Impediments and Enhancements to the Flow of Capital and Information Through Financial Markets

Impediments and enhancements to the flow of capital and information through financial markets

Hamilton, Dennis https://iro.uiowa.edu/discovery/delivery/01IOWA_INST:ResearchRepository/12788582970002771?l#13788582960002771

Hamilton, D. (2020). Impediments and enhancements to the flow of capital and information through financial markets [University of Iowa]. https://doi.org/10.17077/etd.005602

- Impediments and Enhancements to the Flow of Capital and Information Through Financial Markets

Dennis Hamilton

A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Business Administration in the Graduate College of The University of Iowa

August 2020

Thesis Committee: Jarjisu Sa-Aadu, Thesis Supervisor Artem Durnev Jon Garfinkel Erik Lie Ashish Tiwari

ABSTRACT

In the first chapter, I examine the capital market consequences of a post-crisis banking regulation. Banks increased held-to-maturity (HTM) classifications by more than $600 billion between 2010 and 2016 despite binding sale restrictions that render HTM securities illiquid.

They accepted this friction in order to protect regulatory capital ratios from Basel III’s expanded marking to market of fixed income security portfolios. I find the unprecedented rise in restrictive

HTM classifications crowds out dealer inventories, resulting in constrained market making capacity and reduced liquidity provisions by banks. Ultimately, market liquidity worsens for securities most frequently classified as HTM. Contemporaneous regulations are ruled out through analyses of treated and control banks, dealers, asset classes and mortgage-backed securities.

In the second chapter, I examine NBA betting markets, a potentially cleaner laboratory for testing theories regarding insider trading and market quality. Disgraced referee Tim

Donaghy, who was indicted following the 2006-2007 season for betting on his own games, provides exogenous variation in insider trading as he was assigned to referee games involving every team in the league. Abnormal price behavior begins as soon as Donaghy’s information network expands, and market makers provide less liquidity once the insiders’ presence becomes apparent, as theory would predict. Market efficiency increases in price movements, suggesting that insider trading improves price discovery.

In the final chapter, my co-authors and I study why creditors simultaneously hold debt and equity in the same firm. We posit that holding both debt and equity can protect the value of debt which may be at risk for expropriation by stockholders. Using a regression discontinuity design and exogenous events that increase the probability of a wealth transfer, we find that

ii creditors respond by purchasing equity. Importantly, this effect is true for bondholders but not for lenders who are already protected via control rights.

iii PUBLIC ABSTRACT

I examine frictions and enhancements to the flow of capital and information through markets. In the first chapter, I identify a post-crisis regulation and its unintended consequence.

Banks are incentivized to lock bonds away on their balance sheets and discouraged from serving as a conduit through which those bonds trade between more natural buyers and sellers. In the second chapter, I examine betting prices for NBA games officiated by a crooked referee to test theories relating insider trading to market quality. I find such insider trading leads to abnormal price movement and less liquid markets but also contributes to more accurate market prices. In the final chapter, I show investors rebalance their holdings of debt and equity in firms to mitigate value-destroying conflicts between shareholders and creditors. These results provide evidence of investors, acting in their own self-interests, catalyzing a self-correcting mechanism that smooths market frictions.

iv TABLE OF CONTENTS

LIST OF TABLES ...... vii

LIST OF FIGURES ...... ix

CHAPTER 1: WHICH POST-CRISIS REGULATIONS ARE CONSTRAINING BANKS MARKET MAKING? EVIDENCE FROM STRATEGICE ACCOUNTING CLASSIFICATIONS ...... 1

