently resilient in minor shock scenarios, but that they carry major systemic risks in larger-scale, exogenous credit shock scenarios. e model also allows us to analyze the wealth eects of bank bailouts, which seem to exacerbate wealth distributions under our model assumptions. e parsimony of the framework further allows for an analysis of the ecacy of recent regulatory reforms, including capital adequacy measures and bail-in regulations. It was found that, while these may alleviate systemic shocks, they do not resolve the underlying architectural challenges. In a further step, a number of stylized market-based credit systems are modeled in and contrasted with bank-based economies in terms of value capture in ‘calm times’ and systemic resiliency in stress scenarios. It is found that, under the rigid assumptions of the presented model, credit markets can indeed be too-distributed-to-fail. However, it is also established that many of the dierences between a bank-based and a market-based system level out if a government intervention mechanism is introduced to the model. In summary, this chapter introduces a network-based model for analyzing the dierential wealth eects that bank- based and market-based systems can have on surplus and decit agents. While clearly relying on a number of limiting assumptions, the model can highlight distributional eects relevant for regulators and policy makers when designing nancial sector reform strategies.

254 Epilogue

is PhD thesis has embarked on a rather ambitious project, oscillating between the theoretical and empirical, both in the legal and nancial domain and touching on many dierent asset classes with idiosyncratic properties. e thesis makes an aempt at a general law & economics theory of securities regulation and applies the developed ‘eorem’ across dierent asset classes. e theoretical perspective is further challenged through the economic chapters, which conduct empirical studies and perform network simulations. us, the thesis does not suer from a breath of perspectives and methods. However, given the plurality of angles, the avid reader may be le asking what to take from the thesis as a whole body of literature and what future streams of research could be inspired by it. e main contribution of this thesis can be framed in the analogy of a wiring or circuit board. Just like a circuit board can be wired and re-wired in a number of ways in an iterative aempt to nd the optimal design, economic transactions can be placed under dierent allocation regimes that can be dynamically re-allocated before a global equilibrium is reached. e ‘Coase eorem of Securities Regulation’ draws aention to the fact that dierent regulations can act like switches on a circuit board that wire transactions through either a rm or a market allocation. By developing this engineering perspective on securities regulation and alternative regulatory regimes, such as exemptions or industry-specic rm regulations, the eorem provides a systematic basis for constantly challenging the status quo of a given allocative regime. In applying the eorem in dierent asset domains, the benets of the existing ‘circuit board of nancial intermediation’ are recognized and the practical challenges of re-wiring an ingrained conguration are highlighted. e equity chapter pair can be thought of as exploring the optimal nancing arrangements for ‘nancing the future’, ranging from the incremental improvements to everyday human life through soware-enabled cloud applications to the radical technological moonshots. In other words, the equity nancing paper pair revolves around the question of how to optimally run the technological and social experiments of technology startups. In applying the eorem to technology startups, the thesis asks us to consider which circuit board of nancial arrangements is optimally positioned to nance technological progress and innovation – the rm or the market. Both the rm and the market allocation can provide a laboratory, yet with very dierent properties that may be more or less desirable from a policy perspective to optimally foster such innovation. On the one hand, opening the nancing of startups to the market appears more democratic, giving everyday users a vote and economic participation in technological progress. However, as the DotCom bubble two decades ago or more recently the initial coin oering (ICO) bubble have demonstrated, the market-based allocation of early startups subjects them to short-term thinking in quarterly report cycles and is prone to overshooting in times of booms and busts. Perez(2002) has argued that such equity market bubbles followed by crashes may be a healthy and natural property of every technological revolution, as that there are two phases, (i) the installation phase where a technology enter the market and inated prices reect the general excitement around the technology and (ii) the deployment phase when the technology is broadly adopted, with the ‘turning point’ typically being marked by a nancial crash and recovery. On the other hand, the allocation through specialized venture rms may allow for more patient capital, which is less prone to overshooting and understanding of the bumpy road of technological progress. As the recent decades have been characterized by a retrenchment of startups from public markets to specialized capital providers, venture rms have emerged as the dominant form of nancing technological progress. rough the enactment of the JOBS Act, it has been aempted to revert this secular trend and induce startups to re-emerge from the shadows of security laws. However, as this thesis has demonstrated, the advantages of the venture rm allocation go beyond the ‘mere’ provision of capital, ranging from the value of the signal, to advisory roles and hands-on business support

255 with hiring and ‘go to market’. As a result, the dominance of the venture rms has increased further over the past years, despite the active aempts of lawmakers and the SEC to re-introduce startups to a more market-based allocation. is thesis has explored a number of novel technology-enabled means to further strengthen the role of the market in nancing startups, in particular through ground truth data layers that allow for passive and cost-ecient disclosures and novel pooled nancing vehicles. However, the thesis is also cognizant of the fact that, given the complexity and idiosyncratic nature of such startups, they will always remain prone to some level of nancial intermediation, even in the presence of deregulation. Whereas the equity chapter pair can be thought of as exploring the optimal ‘nancing of the future’, the credit chapter pair tries to establish the optimal nancing arrangement of the ‘status quo’. In other words, it is concerned with nancing established economic activity: real property, cash ow positive projects and creditworthy borrowers. e equity paper pair is aspirational and can be thought of as sketching out an optimal design for nancing the ‘R&D department of society’. In contrast, the credit paper pair is wholly functional and of macroeconomic importance in the present, given the potential systemic risks associated with the default of the banking rms. In this respect, the thesis explores a fully market-based, distributed credit system, which allows for an unbundling of credit from monolithic banking organizations. While market-based credit systems have been subject to much innovation over the past decades, through asset-backed securitization by incumbent banking rms and grassroots innovation from nascent peer-to-peer lending platforms, banking rms still maintain a dominant position due to legal, technical and economic advantages enjoyed by centralized rms. is thesis explores a range of technical and legal means through which this dominant position of the banking rm can be substituted by more ecient market mechanisms. While rapidly decreasing marginal costs of information production and dissemination make the origination and secondary trading of small-lot sized loans a technical reality, the legal system is still geared towards a traditional bank-based intermediation architecture. What is required to level the playing eld between the bank and the market for commoditized credit assets, such as mortgages and consumer loans, is a ‘rst principles thinking’ approach by the regulator that starts from the individual transactions and considers the entire cost structure of the respective allocation mode. Even where credit marketplaces are technically enabled, implicit or explicit legal guarantees granted to banking rms may distort market signals. In the end, neither a fully market-based credit allocation, nor a fully bank-based credit allocation may be optimal. However, a system that allows for the dynamic experimentation with dierent modes of allocation is well-placed to encourage actors to strive towards innovation and foster the most ecient solution. Overall, this PhD thesis aims to sharpen our understanding of the ways in which security laws have grown up to govern capital markets and how exemptions from this legal regime or industry-specic bank regulations create incentives for the allocation of credit and equity claims through rms. is analysis is critical for the understanding of the challenges of reforming securities regulation and fostering ecient capital markets. e current regulatory architecture ‘front loads’ burdensome disclosure costs and was not designed to accommodate the market-based allocation of smaller issuers — be it early stage startups or retail borrowers. Since no single theoretical frame exists or suces that fully captures the benets and costs of security laws, this thesis has aempted to sketch out a comprehensive theory that is deeply rooted in the foundational law and economics literature. Recognizing the limits of establishing a novel theoretical frame and being aware that there exists no ‘easy x’, the hope is that this thesis can advance our understanding of the unique set of challenges at play in regulating modern capital markets.

256 Bibliography

500 Startups. (2014). KISS – Free legal docs for startup founders and investors. Retrieved from hps://500.co/kiss Abdullah, S. (2018). What Percentage Do Venture Capitalists Take? Average VC Ownership of 105 Tech Companies. Retrieved from hps://about.crunchbase.com/blog/venture-capitalist-percentage-ownership Accel Partners. (2000). Team Biographies. Acharya, V., Hasan, I., & Saunders, A. (2006). Should Banks Be Diversied? Evidence from Individual Bank Loan Portfolios. e Journal of Business, 79(3), 1355–1412. Acharya, V. V., Schnabl, P., & Suarez, G. (2013). Securitization without risk transfer. Journal of Financial Economics, 107(3), 515 - 536. Ackermann, A. (2019). Regulators See Nonbank Mortgage Firms as Potential Risk to U.S. Financial Sys- tem. Retrieved from hps://www.wsj.com/articles/regulators-see-nonbank-mortgage-rms-as-potential-risk-to -u-s-nancial-system-11575498244 Admati, A., & Hellwig, M. (2013). e Bankers’ New Clothes: What’s Wrong with Banking and What to Do about It (1st ed.). Princeton University Press. Admati, A., & Peiderer, P. (1994). Robust Financial Contracting and the Role of Venture Capitalists. e Journal of Finance, 49(2), 371–402. Adrian, T., & Shin, H. S. (2010). e Changing Nature of Financial Intermediation and the Financial Crisis of 2007–2009. Annual Review of Economics, 2(1), 603-618. Afek, Y., Alon, N., Barad, O., Hornstein, E., Barkai, N., & Bar-Joseph, Z. (2011). A Biological Solution to a Fundamental Distributed Computing Problem. Science, 331(6014), 183–185. Afshar, C., & Rose, P. (2007). Capital Markets Competitiveness: A Survey of Recent Reports. Entrepreneurial Business Law Journal, 2(1), 439-478. Aguirre, G. (2003). Enron Decision: Closing the Fraud-Free Zone on Errant Gatekeepers. Delaware Journal of Corporate Law, 28(2), 447-512. Akerlof, G. A. (1970). e Market for ’Lemons’: ality Uncertainty and the Market Mechanism. e arterly Journal of Economics, 84(3), 488-500. Aldasoro, I., & Faia, E. (2016). Systemic loops and liquidity regulation. Journal of Financial Stability, 27, 1 - 16. Aldridge, I., & Krawciw, S. (2017). Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading and Flash Crashes (1st ed.). John Wiley & Sons. Alexy, O., Block, J., Sandner, P., & Wal, A. (2012). Social Capital of Venture Capitalists and Start-up Funding. Small Business Economics, 39(4), 835-851. Allen, F., & Gale, D. (1990). Diversity of opinion and nancing of new technologies. Journal of Financial Intermediation, 8(1-2), 68-89. Allen, F., & Gale, D. (1997). Financial Markets, Intermediaries, and Intertemporal Smoothing. Journal of Political Economy, 105(3), 523–546. Allen, F., & Gale, D. (2000a). Bubbles and crises. e Economic Journal, 110(460), 236–255. Allen, F., & Gale, D. (2000b). Financial Contagion. Journal of Political Economy, 108(1), 1–33. Allen, L. (1988). e Determinants of Bank Interest Margins: A Note. e Journal of Financial and antitative Analysis, 23(2), 231–235. Amor, S. B., & Kooli, M. (2020). Do MA exits have the same eect on venture capital reputation than IPO exits? Journal of Banking Finance, 111, 105704. Anand, A. I. (2003). e Eciency of Direct Public Oerings. Journal of Small and Emerging Business Law, 7(3), 433-466. Andersen, L., Hager,¨ D., Maberg, S., Næss, M., & Tungland, M. (2012). e nancial crisis in an operational risk management context—A review of causes and inuencing factors. Reliability Engineering & System Safety, 105, 3 - 12. Angbazo, L. (1997). Commercial bank net interest margins, default risk, interest-rate risk, and o-balance sheet banking. Journal of Banking Finance, 21(1), 55 - 87. Anita, B. (2017). is awkward exchange between analysts and Snap CEO Evan Spiegel explains Wall Street’s frustration with the stock. Retrieved from hps://www.cnbc.com/2017/08/11/snap-earnings-call-evan-spiegel-and-unmuted -hot-mic-analyst-transcript.html