1.1. Introduction ...... 1

1.2. Background and Hypothesis Development ...... 7

1.2.1. Strategic Classification Decisions ...... 7

1.2.2. Consequences for Fixed Income Securities ...... 10

1.3. Identification Strategy ...... 12

1.4. Data ...... 14

1.5. Empirical Results ...... 17

1.5.1. Rising Held-to-Maturity Classifications ...... 17

1.5.2. Rising Held-to-Maturity Classifications ...... 20

1.5.3. Less Liquid Capital Markets ...... 27

1.6. Conclusion ...... 34

CHAPTER 2: ON THE MARKET EFFECTS OF INSIDER TRADING: EVIDENCE FROM THE NBA’S MOST NOTORIOUS REFEREE ...... 61

2.1. Introduction ...... 61

2.2. Literature Review...... 67

2.2.1. Liquidity Provisions ...... 67

2.2.2. Price Discovery ...... 70

2.2.3. Sports Betting Markets ...... 71

2.3. Empirical Setting ...... 72

2.4. Data ...... 74

v 2.5. Empirical Results ...... 76

2.5.1. Trading Behavior ...... 76

2.5.2. Price Volatility ...... 79

2.5.3. Liquidity Provisions ...... 81

2.5.4. Market Efficiency ...... 83

2.6. Discussion ...... 88

2.7. Conclusion ...... 90

CHAPTER 3: DUAL OWNERSHIP AS A MARKET SOLUTION TO RISK SHIFTING: EVIDENCE FROM LOAN COVENANT VIOLATIONS ...... 110

3.1. Introduction ...... 110

3.2. Literature ...... 114

3.3. Identification Strategy ...... 117

3.4. Data ...... 119

3.4.1. Calculating Dual Ownership ...... 119

3.4.2. Loan Covenant Violations ...... 121

3.5. Empirical Results ...... 124

3.5.1. Model ...... 124

3.5.2. Results Discussion ...... 125

3.6. Conclusion ...... 129

REFERENCES ...... 151

APPENDIX ...... 160

vi LIST OF TABLES

Table 1.1.Variable Definitions ...... 47

Table 1.2. Summary Statistics ...... 48

Table 1.3. Fixed Income SFAS 115 Allocations by Period ...... 49

Table 1.4. Changes in HTM Classifications ...... 50

Table 1.5. SFAS 115 Classification Correlations by Period ...... 51

Table 1.6. Withdrawing Dealer Capital ...... 53

Table 1.7. Bank-Affiliated Dealer Inventories ...... 54

Table 1.8. Changes in HTM Classifications by MBS Type ...... 55

Table 1.9. Bank-Affiliated Dealers MBS Inventories ...... 56

Table 1.10. Fixed Income Trading Revenue ...... 57

Table 1.11. Turnover and Daily Trades by Agency ...... 58

Table 1.12. Monthly MBS Amihud Measure by Agency ...... 59

Table 1.13. Monthly MBS Intradealer Trade Percentage by Agency...... 60

Table 2.1. Summary Statistics ...... 96

Table 2.2. Donaghy Games by Team...... 97

Table 2.3. Donaghy's Effect on Point Spread Movements ...... 99

Table 2.4. Donaghy's Effect on Contract Price Movements ...... 100

Table 2.5. Donaghy's Effect on Point Spread Range ...... 101

Table 2.6. Donaghy's Effect on Point Spread Jumps ...... 102

Table 2.7. Donaghy's Effect on Contract Price Volatility ...... 103

Table 2.8. Donaghy's Effect on Contract Price Changes ...... 104

Table 2.9. Donaghy's Effect on Point Spread Vigorish (Bid-Ask) ...... 105

Table 2.10. Donaghy's Effect on Money Line Vigorish (Bid-Ask) ...... 106

Table 2.11. Donaghy's Effect on Point Spread Forecast Error ...... 107

vii Table 2.12 Donaghy's Effects on Price Discovery ...... 108

Table 2.13. Donaghy's Effect on Away Bet Win Percentage ...... 109

Table 3.1. Illustrative Example Aggregating to Parent Level ...... 133

Table 3.2. Illustrative Example Construction of Dual Ownership...... 134

Table 3.3. Violations Per Year ...... 135

Table 3.4. Summary Statistics for Firms in Violation ...... 136

Table 3.5. Summary Statistics for Firms not in Violation ...... 137

Table 3.6. Level of All Dual Ownership...... 138

Table 3.7. Level of Bond Dual Ownership ...... 139

Table 3.8. Level of Lender Dual Ownership ...... 140

Table 3.9. Change of All Dual Ownership ...... 141

Table 3.10. Change of Bondholder Dual Ownership ...... 143

Table 3.11. Change of Lender Dual Ownership ...... 145

Table 3.12. Cross Section Firm Heterogeneity of Violation on Dual Ownership ...... 147

Table 3.13. Cross Sectional Investor Heterogeneity of Violation on Dual Ownership ...... 149

viii LIST OF FIGURES

Figure 1.1. Constrained Fixed Income Dealer Inventories ...... 37

Figure 1.2. Imposing a Tradeoff ...... 38

Figure 1.3. Big 6 U.S. Banks Shift Capital to HTM ...... 39

Figure 1.4. Growth in HTM Crowds Out Dealer Inventories ...... 40

Figure 1.5. Primary Identification Strategy ...... 41

Figure 1.6. MBS Are the Asset Class of Choice for HTM ...... 42

Figure 1.7. HTM Classifications Exhibit Clear Treatment Effects ...... 43

Figure 1.8. The Crowding Out is Coordinating by Asset Class ...... 44

Figure 1.9. HTM Rises and Dealer Inventories Fall in Response to Regulatory Signals ...... 45

Figure 1.10. Negative Consequences for MBS Issuance ...... 46

Figure 2.1. Distribution of Point Spread Changes ...... 92

Figure 2.2. Donaghy Games as Percentage of Sample Games by Point Spread Move ...... 93

Figure 2.3. Number of Games Officiated by Donaghy (by team) ...... 94

Figure 2.4. Screenshot of Primary Data Source ...... 95

Figure 3.1. Distances from Covenant Violation Histogram ...... 131

Figure 3.2. Discontinuities Around Covenant Thresholds...... 132

ix CHAPTER 1: WHICH POST-CRISIS REGULATIONS ARE CONSTRAINING BANKS MARKET MAKING? EVIDENCE FROM STRATEGICE ACCOUNTING CLASSIFICATIONS

1.1. Introduction

Fixed income dealer inventories are being constrained. They continue to stagnate long after the financial crisis, and a period of further decline, beginning in 2013, is clearly visible in

Figure 1. Recent studies find that bank-affiliated dealers are indeed targeting lower levels of fixed income inventories, are less willing to deviate from those targets and are generally acting more like brokers than dealers in response to post-crisis regulations (see, for example,

Bessembinder, Jacobsen, Maxwell, and Venkataraman (2018); Adrian, Boyarchenko, and

Shachar (2017); Schultz (2017)). In short, these market makers are providing less market liquidity, and this is concerning because bank-affiliated dealers are the primary liquidity providers in over-the-counter (OTC) fixed income markets (Duffie (2012)). While these studies consistently conclude that post-crisis regulatory changes, broadly, are the constraining force, there is scant evidence tying this outcome to specific regulatory changes. I extend the literature by tracing banks’ withdrawal from fixed income market making to a specific, previously overlooked Basel III provision.

Banks increased held-to-maturity (HTM) classifications over 500% between 2010 and

2016. This was not random. It was a strategic response to Basel III’s expanded marking to market of fixed income portfolios in regulatory capital calculations, known as the AOCI filter removal.1 This provision anointed HTM as the only classification to protect banks’ regulatory capital ratios from the volatility of fixed income securities. Unfortunately, HTM is not a free

1 Accumulated other comprehensive income, or AOCI, is a component of stockholders’ equity that is covered in detail in Section 1.1. Figure 2 illustrates the way in which the AOCI filter removal creates the tradeoff between regulatory capital stability and active portfolio management.

1 lunch. Securities classified as such are prohibited from being sold to meet liquidity needs, rebalance risk or execute customer orders,2 and this friction imbeds a tradeoff between regulatory capital stability and active portfolio management into every classification decision. Banks’ revealed preferences for capital stability distort their balance sheets and constrain their abilities to provide capital market liquidity. This study aims to demonstrate each step in the causal channel from the AOCI filter removal to banks’ well-documented retreat from fixed income market making. I propose and test three empirical links in the provision’s chain of consequences.

First, banks respond to the AOCI filter removal by increasing HTM classifications of fixed income securities. Following U.S. regulators’ initial proposal in June 2012, which would have applied to all banks with more than $250 million in assets, all banks responded by dramatically increasing HTM classifications. Once the final rule exempted (i.e. withdrew the treatment for) banks with less than $250 billion in assets, however, the HTM classifications of these non- advanced approach banks plateaued while the HTM classifications of advanced approach banks only accelerated.3 For example, the big six U.S. banks increased HTM classifications more than fivefold in just four years, from $60 billion at the end of 2012 (just before U.S. adoption was finalized) to $327 billion at the end of 2016.4 In other words, a change in regulatory incentives induced the six biggest banks at the heart of OTC markets to classify nearly $300 billion in otherwise liquid securities as being unavailable for market making.

Second, the increase in illiquid HTM classifications crowds out banks’ dealer inventories for those same asset classes. During the four years of unprecedented growth in HTM, U.S.

2 The sale of reclassification of HTM securities risks “tainting” the entire HTM portfolio. In such cases, auditor can force the bank, per SEC guidance, to reclassify all HTM securities, mark such securities to market, and lose the option to classify HTM securities for two years (Sangiuolo and Seidman, 2008). 3 Advanced approach banks are defined as those banks with over $250 billion in assets or at least $10 billion in foreign assets. Non-advanced approach banks are those which do not surpass these size thresholds. 4 The big six U.S. banks are Bank of America, Wells Fargo, J.P. Morgan Chase, Citigroup, Morgan Stanley, and Goldman Sachs. Year-end figures were taken from the banks’ annual reports and can be seen in Figure 3.

2 broker-dealers cut aggregate fixed income inventories in half, a decline of approximately $291 billion. These substitution effects can be seen in Figure 4. Following the regulatory shock, HTM classifications of mortgage-backed securities (MBS) rose sharply at the same time MBS held for trading fell to sample period lows. In difference-in-difference regressions, I find withdrawals of capital from bank-affiliated broker-dealers are increasing in the amount of HTM classifications made by the broker-dealer’s parent. By contrast, capital levels rise during the same period for non-bank-affiliated dealers, which are unaffected by the AOCI filter removal. Across banks, advanced approach banks reduce their dealers’ fixed income inventories relative to non- advanced approach banks following the final rule. Again, this is the same period in which advanced approach banks are also most aggressively increasing HTM classifications while non- advanced approach banks’ HTM classifications plateau. Put simply, banks withdrew capital from their dealers’ inventories to fund the dramatic growth in HTM portfolios.