257 Aoki, M. (2000). Information and Governance in the Silicon Valley Model. In X. Vives (Ed.), Corporate governance: eoretical and empirical perspectives (p. 169-195). Cambridge University Press. Armour, J. (2014, February). Making Bank Resolution Credible. ECGI Working Paper Series in Law. Armour, J., & Enriques, L. (2018). e promise and perils of crowdfunding: Between corporate nance and consumer contracts. e Modern Law Review, 81(1), 51-84. Arrow, K. J. (1969). e Organization of Economic Activity: Issues Pertinent to the Choice of Market versus Non-Market Allocation. In In the analysis and evaluation of public expenditures: e pbb-system, joint economic commiee, 91st cong., 1st sess. Arya, S., Eckel, C., & Wichman, C. (2013). Anatomy of the credit score. Journal of Economic Behavior Organization, 95, 175 - 185. Avgouleas, E., & Goodhart, C. (2015, 02). Critical Reections on Bank Bail-ins. Journal of Financial Regulation, 1(1), 3-29. Axtell, R. (2003). Economics as Distributed Computation. In T. Terano, H. Deguchi, & K. Takadama (Eds.), Meeting the Challenge of Social Problems via Agent-Based Simulation (pp. 3–23). Tokyo: Springer Japan. Ayres, I., & Curtis, Q. (2015). Beyond Diversication: e Pervasive Problem of Excessive Fees and Dominated Funds in 401(k) Plans. Yale Law Journal, 124(5), 1476-1553. Bagehot, W. (1873). Lombard Street: A Description of the Money Market (1st ed.). London: Henry S. King & Co. Baird, D., & Morrison, E. (2011). Dodd-Frank for Bankruptcy Lawyers. American Bankruptcy Institute Law Review, 19(2), 287-318. Bajtelsmit, V. L., & Worzala, E. M. (1995). Real Estate Allocation in Pension Fund Portfolios. e Journal of Real Estate Portfolio Management, 1(1), 25–38. Baker, D. (2000). Who Wants to Be a Millionaire. ABA Journal, 86(2), 36-43. Baltensperger, E. (1972). Economies of Scale, Firm Size, and Concentration in Banking. Journal of Money, Credit and Banking, 4(3), 467–488. Bank of International Selement (BIS). (2004). International Convergence of Capital Measurement and Capital Stan- dards. Bank of International Selement (BIS). (2010). Basel III: A global regulatory framework for more resilient banks and banking systems. Bank of International Selement (BIS). (2011). Basel Commiee issues nal elements of the reforms to raise the quality of regulatory capital. Bank of International Selement (BIS). (2013). FinTech credit. Market structure, business models and nancial stability implications. Retrieved from hp://www.bis.org/publ/cgfs fsb1.pdf Bank of International Selement (BIS). (2014). Supervisory framework for measuring and controlling large exposures. Bank of International Selement (BIS). (2017). Basel III: e Liquidity Coverage Ratio and liquidity risk monitoring tools. Barabasi, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512. Baremetrics. (2020). Open Startup Initiative. Retrieved from hps://baremetrics.com/open-startups Bar-Gill, O. (2009). e Law, Economics and Psychology of Subprime Mortgage Contracts. Cornell Law Review, 94(5), 1073-1152. Barras, L., Scaillet, O., & Wermers, R. (2010). False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas. Journal of Finance, 65(1), 179-216. Barry, C., Muscarella, C., Peavy, J., & Vetsuypens, M. (1990). e Role of Venture Capital in the Creation of Public Companies: Evidence from the Going-public Process. Journal of Financial Economics, 27(2), 447-471. Barucci, E., Colozza, T., & Milani, C. (2019). e eect of bank bail-outs in the EU. Journal of International Money and Finance, 95, 14 - 26. Basak, S., & McBride, S. (2019). At Private Silicon Valley Summit, No Love for IPOs or Banks. Retrieved from hps:// www.bloomberg.com/news/articles/2019-10-02/at-private-summit-in-silicon-valley-no-love-for-ipos-or-banks Batista, E. (2014). e Shot in the Dark: An Analysis of the SEC’s Response to the Rise of Dark Pools. Journal of High Technology Law, 14(1), 83-119. Bay, D. L. (2011). Dodd-Frank’s Requirement of Skin in the game for Asset-Backed Securities May Scalp Corporate Loan Liquidity. North Carolina Banking Institute, 15, 13-46. Bauguess, S., Gullapalli, R., & Ivanov, V. (2018). Capital Raising in the U.S.: An Analysis of the Market for Unregistered Securities Oerings, 2009–2017. Retrieved from hps://www.sec.gov/dera/sta-papers/white-papers/dera white paper regulation d 082018 Bayar, O., & Chemmanur, T. (2012). What drives the valuation premium in IPOs versus acquisitions? An empirical analysis. Journal of Corporate Finance, 18(3), 451 - 475. Bayar, O., & Chemmanur, T. J. (2011). IPOs versus Acquisitions and the Valuation Premium Puzzle: A eory of Exit Choice by Entrepreneurs and Venture Capitalists. Journal of Financial and antitative Analysis, 46(6), 1755–1793. Bebchuk, L. (2010). e Wages of Failure: Executive Compensation at Bear Stearns and Lehman 2000-2008. Yale Journal on Regulation, 27(2), 257-282.

258 Bebchuk, L., & Kastiel, K. (2017). e Untenable Case for Perpetual Dual-Class Stock. Virginia Law Review, 103(4), 585-630. Beccalli, E., Boitani, A., & Giuliantonio, S. D. (2015). Leverage pro-cyclicality and securitization in US banking. Journal of Financial Intermediation, 24(2), 200 - 230. Beck, T., Demirguc-Kunt, A., & Levine, R. (2006). Bank concentration, competition, and crises: First results. Journal of Banking Finance, 30(5), 1581 - 1603. Bell, K. (2018). Highly condential’ documents reveal Facebook used VPN app to track competitors. Retrieved from hps://mashable.com/article/facebook-used-onavo-vpn-data-to-watch-snapchat-and-whatsapp Benchmark Capital. (2000). Team Biographies. Bencivenga, V., & Smith, B. (1985). Credit markets and the control of capital. Journal of Money, Credit and Banking, 17, 133-52. Bencivenga, V., & Smith, B. (1991). Financial intermediation and endogenous growth. Review of Economics Studies, 58(2), 195-209. Ben-David, I., Franzoni, F., & Moussawi, R. (2018). Do ETFs Increase Volatility? e Journal of Finance, 73(6), 2471-2535. Benet Street Partners. (2019). Franklin BSP Private Credit Fund. Retrieved from hps://www.sec.gov/Archives/edgar/ data/1794041/000110465919065105/tm1923034-2 n2.htm Beneish, M. D., & Whaley, R. E. (1996). An Anatomy of the “S & P Game”: e Eects of Changing the Rules. e Journal of Finance, 51(5), 1909–1930. Benjamin, E., & Geradin, D. (2016). Eciencies and Regulatory Shortcuts: How Should We Regulate Companies Like Airbnb and Uber. Stanford Technology Law Review, 19(2), 293-328. Ben-Shahar, O., & Schneider, C. (2011). e failure of mandated discourse. University of Pennsylvania Law Review, 70(4), 647-750. Ben-Shahar, O., & Schneider, C. (2014). More an You Wanted to Know: e Failure of Mandated Disclosure (1st ed.). Princeton Press. Berdejo, C. (2015). Going Public Aer the JOBS Act. Ohio State Law Journal, 76(1), 1-66. Berger, P. G., & Ofek, E. (1995). Diversication’s eect on rm value. Journal of Financial Economics, 37(1), 39-65. Berndt, A., Hollield, B., & Sandas, P. (2012). e role of mortgage brokers in the subprime crisis. In M. Carey, A. Kashyap, R. Rajan, & R. Stulz (Eds.), Market institutions and nancial market risk. Elsevier, Journal of Financial Economics. Bernile, G., Cumming, D., & Lyandres, E. (2007). e size of venture capital and private equity fund portfolios. Journal of Corporate Finance, 13(4), 564 - 590. (Private Equity, Leveraged Buyouts and Corporate Governance) Bertaut, C., DeMarco, L., Kamin, S., & Tryon, R. (2012). ABS inows to the United States and the global nancial crisis. Journal of International Economics, 88(2), 219 - 234. Bessemer Venture Partners. (2011). Bessemer Venture Partners Closes $1.6 Billion Global Fund. Retrieved from hps:// www.businesswire.com/news/home/20110401005328/en/Bessemer-Venture-Partners-Closes-1.6-Billion-Global Bhaacharya, S., & akor, A. V. (1993). Contemporary Banking eory. Journal of Financial Intermediation, 3(1), 2 - 50. Bhide, A. (1993). e hidden costs of stock market liquidity. Journal of Financial Economics, 34(1), 31-51. Black, B., & Gilson, R. (1998). Venture capital and the structure of capital markets: banks versus stock markets. Journal of Financial Economics, 47(3), 243 - 277. Black, F. (1972). Capital Market Equilibrium with Restricted Borrowing. e Journal of Business, 45(3), 444–455. Black, F., Miller, M. H., & Posner, R. A. (1978). An Approach to the Regulation of Bank Holding Companies. e Journal of Business, 51(3), 379–412. Blackrock. (2014). Corporate bond market structure: the time for reform is now. Retrieved from hps://www.blackrock .com/corporate/literature/whitepaper/viewpoint-corporate-bond-market-structure-september-2014.pdf Blake, D., Lehmann, B. N., & Timmermann, A. (1999). Asset Allocation Dynamics and Pension Fund Performance. e Journal of Business, 72(4), 429–461. Block, J., & Sandner, P. (2009). What is the Eect of the Current Financial Crisis on Venture Capital Financing? Empirical Evidence from US Internet Start-ups. Venture Capital, 11(4), 295-309. Bob, Z. (1998). How venture capital works. Harvard Business Review, 76(6), 131-136. Boot, A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7 - 25. Boot, A., Greenbaum, S., & akor, A. (1993). Reputation and discretion in nancial contracting. American Economic Review, 83, 1165-83. Boot, A., & akor, A. (1997). Financial system architecture. Review of Financial Studies, 10, 693-733. Bower, N. L. (1989). Firm Value and the Choice of Oering Method in Initial Public Oerings. e Journal of Finance, 44(3), 647–662. Bradford, S. (2012). Crowdfunding and the Federal Securities Laws. Columbia Business Law Review, 2012(1), 1-150. Brasoveanu, A. (2016). 10 Marketplace KPIs at Maer. Retrieved from hps://medium.com/@algovc/10-marketplace -kpis-that-maer-22e0fd2d2779 Brophy, D., & Guthner, M. (1988). Publicly traded venture capital funds: implications for institutional “fund of funds” investors. Journal of Business Venturing, 3(3), 187 - 206.