Finally, the constrain on dealer capacity leads to less liquid capital markets as banks are less able to provide market liquidity. To demonstrate this channel, I instrument dealer capital with HTM classifications and find that HTM classifications negatively impact banks’ trading revenue (i.e. compensation for providing market liquidity) through the dealer capital channel. I then test four different market liquidity measures in difference-in-difference models and find that, indeed, liquidity worsens in the markets for securities most frequently classified as HTM relative to the markets for otherwise similar securities. By documenting this string of unintended consequences, my study bridges the gap between a finance literature seeking to explain banks’ reduced role as fixed income market makers and an accounting literature examining the frictions of a regulatory shock to fixed income classification incentives.

3 The empirical challenge of testing these hypotheses is to demonstrate that the observed effects are caused by the AOCI filter removal and are distinct from the effects of concurrent economic and regulatory changes. My identification strategy is to identify treated and control banks, broker-dealers and asset classes at each link in the causal chain and estimate treatment effects in difference-in-difference regressions. As described above, the regulatory treatment and subsequent withdrawal from a subset of banks enables me to establish that the AOCI filter removal causes HTM classifications. In my analysis of broker-dealers, some are affiliated with banks and therefore affected by the regulatory change; others are not affiliated with banks and therefore provide another useful control group. I also exploit cross-sectional variation across banks’ HTM classifications to demonstrate HTM’s role in crowding out dealer inventories and reducing the trading revenue of banks earn following the provision’s implementation. Each difference across banks is unique to the AOCI filter, and I limit my sample to those banks required to participate in the Comprehensive Capital Analysis and Review (CCAR) tests to minimize size and business model differences between the treated and control groups.

Another feature that distinguishes the consequences of the AOCI filter from other regulatory changes is the asset classes it affects. I rule out provisions that penalize risky assets by exploiting the fact that the AOCI filter removal only changes the regulatory treatment of fixed income securities. Derivatives, tradable loans and other non-fixed income securities, which would be penalized by provisions such as the Volcker Rule, are ineligible for HTM classification and are therefore well-suited for falsification tests. Contrary to the decline in fixed income dealer inventories, I find no evidence of decreased inventories for non-fixed income asset classes.

I further narrow my focus to control for the effects of other regulations that penalize low- risk assets, most notably the supplementary leverage ratio (SLR), by exploiting the fact that

4 Fannie Mae RMBS, and to a lesser extent Freddie Mac RMBS, are disproportionately more likely to be classified as HTM than Ginnie Mae RMBS. I examine four commonly used measures of market liquidity and find that securities most heavily “dosed” with the HTM treatment experience greater reductions in market liquidity than otherwise similar control groups.

The only plausible explanation consistent with each of these findings is that HTM classifications, made in response to the AOCI filter removal, crowd out dealers’ inventory capacities and ultimately reduce the liquidity provided to capital markets.

This paper’s main contribution is the documentation of a new cause, HTM classifications, contributing to banks’ retreat from fixed income market making. Empirical studies find that post- crisis regulations, as a whole, discourage fixed income market making, but little work has been done to test the consequences of specific provisions.5 I move this literature forward by examining a specific provision, the AOCI filter removal, and demonstrating the chain of consequences that begins with strategic accounting classifications and ends with less capital market liquidity.

This study also contributes to a burgeoning accounting literature focused on the consequences of HTM classifications. My study distinguishes itself by focusing on the capital market consequences stemming from HTM classifications by the largest dealer banks. Kim,

Kim, and Ryan (2019) examine different consequences of HTM classifications and show that banks increase the use of repurchase agreements, reduce risk in security portfolios, and make fewer, but riskier, loans. In an earlier study, Meder (2015) finds that HTM classifications exacerbate monetary policy tightening by constraining banks’ loan growth. He argues that capital

5 One notable exception is Bao, O’Hara, and Zhou (2018), which demonstrates the constraining effects of the Volcker Rule. I certainly do not suggest that the Volcker Rule has no effects. Rather, my findings show effects that are distinct from those of the Volcker Rule.

5 locked away in HTM portfolios cannot be used to meet loan demand, and I apply similar logic to argue that capital locked away in HTM cannot be used to provide market liquidity. Collectively, we document a range of unintended consequences rippling out from a classification tradeoff to affect banks’ balance sheets, business models, and customers.

Lastly, this study contributes to a broader literature examining banks’ fixed income classification incentives. Hodder, Kohlbeck, and McNally (2002) demonstrate the roll of regulatory incentives by showing banks undid initial HTM classifications once the AOCI filter was put in place in 1995. I complement these findings by showing a predictable reversion toward the SFAS 115 classification levels observed in their pre-treatment periods. More recently, Fuster and Vickery (2019) and Chircop and Novotny-Farkas (2016) examine the same regulatory signals used in this study. The former shows the rise in HTM classifications are concentrated in securities with the greatest interest rate risk. The latter documents negative stock market reactions related to the AOCI filter removal, reflecting investors’ perception of its regulatory costs to banks.

The remainder of the paper is organized as follows. Section 1 discuss the in institutional details and develops hypotheses, Section 2 outlines my identification strategy, Section 3 describes the data, Section 4 presents and interprets the empirical results, and Section 5 concludes.

6 1.2. Background and Hypothesis Development

1.2.1. Strategic Classification Decisions

Statement of Financial Accounting Standards (SFAS) 115, Accounting for Certain

Investments in Debt and Equity Securities (FASB 1993), established three accounting classifications for fixed income securities. At the time of purchase, firms must classify fixed income securities as either held-to-maturity (HTM), trading, or available-for-sale (AFS). The costs and benefits of each classification choice come from the accounting treatment of unrealized gains and losses (i.e. mark to market or amortized cost) and any restrictions made on the sale of securities. This study examines the consequences of Basel III’s provision to remove the AOCI filter, which encouraged HTM classifications by marking AFS securities to market in regulatory capital calculations.

The HTM classification is designated for debt securities that the enterprise has the positive intent and ability to hold to maturity (FASB 1993). This positive intent and ability to hold to maturity is a binding commitment by the bank; HTM securities are prohibited from being sold or reclassified in response to liquidity needs or changes in market interest rates.

Furthermore, any sale or reclassification of HTM securities risks “tainting” the entire HTM portfolio and forfeiting the option to use the HTM classification for two years under SEC guidance (Sangiuolo and Sediman (2008)). The appeal of the classification is that HTM securities are carried at amortized cost rather than being marked to market. This stabilizes key regulatory ratios, such as the supplementary leverage ratio (SLR), and therefore improves banks’ performances on Comprehensive Capital Analysis and Review (CCAR) tests. HTM is effectively a regulatory hedge for fixed income securities.

Another classification choice is trading assets, defined as debt and equity securities that are “bought and held principally for the purpose of selling them in the near term (FASB 1993).”

7 As the definition implies, this is the most appropriate classification choice for dealer inventories, and its costs and benefits contrast perfectly with those of HTM. Securities classified as trading assets may be sold at will, but any fair value fluctuations contribute directly to regulatory capital volatility.

The catchall category is AFS, defined simply as “debt and equity securities not classified as either held-to-maturity securities or trading securities (FASB 1993).” Unrealized gains and losses on AFS securities are included in accumulated other comprehensive income (AOCI), a component of stockholders’ equity that is excluded from Tier 1 regulatory capital via the AOCI filter. Prior to the AOCI filter removal, AFS was the dominant classification choice for two reasons: (1) securities classified as such faced no sale restrictions, and (2) their fair value fluctuations were filtered out of regulatory capital via the AOCI filter. Thus, AFS provided both the regulatory benefits of HTM and the liquidity benefits of trading assets.

Academic studies have long found that regulatory incentives influence accounting classifications. Hodder et al. (2002) find that banks reversed strategic HTM classifications when regulators granted salable AFS securities the same regulatory benefit as restricted HTM securities by introducing the AOCI filter in 1995.6 The AOCI filter remained in place for nearly

20 years, allowing banks to enjoy both balance sheet liquidity and regulatory capital stability via the AFS classification. As a result, 72.1% of all investment securities held by U.S. commercial banks were classified as AFS as of December 31, 2010 while only 4.0% were classified as HTM

(the remaining 23.9% were classified as trading assets). This is in stark contrast to the allocations on December 31, 1994, at which time 39.3% were classified as AFS and 41.8% were classified

6 Federal regulators announced the decision to create the AOCI filter in October 1994 and allowed banks a one-time reclassification amnesty in November 1995 to undo strategic classifications (Hodder et al, 2002).

8 as HTM. It is therefore reasonable to expect a reversion towards those initial SFAS 115 classification allocations once Basel III removes the AOCI filter for the largest banks.

Basel III’s provision to remove the AOCI filter was perhaps the most contentious of the post-crisis regulatory changes because it once again forces banks to make a tradeoff between balance sheet flexibility and regulatory capital stability. They can no longer have their cake and eat it too by using the AFS classification. The American Bankers’ Association argued that removing the AOCI filter would adversely affect banks’ capital ratios in a rising interest rate environment in a manner unrepresentative of their true economic values (ABA 2012). Banks would protect those capital ratios, industry representatives warned, by classifying more securities as HTM, a strategy that necessarily reduces balance sheet liquidity (Federal Register 2013).

Indeed, J.P. Morgan acknowledged precisely this motivation on Page 230 of its 2014 Annual

Report: “During the first quarter of 2014, the Firm transferred U.S. government agency mortgage-backed securities and obligations of U.S. states and municipalities with a fair value of

$19.3 billion from AFS to HTM. The transfer reflected the Firm’s intent to hold the securities to maturity in order to reduce the impact of price volatility on AOCI and certain capital measures under Basel III.” Taken together, prior research on strategic classifications, the warnings banks sounded to regulators, and the publicly disclosed rationale for HTM classifications by the largest bank in the United States lead to my first hypothesis, which is consistent with those of contemporaneous studies by Kim, Kim, and Ryan (2019) and Fuster and Vickery (2019).