259 Brunnermeier, M. (2009). Deciphering the Liquidity and Credit Crunch 2007-2008. Journal of Economic Perspectives, 23(1), 77-100. Brunnermeier, M., & Oehmke, M. (2013). e Maturity Rat Race. e Journal of Finance, 68(2), 483-521. Bubb, R., & Pildes, R. H. (2014). How Behavioral Economics Trims Its Sails and Why. Harvard Law Review, 127(6), 1593-1679. Buchak, G., Matvos, G., Piskorski, T., & Seru, A. (2018). Fintech, regulatory arbitrage, and the rise of shadow banks. Journal of Financial Economics, 130(3), 453-483. Buchanan, J. M., & Stubblebine, W. C. (1962). Externality. Economica, 29(116), 371-384. Buchner, A., Mohamed, A., & Schwienbacher, A. (2017). Diversication, risk, and returns in venture capital. Journal of Business Venturing, 32(5), 519 - 535. Burt, R. (1992). Structural Holes: e Social Structure of Competition. Harvard University Press. Burt, R. (2005). Brokerage and closure: an introduction to social capital. Oxford University Press. Burt, R., Kildu, M., & Tasselli, S. (2013, 01). Social network analysis: Foundations and frontiers on advantage. Annual review of psychology, 64, 527-47. Bushman, R., Piotroski, J., & Smith, A. (2004, 02). What Determines Corporate Transparency? Journal of Accounting Research, 42, 207-252. Butenko, A., & Larouche, P. (2015). Regulation for Innovativeness or Regulation of Innovation? Law, Innovation and Technology, 7(1), 52-82. Buti, S., Rindi, B., & Werner, I. M. (2017). Dark pool trading strategies, market quality and welfare. Journal of Financial Economics, 124(2), 244 - 265. Cai, N., Helwege, J., & Warga, A. (2007). Underpricing in the Corporate Bond Market. e Review of Financial Studies, 20(6), 2021–2046. Calabresi, G. (1970). e Costs of Accidents (1st ed.). Yale University Press. Calacanis, J. (2017). Angel: How to Invest in Technology Startups (1st ed.). Harper Business. Calomiris, C. (2013). Reforming Banks Without Destroying eir Productivity and Value. Journal of Applied Corporate Finance, 25(4), 14-20. Calomiris, C., & Kahn, C. (1991a). e Role of Demandable Debt in Structuring Optimal Banking Arrangements. e American Economic Review, 81(3), 497–513. Calomiris, C., & Kahn, C. (1991b). e Role of Demandable Debt in Structuring Optimal Banking Arrangements. e American Economic Review, 81(3), 497–513. Camp, J. (2002). Venture Capital Due Diligence: A Guide to Making Smart Investment Choices and Increasing Your Portfolio Returns [Book]. John Wiley & Sons: New Jersey. Carhart, M. (1997). On Persistence in Mutual Fund Performance. Journal of Finance, 52(1), 57-82. Carrion, A. (2013). Very fast money: High-frequency trading on the NASDAQ. Journal of Financial Markets, 16(4), 680 - 711. (High-Frequency Trading) Carver, L. (2011). Venture Capital Valuation: Case Studies and Methodology (1st ed.). Wiley Finance. Casamaa, C. (2003). Financing and advising: Optimal nancial contracts with venture capitalists. e Journal of Finance, 58(5), 2059-2085. Castillo, M. (2018). How Spotify’s direct listing is dierent from an IPO. Retrieved from hps://www.cnbc.com/2018/ 04/03/how-does-spotify-direct-listing-work.html Chakin, M., & Buhayar, N. (2016). How Lending Clubs’ Biggest Fanboy Uncovered Shady Loans. Retrieved from hps:// www.bloomberg.com/news/features/2016-08-18/how-lending-club-s-biggest-fanboy-uncovered-shady-loans Chan, Y.-S., & Mak, K.-T. (1985). Depositors’ Welfare, Deposit Insurance, and Deregulation. e Journal of Finance, 40(3), 959–974. Chandrasekhar, A., Gordon, D., & Navlakha, S. (2018, 12). A distributed algorithm to maintain and repair the trail networks of arboreal ants. Scientic Reports, 8. Chemla, G. (2004). Pension Fund Investment in Private Equity and Venture Capital in the U.S. and Canada. e Journal of Private Equity, 7(2), 64–71. Chen, H.-C., & Rier, J. R. (2002). e Seven Percent Solution. e Journal of Finance, 55(3), 1105-1131. Chen, R.-R., Chidambaran, N., Imerman, M. B., & Sopranzei, B. J. (2014). Liquidity, leverage, and Lehman: A structural analysis of nancial institutions in crisis. Journal of Banking Finance, 45, 117 - 139. Clayton, J. (2019). Asset-Level Disclosure Requirements for Residential Mortgage-Backed Securities. Securities and Exchange Commission (SEC). Retrieved from hps://www.sec.gov/news/public-statement/clayton-rmbs-asset -disclosure CNBC. (2020). How Elon Musk Took Tesla To Hell And Back With e Model 3 [Documentary]. Coase, R. (1937). e Nature of the Firm. Economica, 4(16), 386-405. Coase, R. (1960). e Problem of Social Cost. Journal of Law and Economics, 3(1), 1-44. Coee, J. (1984). Fiy Years of Federal Securities Regulation: Symposium on Contemporary Problems in Securities Regulation . Virginia Law Review, 70(4), 717-753. Coee, J. (2013). e Challenge of the Semi-Public Company. e CLS Blue Sky Blog. Retrieved from hps://clsbluesky .law.columbia.edu/2013/04/01/the-challenge-of-the-semi-public-company/# edn2

260 Coee, J., & Sale, H. (2009). Redesigning the SEC: Does the Treasury Have a Beer Idea. Virginia Law Review, 95(4), 707-784. Coggin, D., Fabozzi, F., & Rahman, S. (1993). e Investment Performance of U.S. Equity Pension Fund Managers: An Empirical Investigation. e Journal of Finance, 48(3), 1039–1055. Commiee on Oversight and Government Reform (COGR). (2008). Credit Rating Agencies and the Financial Crisis. Retrieved from hps://www.govinfo.gov/content/pkg/CHRG-110hhrg51103/html/CHRG-110hhrg51103.htm Conrad, J., Johnson, K. M., & Wahal, S. (2003). Institutional trading and alternative trading systems. Journal of Financial Economics, 70(1), 99 - 134. Constine, J., & Ferenstein, G. (2013). SEC Lis Ban On General Solicitation, Allowing Startups To Advertise at ey’re Fundraising. Retrieved from hps://techcrunch.com/2013/07/10/sec-general-solicitation-ban-lied Copeland, A., Martin, A., & Walker, M. (2014). Repo runs: Evidence from the tri-party repo market. e Journal of Finance, 69(6), 2343–2380. Coren, M. (2018). Tesla going private is the ultimate Silicon Valley move. Retrieved from hps://qz.com/1353463/ tesla-going-private-is-the-ultimate-silicon-valley-move Cornelli, F., & Yosha, O. (2003). Stage Financing and the Role of Convertible Securities. e Review of Economic Studies, 70(1), 1–32. Cortese, A. (2013). Seeking Capital, Some Companies Turn to ‘Do-It-Yourself I.P.O.’s’. Retrieved from hps://www.nytimes.com/2013/08/01/business/smallbusiness/seeking-capital-some-companies-turn-to-do -it-yourself-ipos.html Council of Institutional Investors. (2019). Email to SEC re solicitation of comments on the Long-Term Stock, Inc. Retrieved from hps://www.sec.gov/comments/10-234/10234-4844313-177202.pdf Cox, J. (2018). Musk’s bizarre earnings call was ‘the most unusual’ in this Morgan Stanley analyst’s 20-year career. Retrieved from hps://www.cnbc.com/2018/05/03/musk-earnings-call-most-unusual-in-morgan-stanley-analysts -career.html Coyle, J. F., & Green, J. (2014). Contractual Innovation in Venture Capital. Hastings Law Journal, 66, 133-183. Crunchbase. (2020). Glossary of Funding Types. Retrieved from hps://support.crunchbase.com/hc/en-us/articles/ 115010458467-Glossary-of-Funding-Types Cumming, D., & Dai, N. (2011). Fund size, limited aention and valuation of venture capital backed rms. Journal of Empirical Finance, 18(1), 2 - 15. Cumming, D., & MacIntosh, J. (2004). Boom, Bust, and Litigation in Venture Capital Finance. Willamee Law Review, 40(4), 867-906. Cummins, J. D., & Westereld, R. (1981). Paerns of Concentration in Private Pension Plan Common Stock Portfolios since ERISA. e Journal of Risk and Insurance, 48(2), 201–219. Dambra, M., Field, L. C., & Gustafson, M. T. (2015). e JOBS Act and IPO volume: Evidence that disclosure costs aect the IPO decision. Journal of Financial Economics, 116(1), 121 - 143. Daniel, K., Grinbla, M., Titman, S., & Wermers, R. (1997). Measuring Mutual Fund Performance with Characteristic- Based Benchmarks. e Journal of Finance, 52(3), 1035-1058. Daniels, K. N., & Vijayakumar, J. (2007). Does underwriter reputation maer in the municipal bond market? Journal of Economics and Business, 59(6), 500 - 519. Darcy, D. (2009). Credit Rating Agencies and the Credit Crisis: How the Issuer Pays Conict Contributed and What Regulators Might Do about It. Columbia Business Law Review, 2009(2), 605-668. Davis, E. P., Liadze, I., & Piggo, R. (2019). Assessing the macroeconomic impact of alternative macroprudential policies. Economic Modelling, 80, 407 - 428. de Fontenay, E. (2017). e Deregulation of Private Capital and the Decline of the Public Company. Hastings Law Journal, 68(3), 445-502. Deku, S., & Kara, A. (2017). A Historical and Regional Overview of Securitization. In Securitization: Past, Present and Future (pp. 57–91). Del Negro, M., Giannone, D., Giannoni, M., & Tambaloi, A. (2019). Global trends in interest rates. Journal of Interna- tional Economics, 118, 248 - 262. DeLong, J. B., & Magin, K. (2006). A short note on the size of the dot-com bubble (Working Paper No. 12011). National Bureau of Economic Research. Derrien, F., & Womack, K. L. (2003). Auctions vs. Bookbuilding and the Control of Underpricing in Hot IPO Markets. e Review of Financial Studies, 16(1), 31-61. Dewatripont, M. (2014). European banking: Bailout, bail-in and state aid control. International Journal of Industrial Organization, 34, 37 - 43. Dewatripont, M., & Tirole, J. (1993). e prudential regulation of banks (1st ed., Vol. 1). e MIT Press. Diamond, D. (1984, November). Financial intermediation and delegated monitoring. Review of Economic Studies, 20(51), 393-414. Diamond, D. (1991). Debt Maturity Structure and Liquidity Risk. e arterly Journal of Economics, 106(3), 709–737. Diamond, D., & Dybvig, P. (1983a). Bank Runs, Deposit Insurance, and Liquidity. Journal of Political Economy, 91(3), 401–419.