Hypothesis 1: Banks will increase HTM classifications following regulatory signals that their

AOCI filters will be removed.

9 1.2.2. Consequences for Fixed Income Securities

In addition to increasing the appeal of HTM classifications, the AOCI filter removal decreases the appeal of fixed income market making through two distinct, but related, channels.

First, the AOCI filter removal naturally increases the volatility of banks’ regulatory capital

(Barth, Landsman, and Wahlen (1995)), thereby increasing the likelihood that regulatory capital requirements are violated.7 Put differently, the AOCI filter removal tightens regulatory capital constraints. The theoretical models of Cimon and Garriot (2019) predict that such tightening of regulatory capital constraints will reduce banks’ willingness to commit capital to warehousing inventory.

Second, the predicted increase in HTM classifications necessarily reduces the liquidity of banks’ balance sheets and, more specifically, their fixed income portfolios. Active portfolio management is an indispensable part of fixed income market making (Duffie (2012)), and a reduced ability to do so is the cost banks accept in the HTM classification tradeoff (Beatty

(1995)). Indeed, Godwin, Petroni, and Wahlen (1998) find evidence that insurance companies weighed the cost of liquidity risks when adopting SFAS 115. Anecdotally, it is quite telling that

Goldman Sachs, a former investment bank and the quintessential Wall Street trading firm, was the last of the big six U.S. banks to use the HTM classification. It still represents just $87 million, or 0.01% of their $860 billion in total assets as of December 31, 2016. Taken together, the AOCI filter removal’s simultaneous incentives to classify securities as HTM and disincentives to engage in fixed income market making encourage a reallocation of capital from dealer inventories to HTM portfolios. Empirically, multiple studies provide evidence of these substitution effects in a European setting. Banks from several European countries incurred real

7 Without the AOCI filter, the market fluctuations of banks’ fixed income AFS portfolios now flow directly to regulatory capital. Previously, this volatility was “filtered” out by the AOCI filter.

10 costs to bolster capital in the heart of the financial crisis by reclassifying securities not only from

AFS to HTM but from held-for-trading (the IFRS equivalent of FASB’s trading assets) to HTM

(e.g. Argimon et al (2018); Bischoff et al (2019); Fiechter et al (2017)).

Finally, recall that HTM, AFS, and trading assets are mutually exclusive and collectively exhaustive categories of fixed income security classifications. HTM securities cannot be part of dealer inventories because they cannot be sold, and the decision to classify as HTM is irreversible except in rare and specified circumstances.8 If we take a bank’s target level of exposure to an asset class as fixed at the holding company level, an increase in HTM necessarily crowds out market making capacity by reducing the sum of AFS and trading assets. For example, suppose Bank A targets $10 billion in total exposure to U.S. Treasury securities. If it classifies none of its U.S. Treasuries as HTM, Bank A will have $10 billion in U.S. Treasuries with which it can provide fixed income market making services (i.e. classified as either AFS or trading assets). If, however, it reclassifies $5 billion of those U.S. Treasuries as HTM (perhaps in response to a provision that changes classification incentives), it now has only $5 billion with which to facilitate fixed income market making. The other $5 billion of market making capacity has been crowded out by HTM.

In summary, the AOCI filter removal increases the appeal of the HTM classification at the same time it discourages fixed income market making by tightening regulatory capital constraints and impeding active portfolio management. Consequently, I expect the increase in

HTM classifications to be accompanied by withdrawals of capital from fixed income market making activities. This crowding out of market making capacity then leads to less market

8 As noted in Kim, Kim, and Ryan (2019) in reference to FASB (1993), “The specified circumstances are significant deterioration of the issuer’s creditworthiness, significant changes in relevant tax laws, or regulatory requirements, major business combinations or disruptions, and the security is close to maturity or largely paid off.”

11 liquidity provided by banks and ultimately less liquidity in the markets for those assets. These consequences are reflected in the hypotheses that follow.

Hypothesis 2: Bank-affiliated dealers will decrease fixed income inventories following regulatory signals that their AOCI filters will be removed, but only for those asset classes most frequently classified as HTM.

Hypothesis 3: Banks will provide less liquidity to fixed income markets, and that reduction will be increasing in the bank’s increase in HTM classifications.

Hypothesis 4: Capital markets for the asset classes most prone to HTM classification will experience worsening liquidity relative to similar capital markets less prone to HTM classifications.

1.3. Identification Strategy

The primary challenge in demonstrating the consequences of specific post-crisis regulations is to control for the confounding effects of all the other economic and regulatory changes occurring around the same time. I address this challenge by comparing treated and control groups at four different levels: the bank level, the broker-dealer level, the asset class level, and the agency MBS level. Each level of analysis rules out different alternative explanations while confirming my economically intuitive hypotheses. As a result, the AOCI filter removal is the only regulatory change whose consequences are consistent with this study’s collective body of evidence.