261 Diamond, D., & Dybvig, P. (1983b). Bank Runs, Deposit Insurance, and Liquidity. Journal of Political Economy, 91(3), 401–419. Diamond, D., & Verrecchia, R. (1991). Disclosure, Liquidity, and the Cost of Capital. e Journal of Finance, 46(4), 1325–1359. Dietrich, A., & Wanzenried, G. (2011). Determinants of bank protability before and during the crisis: Evidence from Switzerland. Journal of International Financial Markets, Institutions and Money, 21(3), 307 - 327. Dimitrova, L. (2017). Perverse incentives of special purpose acquisition companies, the “poor man’s private equity funds”. Journal of Accounting and Economics, 63(1), 99 - 120. Dobbie, W., Goldsmith-Pinkham, P., & Yang, C. (2017). Consumer Bankruptcy and Financial Health. e Review of Economics and Statistics, 99(5), 853-869. D’Onfro, J. (2015). Travis Kalanick: Uber Is ’Nowhere Near’ Going Public. Retrieved from hps://www.inc.com/ business-insider/uber-not-close-to-going-public.html Dugid, K. (2019). MarketAxess earnings miss, revenue solid as bond traders go electronic. Re- trieved from hps://www.reuters.com/article/marketaxess-results/marketaxess-earnings-miss-revenue-solid-as -bond-traders-go-electronic-idUSL2N24O145 Dunn, K., & McConnell, J. (1981). Valuation of GNMA Mortgage-Backed Securities. e Journal of Finance, 36(3), 599-616. Easterbrook, F. H., & Fischel, D. R. (1984). Mandatory Disclosure and the Protection of Investors. Virginia Law Review, 70(4), 669-716. EBK. (2008). SFBC – UBS Subprime Report. Retrieved from hps://www.nma.ch/FinmaArchiv/ebk/e/publik/ medienmit/20081016/ubs-subprime-bericht-ebk-e.pdf Edwards, A., Harris, L., & Piwowar, M. (2007). Corporate bond market transaction costs and transparency. e Journal of Finance, 62(3), 1421–1451. Eliot, M. F., Brown, & Anupreeta, D. (2019). WeWork Co-Founder Has Cashed Out at Least $ 700 Million Via Sales, Loans. Retrieved from hps://www.wsj.com/articles/wework-co-founder-has-cashed-out-at-least-700-million-from-the -company-11563481395 Emily Chang (Producer). (2016). Vinod Khosla. Bloomberg Studio 1.0 [Audio podcast]. Retrieved from hps://www .bloomberg.com/podcasts/studio 1 Eng, E., Frank, R., & Lyn, E. (2013). Finding best execution in the dark: Market fragmentation and the rise of dark pools. Journal of International Business and Law, 12(1), 39-50. Entrop, O., Memmel, C., Ruprecht, B., & Wilkens, M. (2015). Determinants of bank interest margins: Impact of maturity transformation. Journal of Banking Finance, 54, 1 - 19. Erdogan, B., Kant, R., Miller, A., & Sprague, K. (2016). Grow fast or die slow: Why unicorns are staying private. Retrieved from hps://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/ grow-fast-or-die-slow-why-unicorns-are-staying-private Essaghoolian, N. (2019). Initial Coin Oerings: Emerging Technology’s Fund- raising Innovation. UCLA Law Review, 66(1), 294-344. European Central Bank (ECB). (2013, October). A macro stress testing framework for assessing systemic risks in the banking sector (Occasional Paper Series No. 152). European Central Bank. Fabozzi, F. (2016). Mortgage-backed Handbook (7th ed.). Oxford University Press. Fama, E. F. (1970). Ecient Capital Markets: A Review of eory and Empirical Work. e Journal of Finance, 25(2), 383–417. Fannie Mae. (2019). Fannie Mae Eligibility Matrix. Retrieved from hps://singlefamily.fanniemae.com/media/20786/ display Farruggio, C., Michalak, T. C., & Uhde, A. (2013). e light and dark side of TARP. Journal of Banking Finance, 37(7), 2586 - 2604. Federal Reserve Bank of St. Louis. (2020). Net Interest Margin for all U.S. Banks. Feldman, A. (2007). Puing Founders First – – How one VC rm coddles its CEOs. Retrieved from hps://www.inc.com/ magazine/20070301/nance-raising-funds.html Fenwick, M., Kaal, W., & Vermeulen, E. (2017). Regulation Tomorrow: What Happens When Technology Is Faster than the Law. American University Business Law Review, 6(3), 561-594. Fisch, J., & Sale, H. (2003). e Securities Analyst as Agent: Rethinking the Regulation of Analysts. Iowa Law Review, 88(5), 1035-1098. Fost, D. (2003). Louis Borders: Older and wiser Webvan founder eases back into the fray with Web content venture. Retrieved from hps://www.sfgate.com/business/article/Louis-Borders-Older-and-wiser-Webvan-founder -2575518.php Freeman, L. C. (1979). Centrality in Social Networks: Conceptual clarication. Social Networks, 1(3), 215–239. Freixas, X., & Parigi, B. (1998). Contagion and eciency in gross and net interbank payment systems. Journal of Financial Intermediation, 7(1), 3 - 31. Freixas, X., & Rochet, J. (1997). Microeconomics of banking (1st ed., Vol. 1). e MIT Press. Freixas, X., & Rochet, J. (2008). Microeconomics of Banking (2nd ed.). e MIT Press.

262 Friend, T. (2015). Tomorrow’s Advance Man – Marc Andreessen’s plan to win the future. Retrieved from hps:// www.newyorker.com/magazine/2015/05/18/tomorrows-advance-man Frischmann, B., & Marciano, A. (2015). Understanding e Problem of Social Cost. Journal of Institutional Economics, 11(2), 329–352. Fujimoto, K., Chou, C.-P., & Valente, T. (2011, 07). e Network Autocorrelation Model using Two-mode Data: Aliation Exposure and Potential Bias in the Autocorrelation Parameter. Social networks, 33, 231-243. Gai, P., Haldane, A., & Kapadia, S. (2011). Complexity, concentration and contagion. Journal of Monetary Economics, 58(5), 453 - 470. Gai, P., & Kapadia, S. (2010a). Contagion in nancial networks. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 466(2120), 2401-2423. Gai, P., & Kapadia, S. (2010b). Contagion in Financial Networks. Proceedings of the Royal Society A, 466(2120), 2401-2423. Gao, P., Schultz, P., & Song, Z. (2017). Liquidity in a Market for Unique Assets: Specied Pool and To-Be-Announced Trading in the Mortgage-Backed Securities Market. e Journal of Finance, 72(3), 1119-1170. Gao, X., Rier, J., & Zhu, Z. (2012). Where have all the IPOs gone? Retrieved from hp://www.sec.gov/info/smallbus/ acsec/acsec-090712-rier-slides.pdf Gao, X., Rier, J., & Zhu, Z. (2013). Where Have All e IPOs Gone? Journal of Financial and antitative Analysis, 48(6), 1663–1692. Garcia, T., Chau, M., & Spiteri, P. (2011). Synchronous and Asynchronous Distributed Computing for Financial Option Pricing. In B. Murgante, O. Gervasi, A. Iglesias, D. Taniar, & B. O. Apduhan (Eds.), Computational Science and Its Applications - ICCSA 2011 (pp. 664–679). Garten, H. A. (1989). Regulatory Growing Pains: A Perspective on Bank Regulation in a Deregulatory Age. Fordham Law Review, 57(4), 501-577. Gerding, E. (2009). Code, Crash, and Open Source: e Outsourcing of Financial Regulation to Risk Models and the Global Financial Crisis. Washington Law Review, 84(2), 127-198. Gerding, E. (2016a). e Dialectics of Bank Capital: Regulation and Regulatory Capital Arbitrage. Washington Law Journal, 55(2), 357-384. Gerding, E. (2016b). Disclosure 2.0: Can technology solve overload, complexity, and other information failures. Tulane Law Review, 90(5), 1143-1180. Gersbach, H., & Rochet, J.-C. (2012). Aggregate Investment Externalities and Macroprudential Regulation. Journal of Money, Credit and Banking, 44, 73–109. Gil, E. (2018). High Growth Handbook [Book]. Stripe Press. Gillespie, C. (2015). Fiing Heavy Tailed Distributions: e poweRlaw Package. Journal of Statistical Soware, 64(2), 1–16. Gilson, R., & Kraakman, R. (1984). e Mechanisms of Market Eciency. Virginia Law Review, 70(4), 549-644. Gilson, R. J. (2003). Engineering a Venture Capital Market: Lessons from the American Experience. Stanford Law Review, 55(4), 1067-1104. Gompers, P., & Lerner, J. (1996). e Use of Covenants: An Empirical Analysis of Venture Partnership Agreements. e Journal of Law & Economics, 39(2), 463–498. Gonzalez, G. (2018). Giant Companies Are Scooping Up Startups Before eir IPOs. What Silicon Valley Wishes Would Happen Instead. Inc. Magazine. Retrieved from hps://www.inc.com/guadalupe-gonzalez/why-fewer-companies -are-going-public-acquisition-ltse.html Gopinath, P. G., & Vasudevan, S. K. (2015). An In-depth Analysis and Study of Load Balancing Techniques in the Cloud Computing Environment. Procedia Computer Science, 50, 427 - 432. (Big Data, Cloud and Computing Challenges) Gordon, E. (2014). e JOBS Act Trojan Horse: A Gi to Startups with Something Else Inside? Michigan Journal of Private Equity Venture Capital Law, 3(2), 213-230. Gordon, J., & Kornhauser, L. (1985). Ecient Markets, Costly Information, and Securities Research. New York University Law Review, 60(5), 761-849. Gornall, W., & Strebulaev, I. A. (2020). Squaring venture capital valuations with reality. Journal of Financial Economics, 135(1), 120 - 143. Goscinski, A. (1991). Distributed Operating Systems: e Logical Design (1st ed.). USA: Addison-Wesley Longman Publishing Co., Inc. Goshen, Z., & Parchomovsky, G. (2006). e Essential Role of Securities Regulation. Duke Law Journal, 55(4), 711-782. Green, R. C., & Talmor, E. (1986). Asset substitution and the agencycosts of debt nancing. Journal of Banking Finance, 10(3), 391 - 399. Grith, E. (2020). Notion, Maker of Collaboration Soware, Raises $50 Million. Retrieved from hps://www.nytimes .com/2020/04/01/technology/notion-startup-fund-raising.html Grinter, R. E., Herbsleb, J. D., & Perry, D. E. (1999). e geography of coordination: Dealing with distance in rd work. In Proceedings of the international acm siggroup conference on supporting group work (p. 306–315). Gudzowski, M. (2010). Mortgage Credit Ratings and the Financial Crisis: e Need for a State-Run Mortgage Security Credit Rating Agency. Columbia Business Law Review, 2010(1), 245-280.