12 Figure 5 summarizes the sequence of regulatory signals examined. The key feature of my bank-level analysis is that two groups of banks are initially exposed to the regulatory treatment, but one group later has the treatment withdrawn. The first signal is the initial rule proposal, which was issued by the Federal Reserve on June 7, 2012 and required the removal of the AOCI filter for all banks with more than $500 million in assets. The final rule, however, issued on July

2, 2013, exempted non-advanced approach banks, providing an opportunity to test for a cessation of treatment effects specific to that group following its July 2, 2013 issuance.9 This is important because other regulations do not provide the same sequence of treatment and withdrawal for non- advanced approach banks and therefore would not explain the sequence of similar then diverging treatment effects between the two groups.

Still, it is possible that the AOCI filter removal causes the increase in HTM classifications, and other regulatory changes simultaneously cause banks to reduce dealer inventories. I alleviate this concern by performing most of my analysis on agency mortgage- backed securities (MBS). Agency MBS possess two desirable features. First, Figure 6 demonstrates that MBS are banks’ favorite asset class to classify as HTM. By the end of 2016, my sample of advanced approach banks classified an average of nearly 30% of their MBS portfolios as HTM. No other asset class exceeded 20%. If HTM classifications do in fact crowd out dealer inventories, the effect should be most evident when examining MBS.

The other benefit of examining agency MBS is that they are unaffected by two alternative regulatory changes I must rule out. The Volcker Rule exempts agency MBS, and the asset weights applied for risk-based capital measures give preferential treatments to agency MBS due

9 I limit my sample to banks subject to the Comprehensive Capital Analysis and Review (CCAR) tests to keep treated and control groups as similar as possible. Admittedly, advanced approach banks are inherently different from non-advanced banks. I therefore take this as one piece of evidence and later strengthen identification by testing treated and control asset classes.

13 to their low-risk nature. While other studies point to the effects of the Volcker Rule on corporate bond markets, there is no clear reason why it should affect asset classes it explicitly exempts. If dealers’ inventories of agency MBS are found to be constrained as well, the causal explanation would need to be something other than these regulations, and it is at that point that I continue to draw the connection HTM classifications.

Finally, I must rule out regulations that discourage the holding of low-risk assets, most notably the supplementary leverage ratio (SLR). I do so by focusing within the agency MBS asset class. All agency securities are either explicitly or implicitly backed by the U.S. government and are therefore considered to have virtually no credit risk. These securities do, however, differ in their propensity to be classified as HTM, as my analysis shows. Agency

RMBS are more frequently classified as HTM than agency CMBS, Fannie Mae and Freddie Mae

RMBS are more frequently classified as HTM than interest-only collateralized mortgage operations (CMOs). I examine measures of dealer inventories or market liquidity or both across these agencies and security types to further raise the number of empirical relationships any causal story would be required to explain.

1.4. Data

I draw from multiple data sources to conduct my analyses. Quarterly bank-level data are obtained from the WRDS Bank Regulatory Database, which compiles consolidated financial information at the holding company level from Y-9 call reports. These reports are filed each quarter with the Federal Reserve and are described on the Federal Reserve’s website as the “most widely requested and reviewed report at the holding company level.” Broker-dealer capital data is obtained from monthly reports issued by the Commodity Futures Trading Commission

14 (CFTC) and made available on their website.10 I derive market liquidity measures based on data from the Financial Industry Regulation Authority (FINRA) and Securities Industry and Financial

Markets Association (SIFMA). I obtain trade-level data on agency MBS from TRACE’s standard database. Finally, interest rate and macroeconomic variables are obtained from the

Federal Reserve Economic Data (FRED) maintained by the Federal Reserve Bank of St. Louis.

Variables are defined in Table 1.

I restrict my bank-level sample to U.S. bank holding companies that are subject to the

Comprehensive Capital Analysis and Review (CCAR), resulting in a sample of 14 advanced- approach holding companies and 14 non-advanced approach holding companies.11 This restriction is imposed to minimize regulatory and business model differences between the treated and control groups. The sample period for my bank-level analysis extends from the first quarter of 2010 through the fourth quarter of 2016, resulting in a balanced panel of 784 bank-quarters.