263 Guercio, D. D., & Reuter, J. (2014). Mutual Fund Performance and the Incentive to Generate Alpha. e Journal of Finance, 69(4), 1673-1704. Guzman, J., & Stern, S. (2015). Where is Silicon Valley? Science, 347(6222), 606-609. Hackernoon. (2018). Hacker Noon - e Premier Independent Tech Publication. Retrieved from hps://www.startengine .com/hackernoon Haeberle, K. (2018). A New Market-Based Approach to Securities Law. University of Chicago Law Review, 85(6), 1313- 1394. Haeberle, K., & Henderson, T. (2018). Making a Market for Corporate Disclosure. Yale Journal on Regulation, 35(2), 383-436. Hannan, M., Burton, D., & Baron, J. (1996, 03). Inertia and Change in the Early Years: Employment Relations in Young, High Technology Firms. Industrial and Corporate Change, 5(2), 503-536. Hansmann, H., & Pargendler, M. (2014). e Evolution of Shareholder Voting Rights: Separation of Ownership and Consumption. Yale Law Journal, 123(4), 948-1013. Harris, L., & Gurel, E. (1986). Price and Volume Eects Associated with Changes in the S&P 500 List: New Evidence for the Existence of Price Pressures. e Journal of Finance, 41(4), 815–829. Harris, R., Jenkinson, T., & Kaplan, S. (2014). Private Equity Performance: What Do We Know? e Journal of Finance, 69(5), 1851-1882. Harry Stebbings (Producer). (2020). Vinod Khosla on What Venture Assistance Really Means,. e Twenty Minute VC [Audio podcast]. Retrieved from hps://thetwentyminutevc.com/vinodkhosla Hartzmark, M. (2011). Fraud on the Market: Analysis of the Eciency of the Corporate Bond Market. Columbia Business Law Review, 2011(3), 654-716. Healy, P. M., & Palepu, K. G. (2001). Information asymmetry, corporate disclosure, and the capital markets: A review of the empirical disclosure literature. Journal of Accounting and Economics, 31(1), 405 - 440. Hellmann, T. (1998). e Allocation of Control Rights in Venture Capital Contracts. e RAND Journal of Economics, 29(1), 57–76. Hellmann, T. (2006). IPOs, acquisitions, and the use of convertible securities in venture capital. Journal of Financial Economics, 81(3), 649 - 679. Hellwig, M. (1991). Banking, nancial intermediation and corporate nance (Giovannini, A. et al., Eds.) (1st ed.). Cambridge University Press. Heyman, S. (2015). Rethinking Regulation Fair Disclosure and Corporate Free Speech. Cardozo Law Review, 36(3), 1099-1148. Hirtle, B. (2007). e impact of network size on bank branch performance. Journal of Banking & Finance, 31(12), 3782 - 3805. Hmida, M., & Brahmi, M. B. (2016). Bond Markets and Financial Stability: Evidence from the Asian Experience. Inter- national Journal of Econometrics and Financial Management, 4(1), 17–28. Ho, T., & Saunders, A. (1981). e Determinants of Bank Interest Margins: eory and Empirical Evidence. e Journal of Financial and antitative Analysis, 16(4), 581–600. Hochberg, Y., Ljungqvist, A., & Lu, Y. (2007). Whom You Know Maers: Venture Capital Networks and Investment Performance. Journal of Finance, 62(1), 251-301. Homann, M., & Nitschka, T. (2012). Securitization of mortgage debt, domestic lending, and international risk sharing. e Canadian Journal of Economics / Revue canadienne d’Economique, 45(2), 493–508. Holiday, A. (2015). Seing e Right Valuation For A Competitive Series A Roun. Retrieved from hps://techcrunch.com/ 2015/07/31/seing-the-right-valuation-for-a-competitive-series-a-round Holmstrom, B., & Tirole, J. (1993). Market liquidity and performance monitoring. Journal of Political Economy, 101(4), 678-709. Horvat, E.-A., & Zweig, K. (2012). One-mode Projection of Multiplex Bipartite Graphs. In Proc. 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) (p. 599-606). Hsu, D. H. (2004). What Do Entrepreneurs Pay for Venture Capital Aliation? Journal of Finance, 59(4), 1805-1844. Hu, H. (2012). Too Complex to Depict - Innovation, Pure Information, and the SEC Disclosure Paradigm. Texas Law Review, 90(7), 1601-1716. Huber, D. (2016). e Myth of Protecting the Public Interest: e Case of the Missing Mandate in Federal Securities Law. Journal of Business and Securities Law, 16(2), 401-424. Huberman, B. (1998). Computation as economics. Journal of Economic Dynamics and Control, 22(8), 1169 - 1186. Huberman, B., & Hogg, T. (1995). Distributed Computation as an Economic System. e Journal of Economic Perspectives, 9(1), 141–152. Hurdle, S. (2005). A Blow to Public Investing: Reforming the System of Private Equity Fund Disclosures. UCLA Law Review, 53(1), 239-278. Hurt, C. (2005). Moral hazard and the initial public oering. Cardozo Law Review, 26(2), 711-790. Huszar,´ Z., & Yu, W. (2019). Mortgage lending regulatory arbitrage: A cross-sectional analysis of nonbank lenders. Journal of Real Estate Research, 41(2), 219-248. Ibrahim, D. M. (2012). e New Exit in Venture Capital. Vanderbilt Law Review, 65(1), 1-48.

264 Ibrahim, D. M. (2015). Equity Crowdfunding: A Market for Lemons. Minnesota Law Review, 100(2), 561-608. Ilie, S., & Badica, C. (2013). Multi-agent approach to distributed ant colony optimization. Science of Computer Program- ming, 78(6), 762 - 774. Insight Partners. (2018). e 10 Most Important SaaS Metrics for High-growth SaaS Companies. Retrieved from hps:// www.insightpartners.com/blog/the-10-most-important-saas-metrics-for-high-growth-saas-companies Ioannou, C. C., Gual, V., & Couzin, I. D. (n.d.). predatory sh select for coordinated collective motion in virtual prey. Ippolito, R. A., & Turner, J. A. (1987). Turnover, Fees and Pension Plan Performance. Financial Analysts Journal, 43(6), 16–26. iShares. (2014). iShares CMBS ETF. Retrieved from hps://www.ishares.com/ch/institutional/en/literature/prospectus/ p-ishares-cmbs-etf-10-31-ch.pdf Jang, H. J., Na, Y. H., & Zheng, H. (2018). Contingent convertible bonds with the default risk premium. International Review of Financial Analysis, 59, 77 - 93. Jason Calacanis (Producer). (2019). Robbed of Equity: Toptal with e Information’s Amir Efrati former employee Sco Rier. is Week in Startups, E972 [Audio podcast]. Retrieved from hps://thisweekinstartups.com/ e972-robbed-of-equity-toptal-denies-any-shares-to-employees-investors-amir-efrati-of-the-information-on -breaking-the-story-founder-taso-du-vals-stock-hoarding-unusual-tactics-changing-l Jason Calacanis (Producer). (2020). Cendana Capital’s Michael Kim on investing in Seed funds as a serial LP, Robinhood as an acquisition target, greatest Seed funds of all time more. is Week in Startups, E1075 [Audio podcast]. Retrieved from hps://thisweekinstartups.com/e1075-cendana-capitals-michael-kim-on-investing-in-seed-funds -as-a-serial-lp-robinhood-as-an-acquisition-target-greatest-seed-funds-of-all-time-more Jehane Noujaim and Chris Hegedus (Directors). (2001). Startup.com [Documentary]. Jensen, M., & Murphy, K. (1990). Performance pay and top-management incentives. Journal of Political Economy, 98(2), 225–264. Jensen, M. C. (1968). e Performance of Mutual Funds in the Period 1945–1964. e Journal of Finance, 23(2), 389-416. Jensen, M. C. (1978). Some anomalous evidence regarding market eciency. Journal of Financial Economics, 6(2), 95 - 101. Jensen, M. C., & Meckling, W. H. (1976). eory of the rm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305 - 360. Jeong, S. (2016). Credit bureaus were the nsa of the 19th century. Retrieved from hps://www.theatlantic.com/ technology/archive/2016/04/mass-surveillance-was-invented-by-credit-bureaus/479226 Jeske, K., Krueger, D., & Mitman, K. (2013). Housing, mortgage bailout guarantees and the macro economy. Journal of Monetary Economics, 60(8), 917 - 935. Jiang, E. (2019). ’2.7 billion people can’t be wrong:’ Here’s what Wall Street is saying about Facebook earnings. Retrieved from hps://markets.businessinsider.com/news/stocks/facebook-stock-price-earnings-revenuewall-street-2019-1 -1027913555#jp-morgan-facebook-is-in-rareed-air-across-the-combination-of-scale-growth-and-protability-1 Jones, E., & Zeitz, A. (2017, 04). e Limits of Globalizing Basel Banking Standards. Journal of Financial Regulation, 3(1), 89-124. Jones, K. (2019). e Big Five: Largest Acquisitions by Tech Company. Retrieved from hps://www.visualcapitalist.com/ the-big-ve-largest-acquisitions-by-tech-company Jordan, J., Jin, L., Coolican, D., & Chen, A. (2020). 13 Metrics for Marketplace Companies. Retrieved from hps:// a16z.com/2020/02/21/marketplace-metrics Judge, K. (2012). Fragmentation Nodes: A Study in Financial Innovation, Complexity, and Systemic Risk. Stanford Law Review, 64(3), 657-726. Judge, K. (2014). ree Discount Windows. Cornell Law Review, 99(4), 795-856. Judge, K. (2019). Intermediary Inuence. University of Chicago Law Review, 82(2), 573-642. Kabbage. (2019). Kabbage Closes $ 700 Million Securitization. Kang, Q., He, H., & Deng, R. (2012). Bi-objective task assignment in heterogeneous distributed systems using honeybee mating optimization. Applied Mathematics and Computation, 219(5), 2589 - 2600. Kaplan, S. N., & Lerner, J. (2010). It ain’t broke: e past, present, and future of venture capital. Journal of Applied Corporate Finance, 22(2), 36-47. Kaplan, S. N., & Lerner, J. (2016, August). Venture Capital Data: Opportunities and Challenges [Book]. In Measuring Entrepreneurial Businesses: Current Knowledge and Challenges (p. 413-431). University of Chicago Press. Kashyap, A., Rajan, R., & Stein, J. (2002). Banks as Liquidity Providers: An Explanation for the Coexistence of Lending and Deposit-taking. e Journal of Finance, 57(1), 33-73. Kate, C. (2019). Zoom pops 81% in Nasdaq debut. Retrieved from hps://techcrunch.com/2019/04/18/zoom-pops-81-in -nasdaq-debut Keering, K. (2008). Securitization and Its Discontents: e Dynamics of Financial Product Development. Cardozo Law Review, 29(4), 1553-1728. Kevin, D. (2019). A group from Silicon Valley has a serious plan for creating a totally new US stock exchange. Retrieved from hps://qz.com/704657/eric-ries-ltse-long-term-stock-exchange

265 Kiran, S. (2019). Fannie and Freddie to be allowed to keep more capital. Retrieved from hps://www..com/content/ 20885890-e38b-11e9-9743-db5a370481bc Kitch, E. (1995). e eory and Practice of Securities Disclosure. Brooklyn Law Review, 61(3), 763-888. Kiyotaki, N., & Moore, J. (1997). Credit cycles. Journal of Political Economy, 105(2), 211-248. Klausner, M., & Litvak, K. (2001). What economists have taught us about venture capital contracting. In M. Whincop (Ed.), Bridging the entrepreneurial nancing gap: linking governance with regulatory policy (p. 54-74). Ashgate. Klausner, M., & Venuto, S. (2013). Liquidation Rights and Incentive Misalignment in Start-up Financing. Cornell Law Review, 98(6), 1399 - 1436. Knight, T. (2012). Panic, Prosperity, and Progress: Five Centuries of History and the Markets [Book]. Wiley & Sons: New Jersey. Knyazeva, A. (2016). Regulation A+: What Do We Know So Far? SEC Sta paper. Retrieved from hps://www.sec.gov/ dera/sta-papers/white-papers/18nov16 knyazeva regulation-a-plus-what-do-we-know-so-far.html Kobayashi, T. (2014). A model of nancial contagion with variable asset returns may be replaced with a simple threshold model of cascades. Economics Leers, 124(1), 113 - 116. Kokalitcheva, K. (2017). Social Capital says its ‘IPO 2.0’ is a beer process for unicorns. Re- trieved from hps://www.axios.com/social-capital-says-its-ipo-20-is-a-beer-process-for-unicorns-1513305505 -22b6c5b1-6ed2-4b47-890a-1e2d8361855b.html Kolb, J., & Tykvova,´ T. (2016). Going public via special purpose acquisition companies: Frogs do not turn into princes. Journal of Corporate Finance, 40, 80 - 96. Konrad, A. (2015). e Benchmark Way: Five Partners Who Make Other VC Firms Look Outgunned And Over- staed. Retrieved from hps://www.forbes.com/sites/alexkonrad/2015/03/25/benchmark-makes-other-rms-look -outmatched/#110c12174006 Konrad, A. (2019). Andreessen Horowitz is blowing up the venture capital model. Retrieved from hps://www.forbes.com/sites/alexkonrad/2019/04/02/andreessen-horowitz-is-blowing-up-the-venture-capital -model-again/#7dae66157d9f Kraakman, R., Armour, J., Davies, P., Enriques, L., Hansmann, H., Hertig, G., ... Rock, E. (2017). e Anatomy of Corporate Law: A Comparative and Functional Approach (3rd ed.). Oxford University Press. Kraakman, R. H. (1986). Gatekeepers: e Anatomy of a ird-Party Enforcement Strategy. Journal of Law, Economics, Organization, 2(1), 53-104. Kroger, J. (2005). Enron, Fraud, and Securities Reform: An Enron Prosecutor’s Perspective. University of Colorado Law Review, 76(3), 57-138. Krug, A. (2013). Escaping Entity-Centrism in Financial Services Regulation. Columbia Law Review, 113(8), 2039-2116. Kshemkalyani, A. D., & Singhal, M. (2008). Distributed Computing: Principles, Algorithms, and Systems (1st ed.). USA: Cambridge University Press. Kupor, S. (2019). Secrets of Silicon Valley (1st ed.). Virgin Books. Kwak, J. (2013). Improving Retirement Savings Options for Employees. University of Pennsylvania Journal of Business Law, 15(2), 483-540. Kwon, S., Lowry, M., & Qian, Y. (2020). Mutual fund investments in private rms. Journal of Financial Economics, 136(2), 407 - 443. Lakonishok, J., Shleifer, A., & Vishny, R. W. (1992). e Structure and Performance of the Money Management Industry. Brookings Papers on Economic Activity, 23, 339-391. Lang, L. H. P., & Stulz, R. M. (1994). Tobin’s q, Corporate Diversication, and Firm Performance. Journal of Political Economy, 102(6), 1248–1280. Langevoort, D. (1997). Toward More Eective Risk Disclosure for Technology-Enhanced Investing. Washington Univer- sity Law arterly, 75(2), 753-778. Larman, C. (2001). Protected variation: the importance of being closed. IEEE Soware, 18(3), 89-91. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. Lee, E. (2014). Basel III and Its New Capital Requirements, as Distinguished from Basel II. Banking Law Journal, 131(1), 27-69. Lee, T. (2014). e IPO is dying. Marc Andreessen explains why. Leenders, R. (2002). Modeling social inuence through network autocorrelation: constructing the weight matrix. Social Networks, 24(1), 21 - 47. Lending Club. (2019). Form S-3 Registration Statement under the Securities Act of 1933. Lenzu, S., & Tedeschi, G. (2012). Systemic risk on dierent interbank network topologies. Physica A: Statistical Mechanics and its Applications, 391(18), 4331 - 4341. Lerner, J. (1994). e syndication of venture capital investments. Financial Management, 23(3), 16–27. Leau, M., & Madhavan, A. (2018). Exchange-Traded Funds 101 for Economists. e Journal of Economic Perspectives, 32(1), 135–154. Levin, D., Wundsam, A., Heller, B., Handigol, N., & Feldmann, A. (2012). Logically Centralized? State Distribution Trade-Os in Soware Dened Networks. In Proceedings of the rst workshop on hot topics in soware dened networks (p. 1–6). New York, NY, USA: Association for Computing Machinery.

266 Levine, P., & Li, J. (2020). Open Source: From Community to Commercialization. Retrieved from hps://a16z.com/2019/ 10/04/commercializing-open-source Levine, R. (1991). Stock markets, growth, and tax policy. Journal of Finance, 46(4), 1445-65. Levitin, A. (2011). Skin-in-the-Game: Risk Retention Lessons from Credit Card Securitization. George Washington Law Review, 81(3), 813-855. Li, D. X. (2000). On default correlation. e Journal of Fixed Income, 9(4), 43–54. Lieberman, E., Hauert, C., & Nowak, M. (2005). Evolutionary dynamics on graphs. Nature, 433, 312 - 316. Lindsey, L. (2002). e Venture Capital Keiretsu Eect: an Empirical Analysis of Strategic Alliances among Portfolio Firms [SIEPR policy paper No. 02-017]. Lintner, J. (1965). e Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. e Review of Economics and Statistics, 47(1), 13–37. Lo, A., & Mueller, M. (2010). Warning: physics envy may be hazardous to your wealth! Journal of Investment Manage- ment, 8(2), 13 - 63. Logue, K., & Slemrod, J. (2010). Of Coase, Calabresi, and Optimal Tax Liability. Tax Law Review, 63(4), 797-866. Loizos, C. (2019). Carta was just valued at $ 1.7 billion by Andreessen Horowitz, in a deal some see as rich. Re- trieved from hps://techcrunch.com/2019/05/06/carta-was-just-valued-at-1-7-billion-by-andreessen-horowitz-in -a-deal-some-see-as-rich Loizos, C. (2020). Bill Gurley is stepping away from an active role at Benchmark, 21 years aer joining the rm. Retrieved from hps://techcrunch.com/2020/04/22/report-bill-gurley-is-stepping-away Lynch, A., & Mendenhall, R. (1997). New Evidence on Stock Price Eects Associated with Changes in the SP 500 Index. e Journal of Business, 70(3), 351-83. MacKenzie, D., & Spears, T. (2014). ’e formula that killed Wall Street’: e Gaussian copula and modelling practices in investment banking. Social Studies of Science, 44(3), 393–417. Mahoney, P. G. (1995). Mandatory Disclosure as a Solution to Agency Problems. Chicago Law Review, 62(3), 1047-1112. Mahoney, P. G. (2001). e Political Economy of the Securities Act of 1933. e Journal of Legal Studies, 30(1), 1-31. Mandelker, G., & Raviv, A. (1977). Investment banking: an economic analysis of optimal underwriting contracts. e Journal of Finance, 32(3), 683-694. Manju, D. (2018). e New Floor for Bond Underwriting Fees: $ 1. Retrieved from hps://www.wsj.com/articles/ the-new-oor-for-bond-underwriting-fees-1-1514973603 Mann, R. (1997). Explaining the Paern of Secured Credit. Harvard Law Review, 110(3), 625-683. Marchant, G., Allenby, B., & Herkert, J. (2011). e Growing Gap Between Emerging Technologies and Legal-Ethical Oversight. Springer. Mariathasan, M., & Merrouche, O. (2014). e manipulation of basel risk-weights. Journal of Financial Intermediation, 23(3), 300 - 321. Marko, J. (n.d.). google irts; investors wonder about date. Markowitz, H. (1952). Portfolio Selection. e Journal of Finance, 7(1), 77-91. Markowitz, H. (1959). Portfolio Selection: Ecient Diversication of Investments. Yale University Press. Massa, A., & Barr, A. (2019). Why FPOs Are Taking the Wind Out of Tech IPOs. Retrieved from hps://www.bloomberg .com/news/articles/2019-11-01/why-fpos-are-taking-the-wind-out-of-tech-ipos-quicktake Massa, A., Barr, A., & McBride, S. (2019). Bloomberg BusinessweekSign InSubscribe e Big Money in Startups Comes From Investing Before the IPO. Retrieved from hps://www.bloomberg.com/news/articles/2019-10-22/the-big -money-in-startups-comes-from-investing-before-the-ipo Masulis, R., & Nahata, R. (2011). Venture Capital Conicts of Interest: Evidence from Acquisitions of Venture-Backed Firms. e Journal of Financial and antitative Analysis, 46(2), 395–430. Maudos, J., Pastor, J. M., Perez,´ F., & esada, J. (2002). Cost and prot eciency in European banks. Journal of International Financial Markets, Institutions and Money, 12(1), 33 - 58. McBride, S. (2014). With WhatsApp deal, Sequoia Capital burnishes reputation. Retrieved from hps://www.reuters .com/article/us-whatsapp-facebook-sequoia-idUSBREA1K04720140221 McClure, D. (2015). 99 VC Problems But A Batch Ain’t One: Why Portfolio Size Maers For Returns. Retrieved from hps://500hats.com/99-vc-problems-but-a-batch-ain-t-one-why-portfolio-size-maers-for-returns-16cf556d4af0 McCulloch, J. (1985). Interest-risk sensitive deposit insurance premia: Stable ACH estimates. Journal of Banking Finance, 9(1), 137 - 156. McDonald, R. L. (2013). Contingent capital with a dual price trigger. Journal of Financial Stability, 9(2), 230 - 241. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27(1), 415-444. Mendelson, M. (2019). From Initial Coin Oerings to Securities Tokens: A U.S. Federal Securities Law Analysis. Stanford Technology Law Review, 22(1), 52-94. Menkveld, A. (2013). High frequency trading and the new market makers. Journal of Financial Markets, 16(4), 712 - 740. (High-Frequency Trading) Merton, R. (1974). On the pricing of corporate debt: e risk structure of interest rates. e Journal of Finance, 29(2), 449-470.

267 Merton, R. K. (1968). e Mahew Eect in Science. Science, 159(3810), 56–63. Milcheva, S. (2013). Cross-country eects of regulatory capital arbitrage. Journal of Banking Finance, 37(12), 5329 - 5345. Retrieved from hp://www.sciencedirect.com/science/article/pii/S037842661300229X Mindus, D., & Wessel, M. (2017). Do Ex-Startup Founders Make e Best Venture Capitalists? Retrieved from hps:// www.cbinsights.com/research/founders-best-venture-capitalist-investors Mizruchi, M. S., & Neuman, E. J. (2008). e eect of density on the level of bias in the network autocorrelation model. Social Networks, 30(3), 190 - 200. Morley, J. (2018). Why Do Investment Funds Have Special Securities Regulation? In W. Birdthistle & J. Morley (Eds.), Research Handbook of Mutual Funds (Vol. 1, p. 9-21). Edward Elgar Publishing. Morrison, E. (2009). Is the Bankruptcy Code an Adequate Mechanism for Resolving the Distress of Systemically Impor- tant Institutions. Temple Law Review, 82(2), 449-464. Murphy, E. (2015). Who Regulates Whom and How? An Overview of U.S. Financial Regulatory Policy for Banking and Securities Markets. Congressional Research Service Paper. Retrieved from hps://fas.org/sgp/crs/misc/R43087.pdf Murphy, H. (2019). DoJ drops investigation into Snap. Retrieved from hps://www..com/content/7ad78f78-f5a6-11e9 -9ef3-eca8fc8f2d65 Nadauld, T. D., Sensoy, B. A., Vorkink, K., & Weisbach, M. S. (2019). e liquidity cost of private equity investments: Evidence from secondary market transactions. Journal of Financial Economics, 132(3), 158 - 181. Nahata, R. (2008). Venture capital reputation and investment performance. Journal of Financial Economics, 90(2), 127 - 151. Nauman, B. (2020). Top us pension plans eye private credit. Retrieved from hps://www..com/content/f7584069-b74a -4039-84f2-fdbdefd11728 Navlakha, S., & Bar-Joseph, Z. (2014, 12). Distributed Information Processing in Biological and Computational Systems. Communications of the ACM, 58, 94-102. Nelson, R. (2015). Breaking the Bank: Why FDIC and NCUA Deposit Insurance Coverage Fails to Trigger OFCCP’S Jurisdiction. Florida State University Business Review, 14(1), 53-108. Neuman, B. C. (1994). Scale in distributed systems. In Readings in distributed computing systems (pp. 463–489). IEEE Computer Society Press. Newman, M. (2001, Jul). Clustering and preferential aachment in growing networks. Physical Review E, 64(2). Newman, M., Barabasi, A.-L., & Was, D. (2006). e structure and dynamics of networks. Princeton University Press. Nguyen, J. (2012). e relationship between net interest margin and noninterest income using a system estimation approach. Journal of Banking Finance, 36(9), 2429 - 2437. Niamh Moloney, J. P., Eil´ıs Ferran. (2015). e Oxford Handbook of Financial Regulation (1st ed.). Oxford University Press. Nicholas, T. (2019). VC: An American History [Book]. Havard University Press: Cambridge. Nickerson, B. J. (2019). e Underlying Underwriter: An Analysis of the Spotify Direct Listing. University of Chicago Law Review, 86(4), 985-1026. Nier, E., Yang, J., Yorulmazer, T., & Alentorn, A. (2007). Network Models and Financial Stability. Journal of Economic Dynamics and Control, 31(6), 2033-2060. Norton, E., & Tenenbaum, B. H. (1993). Specialization versus diversication as a venture capital investment strategy. Journal of Business Venturing, 8(5), 431 - 442. NVCA. (2011). NVCA Yearbook 2011. Retrieved from hps://nvca.org/ NVCA. (2019). NVCA Yearbook 2019. Retrieved from hps://nvca.org/wp-content/uploads/2019/08/NVCA-2019 -Yearbook.pdf Obstfeld, M. (1994). Risk-taking, global diversication, and growth. e American Economic Review, 84(5), 1310–1329. O’Hara, M., & Shaw, W. (1990). Deposit Insurance and Wealth Eects: e Value of Being “Too Big to Fail”. e Journal of Finance, 45(5), 1587–1600. Ohlrogge, M., & Giesecke, K. (2016). Can the SEC’s Net Capital Rule Really Explain e Growth of Mortgage Securiti- zation? [Stanford Law School Working paper]. Oranburg, S. C. (2018). Hyperfunding: Regulating Financial Innovations. University of Colorado Law Review, 89(4), 1033-1100. Osipvich, A. (2019). SEC Rejects NYSE Plan to Expand Direct Listings. Retrieved from hps://www.wsj.com/articles/ sec-rejects-nyse-plan-to-expand-direct-listings-11575674476 Osterberg, W. (1996). e Impact of Depositor Preference Laws. Federal Reserve Bank of Cleveland Economic Review, 32(3), 2–11. Paez, A., Sco, D. M., & Volz, E. (2008). Weight matrices for social inuence analysis: An investigation of measurement errors and their eect on model identication and estimation quality. Social Networks, 30(4), 309 - 317. Pagano, M. (1989, 05). Trading Volume and Asset Liquidity. e arterly Journal of Economics, 104(2), 255-274. Pagnoncelli, B. K., Cifuentes, A., & Denis, G. (2017). A two-step hybrid investment strategy for pension funds. e North American Journal of Economics and Finance, 42, 574 - 583. Palmiter, A. (1999). Toward Disclosure Choice in Securities Oerings. Columbia Business Law Review, 1999(1), 1-136. Parlour, C. A., & Rajan, U. (2001). Competition in Loan Contracts. e American Economic Review, 91(5), 1311–1328.

268 Parnas, D. (1972). On the Criteria to Be Used in Decomposing Systems into Modules. , 15(12), 1053–1058. Patrick O’Shaughnessy (Producer). (2019a). Alex Mial – Early Stage Investing. Invest Like the Best, EP.119 [Audio podcast]. Retrieved from hp://investoreldguide.com/mial Patrick O’Shaughnessy (Producer). (2019b). Bill Gurley – Direct Listing vs. IPO. Invest Like the Best, EP.144 [Audio podcast]. Retrieved from hp://investoreldguide.com/directlisting Perez, C. (2002). Technological Revolutions and Financial Capital. Edward Elgar. Phalippou, L., & Goschalg, O. (2009). e Performance of Private Equity Funds. e Review of Financial Studies, 22(4), 1747–1776. Pinter, A. (2003). A Star Is Rebooted. Retrieved from hps://observer.com/2003/03/a-star-is-rebooted/ Pla, E., Kruppa, M., & Fontanella-Khan, J. (2019). WeWork founder Neumann raised $700m through share sales and loans. Retrieved from hps://www..com/content/076aec74-a9b1-11e9-984c-fac8325aaa0 Pollman, E. (2019). Startup Governance. University of Pennsylvania Law Review, 169. Posner, R. A. (2010). From the new institutional economics to organization economics: with applications to corporate governance, government agencies, and legal institutions. Journal of Institutional Economics, 6(1), 1–37. Price, D. (1976). A general theory of bibliometric and other cumulative advantage processes. Journal of the American Society for Information Science, 27(5), 292-306. Primack, D. (2011a). Greylock joins the growth equity land-grab. Retrieved from hps://fortune.com/2011/03/02/ greylock-joins-the-growth-equity-land-grab Primack, D. (2011b). Notes on the Khosla Ventures fundraise. Retrieved from hps://fortune.com/2011/10/17/notes-on -the-khosla-ventures-fundraise Prohs, E. (2002). Periodic Financial Reporting - A Relic of the Past. Journal of Corporation Law, 27(3), 481-498. Qian, X., Jiang, L., Xu, C., & Wu, S. (2012). Explicit formulas for pricing of callable mortgage-backed securities in a case of prepayment rate negatively correlated with interest rates. Journal of Mathematical Analysis and Applications, 393(2), 421 - 433. Radner, R. (1985). Repeated principal-agent games with discounting. Econometrica, 53(5), 1173–1198. Rajan, R. (1992). Insiders and outsiders: e choice between informed and arm’s-length debt. e Journal of Finance, 47(4), 1367-1400. Rajan, R., & Zingales, L. (1998). Which capitalism? lessons from the east asian crisis. Journal of Applied Corporate Finance, 11(1), 40-48. Ramakrishnan, R., & akor, A. (1984, November). Information reliability and a theory of nancial intermediation. Review of Economic Studies, 3(51), 415-432. Rameden, D. (1995). When the Database is Wrong.. Do Consumers Have Any Eective Remedies against Credit Report- ing Agencies or Information Providers. Commercial Law Journal, 100(3), 390 - 426. Ramsinghani, M. (2018). e Business of Venture Capital (2nd ed.). John Wiley & Sons. Reader, R. (2015). Lending Platform Kabbage raises $ 135m to expand its technology. Retrieved from hps://venturebeat .com/2015/10/14/lending-platform-kabbage-raises-135m-to-expand-its-technology Rennison, J. (2019). Banks launch platform to propel bond issuance into electronic age. Retrieved from hps://www. .com/content/a16307b2-eb9c-11e9-a240-3b065ef5fc55 Repullo, R., & Suarez, J. (2004, 03). Venture Capital Finance: A Security Design Approach. Review of Finance, 8(1), 75-108. Richard Carnell, J. M., & Miller, G. (2013). e Law of Financial Institutions (Aspen Casebook) (5th ed.). Wolters Kluwer. Ries, E. (2011). e Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses (1st ed.). New York:Crown Business. Rier, J. R. (2019). Initial public oerings: Median age of ipos through 2018. Retrieved from hps://site.warrington.u .edu/rier/les/2019/03/IPOs2018Age.pdf Rier, J. R. (2020). Big IPO Runups of 1975-2019, as of January 31, 2020. Retrieved from hps://site.warrington.u.edu/ rier/les/2020/02/Runup7519.pdf Rodrigues, U. (2013). Securities Law’s Dirty Lile Secret. Fordham Law Review, 81(6), 3389-3438. Rodrigues, U. (2015). e Once and Future Irrelevancy of Section 12(g). University of Illinois Law Review, 4, 1529-1562. Rogers, B. (2015). e Social Costs of Uber. University of Chicago Law Review Dialogue, 82, 85-102. Romano, R. (1998). Empowering Investors: A Market Approach to Securities Regulation. Yale Law Journal, 107(8), 2359-2430. Romano, R. (2001). e Need for Competition in International Securities Regulation. eoretical Inquiries in Law, 2(2), 387-562. Ronn, E. I., & Verma, A. K. (1986). Pricing Risk-Adjusted Deposit Insurance: An Option-Based Model. e Journal of Finance, 41(4), 871–895. Roof, K. (2015). NASDAQ Acquires SecondMarket To Help Startups Sell Shares. Retrieved from hps://techcrunch.com/ 2015/10/22/nasdaq-acquires-secondmarket-to-help-startups-sell-shares Roof, K. (2017a). Grammarly raises $110 million for a beer spell check. Retrieved from hps://techcrunch.com/2017/ 05/08/grammarly-raises-110-million-for-a-beer-spell-check

269 Roof, K. (2017b). IVP announces $1.5 billion venture fund. Retrieved from hps://techcrunch.com/2017/09/26/ivp -announces-1-5-billion-venture-fund Roof, K. (2017c). Social Capital to take startups public without an IPO. Retrieved from hps://techcrunch.com/2017/09/ 14/social-capital-to-take-startups-public-without-an-ipo Rosas, G. (2006). Bagehot or bailout? an analysis of government responses to banking crises. American Journal of Political Science, 50(1), 175–191. Rosenstein, J. (1988). e board and strategy: Venture capital and high technology. Journal of Business Venturing, 3(2), 159 - 170. Roso, M. (2016). Je Bezos told what may be the best startup investment story ever. Retrieved from hps://www .businessinsider.com/je-bezos-on-early-amazon-investors-2016-10?r=US&IR=T Rowley, J. (2018). Inside the rise of and reign of supergiant venture capital rounds. Retrieved from hps://techcrunch .com/2018/07/22/inside-the-rise-and-reign-of-supergiant-venture-capital-rounds Sahlman, W. A. (1990). e structure and governance of venture-capital organizations. Journal of Financial Economics, 27(2), 473 - 521. Saunders, A., Srinivasan, A., & Walter, I. (2002). Price formation in the OTC corporate bond markets: a eld study of the inter-dealer market. Journal of Economics and Business, 54(1), 95 - 113. Schaefer, S. (2015). Geing Into e Unicorn Boom: 10 Mutual Funds With Stakes In Pre-IPO Tech Stars. Retrieved from hps://www.forbes.com/sites/steveschaefer/2015/10/14/unicorns-funds-delity-trowe-uber -dropbox/#716664a2a92d Schapiro, M. (2010). Testimony Concerning the Lehman Brothers Examiner’s Report by Mary Schapiro. Retrieved from hps://www.sec.gov/news/testimony/2010/ts042010mls.htm Schmidt, K. M. (2003). Convertible Securities and Venture Capital Finance. e Journal of Finance, 58(3), 1139–1166. Schooner, H. M. (2016). Top-down Bank Capital Regulation. Washburn Law Journal, 55(2), 327-356. Schroth, E., Suarez, G. A., & Taylor, L. A. (2014). Dynamic debt runs and nancial fragility: Evidence from the 2007 ABCP crisis. Journal of Financial Economics, 112(2), 164 - 189. Schwab, S. J. (1993). Coase’s Twin Towers: e Relation between the Nature of the Firm and the Problem of Social Cost. Journal of Corporation Law, 18(2), 359-370. Schwarcz, S. (2008). Disclosure’s Failure in the Subprime Mortgage Crisis. Utah Law Review, 2008(3), 1109-1122. Schwartz, A. (2019). Mandatory Disclosure in Primary Markets. Columbia Business Law Review, 2019(5), 1069-1104. Schwartz, J. (2012). e Twilight of Equity Liquidity. Tulane Law Review, 34(2), 531-608. Scitovsky, T. (1954). Two Concepts of External Economies. Journal of Political Economy, 62(2), 143-151. SEC. (1983). e Glass-Steagall Act and the Securities and Exchange Commission. Retrieved from hps://www.sec.gov/ news/speech/1983/032383treadway.pdf SEC. (2001). Denition of Terms in and Specic Exemptions for Banks, Savings Associations, and Savings Banks Under Sections 3(a)(4) and 3(a)(5) of the Securities Exchange Act of 1934. Retrieved from hps://www.sec.gov/rules/ nal/34-44291.htm#P201 33705 SEC. (2006). Electronic Filing and the EDGAR System: A Regulatory Overview. Retrieved from hps://www.sec.gov/ info/edgar/regoverview.htm SEC. (2009). Beginners’ Guide to Asset Allocation, Diversication, and Rebalancing. Retrieved from hps://www.sec .gov/reportspubs/investor-publications/investorpubsassetallocationhtm.html SEC. (2010). Concept Release on Equity Market Structure. Retrieved from hps://www.sec.gov/rules/concept/2010/ 34-61358.pdf SEC. (2012). Report on Authority to Enforce Exchange Act Rule 12g5-1 and Subsection (b)(3). Retrieved from hps:// www.sec.gov/les/authority-to-enforce-rule-12g5-1.pdf SEC. (2014). Asset-Backed Securities Disclosure and Registration. SEC. (2015). Crowdfunding: Final Rule. Retrieved from hps://www.sec.gov/rules/nal/2015/33-9974.pdf SEC. (2017). Investment Company Liquidity Risk Management Programs. Retrieved from hps://www.sec.gov/rules/ nal/2016/33-10233.pdf SEC. (2019a). Framework for “Investment Contract” Analysis of Digital Asset. Retrieved from hps://www.sec.gov/ les/dlt-framework.pdf SEC. (2019b). In the Maer of the Application of Long Term Stock Exchange, Inc. for Registration as a National Securities Exchange. Retrieved from hps://www.sec.gov/rules/other/2019/34-85828.pdf SEC. (2019c). Reg D exemption ling of Long Term Stock Exchange (LTSE). Retrieved from hps://www.sec.gov/ Archives/edgar/data/1680712/000168071219000002/xslFormDX01/primary doc.xml Securities Industry and Financial Markets Association (SIFMA). (2003). Comment on the third consultative paper on the new Basel Capital Accord. Retrieved from hp://www.sifma.org/regulatory/comment leers/comment leer archives/30530635.pdf Servaes, H. (1996). e Value of Diversication During the Conglomerate Merger Wave. e Journal of Finance, 51(4), 1201-1225. Sharepost. (2019a). Sharepost 100 Fund. Retrieved from hps://www.sec.gov/Archives/edgar/data/1557265/ 000114420415026639/v408557 posam.htm

270 Sharepost. (2019b). Sharepost Marketplace. Retrieved from hps://sharespost.com/marketplace Sharpe, W. (1964). Capital Asset Prices: A eory of Market Equilibrium under Conditions of Risk. e Journal of Finance, 19(3), 425-442. Shephard, G. (2019). Startups Must Get Creative as Early-Stage Investors Shi to Larger Deals. Retrieved from hps:// www.entrepreneur.com/article/335606 Sherman, A. G. (1992). e Pricing of Best Eorts New Issues. e Journal of Finance, 47(2), 781–790. Shin, H.-S. (2009). Reections on Northern Rock: e Bank Run at Heralded the Global Financial Crisis. e Journal of Economic Perspectives, 23(1), 101–120. Shleifer, A. (1986). Do Demand Curves for Stocks Slope Down? e Journal of Finance, 41(3), 579–590. Singhal, M., & Shivaratri, N. G. (1994). Advanced Concepts in Operating Systems. USA: McGraw-Hill, Inc. Sjostrom, W. (2001). Going Public through an Internet Direct Public Oering: A Sensible Alternative for Small Compa- nies. Florida Law Review, 53(3), 529-594. Sjostrom, W. (2011). estioning the 500 Equity Holders Trigger. Harvard Business Law Review online, 1, 43-46. Smith, G. (2005). e Exit Structure of Venture Capital. UCLA Law Review, 53(2), 669-716. Solomon, S. D. (2011). Facebook May Be Forced To Go Public amid Market Gloom. Retrieved from hp://dealbook .nytimes.com/2011/11/29/facebook-may-be-forced-to-go-public-amid-market-gloom/? r=0 Sorenson, O., & Stuart, T. (2001). Syndication Networks and the Spatial Distribution of Venture Capital Investments. American Journal of Sociology, 106(6), 1546-1588. Spence, D. (2008). Can Law Manage Competitive Energy Markets. Cornell Law Review, 93(4), 765-818. Spindler, J. (2009). How Private Is Private Equity, and at What Cost. University of Chicago Law Review, 76(1), 311-334. Stadtfeld, C., Takacs, K., & Voros, A. (2020). e Emergence and Stability of Groups in Social Networks. Social Networks, 60, 129 - 145. (Social Network Research on Negative Ties and Signed Graphs) Staord, P. (2019). Tradeweb lines up new benchmark for $16tn Treasuries marke. Retrieved from hps://www..com/ content/e0d99112-d216-11e9-a0bd-ab8ec6435630 Stein, J. C. (1992). Convertible bonds as backdoor equity nancing. Journal of Financial Economics, 32(1), 3 - 21. Stout, L. (2003). e Mechanisms of Market Eciency: An Introduction to the New Finance. Journal of Corporation Law, 28(4), 635-670. Suster, M. (2011). Understanding Changes in the Soware & Venture Capital Industries. Retrieved from hps:// bothsidesohetable.com/understanding-changes-in-the-soware-venture-capital-industries-b69a7e3a1ec7 Suster, M. (2012). It’s Morning in Venture Capital. Retrieved from hps://bothsidesohetable.com/it-s-morning-in -venture-capital-ac539cd5f0e8 Swisher, K. (2017). Full Transcript: Venture Capitalist Jeremy Liew on Recode Decode. Retrieved from hps://www.vox .com/2017/3/5/14809104/transcript-venture-capitalist-jeremy-liew-innovation-silicon-valley-recode-decode Tanenbaum, A., & van Steen, M. (2007). Distributed Systems. Principles and Paradigms. (2nd ed.). Pearson Prentice Hall. ompson, C. (2014). Free advice: Don’t go public, says Palantir’s CEO. Retrieved from hps://www.cnbc.com/2014/ 03/19/free-advice-dont-go-public-says-palantirs-ceo.html Tinic¸, S. M. (1988). Anatomy of initial public oerings of common stock. e Journal of Finance, 43(4), 789-822. Tonks, I. (2005). Performance Persistence of Pension[U+2010]Fund Managers. e Journal of Business, 78(5), 1917–1942. Toumano, P. (1984). A Positive Analysis of the eory of Market Failure. Kyklos, 37(4), 529-541. Tuch, A. (2014). e Self-Regulation of Investment Bankers. George Washington Law Review, 83(1), 101-175. Tunguz, T. (2019). Redpoint Saas startup key metrics template. Retrieved from hps://tomtunguz.com/saas-startup -metrics-template Uri, N. (1985). An analysis of the multiple listing service. Socio-Economic Planning Sciences, 19(6), 399 - 405. Verstein, A. (2011). e Misregulation of Person-to-Person Lending. UC Davis Law Review, 45(2), 445-530. Villalonga, B. (2004). Diversication Discount or Premium? New Evidence from the Business Information Tracking Series. e Journal of Finance, 59(2), 479-506. Wales, B. (2020). Past Performance is No Guarantee for Future Results: Tik Tok and Its Rise rough the App Store. Retrieved from hps://medium.com/eventuresvc-news/past-performance-is-no-guarantee-for-future -results-b08c12b51e91 Wallman, S. (1997). e future of accounting and nancial reporting, part IV: “Access” Accounting. Accounting Horizons, 11(2), 103-116. Wang, J., Crawl, D., Purawat, S., Nguyen, M., & Altintas, I. (2015). Big data provenance: Challenges, state of the art and opportunities. In Proceedings of the 2015 ieee international conference on big data (big data) (p. 2509–2516). IEEE Computer Society. Warburton, J. (2013). Should Mutual Funds Be Corporations - A Legal (and) Econometric Analysis. Journal of Corporation Law, 33(3), 745-776. Wasserman, S., & Faust, K. (1997). Social Network Analysis : Methods and Applications. Cambridge University Press. Waters, R., & Foley, S. (2015). Silicon Valley: Inside the Winners’ Circle. e Financial Times. Retrieved from hps:// www..com/content/bf1a4890-1be1-11e5-8201-cbdb03d71480

271 Wenger, E., & Kaserer, C. (1998). e German system of corporate governance: A model which should not be imitated, in “Competition and Convergence in Financial Markets: e German and Anglo-American Models” (Stanley W. Black and Mathias Moersch, Eds) (1st ed.). North –Holland Press, New York. Werner, R. A. (2014a). Can banks individually create money out of nothing? — e theories and the empirical evidence. International Review of Financial Analysis, 36, 1 - 19. Werner, R. A. (2014b). How do banks create money, and why can other rms not do the same? An explanation for the coexistence of lending and deposit-taking. International Review of Financial Analysis, 36, 71 - 77. Werth, J., & Boeert, P. (2013). Co-investment Networks of Business Angels and the Performance of their Start-up Investments. International journal of entrepreneurial venturing, 5(3), 240-256. Wigglesworth, R. (2018). Share price records topple as Faangs power ahead. e Financial Times. Retrieved from hps://www..com/content/10dd53c2-74d4-11e8-aa31-31da4279a601 Wigglesworth, R. (2020). Tech stockpicker raises $6bn for hybrid fund. Retrieved from hps://www..com/content/ fc92c020-490f-11ea-aeb3-955839e06441 Williamson, O. (1971). e Vertical Integration of Production: Market Failure Considerations. e American Economic Review, 61(2), 112–123. Williamson, O. (1985). e economic institutions of capitalism (1st ed.). Yale University Press. Williamson, O. (1986). Economic Organization: Firms, Markets and Policy Control (1st ed.). New York University Press. Wilson, F. (2017). Reserves. Retrieved from hps://avc.com/2017/01/reserves Winnike, D. J., & Nordquist, C. (1993). Federal Securities Law Issues for the Sticky Oering. e Business Lawyer, 48(3), 869–887. Wright, J., & Vickery, J. (2013). TBA trading and liquidity in the agency MBS market. Economic Policy Review(may), 1-18. Wroldsen, J. (2017). Crowdfunding Investment Contracts. Virginia Law and Business Review, 11(3), 543-612. Wu, D., Zhu, L., Xu, X., Sakr, S., Sun, D., & Lu, Q. (2016). Building pipelines for heterogeneous execution environments for big data processing. IEEE Soware, 33(2), 60-67. Wurgler, J., & Zhuravskaya, E. (2002). Does Arbitrage Flaen Demand Curves for Stocks? e Journal of Business, 75(4), 583–608. Y Combinator. (2013). Safe Financing Documents. Retrieved from hps://www.ycombinator.com/documents Yang, H.-F., Liu, C.-L., & Chou, R. (2019). Bank diversication and systemic risk. e arterly Review of Economics and Finance. Yarow, J. (2013). A QA With Dave Goldberg On Why He Avoided An IPO For His Company, SurveyMonkey. Retrieved from hps://www.businessinsider.com/dave-golderg-surveymonkey-interview-2013-1?r=US&IR=T Yong, Y., & Fei-Yue, W. (2018). Blockchain and Cryptocurrencies: Model, Techniques, and Applications. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(9), 1421-1428. Zelinsky, E. (2004). e Dened Contribution Paradigm. Yale Law Journal, 114(3), 451-534. Zingales, L. (2009). e future of securities regulation. Journal of Accounting Research, 47(2), 391-425.

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