I examine four common measures of market liquidity: daily turnover, daily number of trades, the monthly average of the Amihud factor, and the monthly percentage of total trades made between dealers (i.e. interdealer trades). Trade volume and the number of trades per day come from Structured Trading Activity Reports from FINRA.12 I obtain single family MBS outstanding at quarterly frequencies, by agency, from data compiled by SIFMA. Finally, data to calculate the Amihud factor and percentage of interdealer trades were obtained from TRACE’s trade-level data on agency MBS. I cleaned the TRACE transaction data following the method

10 Reports can be downloaded at https://www.cftc.gov/MarketReports/financialfcmdata/index.htm. 11 The 14 advanced approach banks include American Express, Bank of America, BNY Mellon, Capital One, Citigroup, Goldman Sachs, HSBC North America, J.P. Morgan Chase, Morgan Stanley, Northern Trust, PNC Financial, State Street, US Bancorp, and Wells Fargo. The 14 non-advanced approach banks include Ally Financial, BB&T, BBVA Compass, BMO Financial, Citizens Financial, Comerica, Discover, Fifth Third, Huntington, Keycorp, M&T, Regions, Suntrust, and Zions. These are quite similar to those used in Kim, Kim, and Ryan (2019). I dropped TD Bank US and Santander to ensure a balanced panel. 12 Daily summaries can be found at http://tps.finra.org/idc-index.html.

15 outlined in Dick-Nielsen (2009). Code was graciously made available by Qingyi (Freda) Song

Dreschler. Continuous bank-level and liquidity measure variables are winsorized at the1% and

99% levels to mitigate the effects of outliers.

Table 2 presents summary statistics for the bank-level variables of interest, as well as control variables, for each group of banks. The two groups of banks are clearly different because he advanced approach designation is determined by size. I limit the sample to 28 CCAR banks to mitigate the extent of these differences. Furthermore, all bank-level regressions control for bank size and include an indicator variable to absorb the average difference between advanced approach and non-advanced approach banks.

It is also important to note that the two groups of banks, while inherently different, are similar in terms of regulatory incentives, which are the incentives upon which my hypotheses are based. Each group wants to avoid the consequences of violating regulatory capital requirements

(e.g. increased regulatory scrutiny, restrictions on capital distributions, stigma in the marketplace, etc.). It is therefore reasonable to expect each group to be similarly motivated to mitigate the effects of provisions that increase regulatory risk.

For example, if capital ratio requirements were increased for both groups, it would be reasonable to expect both groups to increase capital ratios despite their inherent differences. In the case of the AOCI filter removal, that means taking actions, such as classifying larger portions of fixed income portfolios as HTM, to reduce the sensitivity of regulatory capital ratios to interest rate risk. Further, the sale restrictions associated with those HTM classifications (i.e. the consequences of the actions they are similarly incentivized to take) are exactly the same for each group. Between multiple size controls, significant sample restrictions and the two groups’ similar

16 incentives to meet regulatory capital requirements, I believe I have done as much as the data permits to control for any differences between the two groups of banks.

1.5. Empirical Results

1.5.1. Rising Held-to-Maturity Classifications

I begin my analysis by establishing the empirical fact that banks increase HTM classifications to protect regulatory capital from the effects of the AOCI filter removal. J.P.

Morgan explicitly cited this rationale as the basis for such actions in their 2014 annual report.

Recall from Hypothesis 1 that I expect banks to increase HTM classifications following regulatory signals that they will lose their AOCI filters (i.e. during the proposal period for non- advanced approach banks and during both the proposal and final rule periods for advanced approach banks). Doing so shields Tier 1 regulatory capital from the threat of rising interest rates and the corresponding fair value losses on fixed income securities.

Figure 7 illustrates HTM classifications of MBS for both advanced approach and non- advanced approach banks. Leading up to the July 2013 final rule, the two groups exhibit parallel trends because, to that point, regulatory signals suggest both groups will be subject to the new provision. In both cases, HTM classifications increase gradually in response to the Basel

Committee’s final regulatory framework in June 2011. This growth accelerates following the

Federal Reserve’s initial proposal of the U.S. adoption in June 2012.

Finally, the groups’ trends diverge when regulators issue the final rule in July 2013, which confirms advanced approach banks will be subject to the provision but exempts non- advanced approach banks. Advanced approach banks dramatically accelerate the rate of HTM growth while non-advanced approach banks’ HTM classifications plateau shortly thereafter. The

HTM classifications of non-advanced approach banks plateau rather than revert to pre-treatment

17 levels because HTM classifications are irreversible. Advanced approach banks commit more fully to HTM classifications because all uncertainty has been removed.13 Put differently, the stronger dose prompted a stronger treatment effect.

I empirically test Hypothesis 1 using a difference-in-difference model based on the model used in Chircop and Novotny-Farkas (2016), referred to hereafter as CNF.

푆퐹퐴푆115, = 훽0 + 훽1 푃표푠푡퐹푖푛푎푙t∗ 퐴푑푣퐴푝푝i,t + 훽2 푃표푠푡퐹푖푛푎푙t +

훽3 푃표푠푡푃표푠푡t∗ 퐴푑푣퐴푝푝i,t + 훽4 푃표푠푡푃표푠푡t + 훾푋i,t + δ+ λ + ε,

In this initial test, the dependent variable is HTM, which is the fair value of HTM securities, scaled by assets, for bank i in quarter t. PostProp takes a value of one for quarters between the

June 2012 issuance of the proposal and the July 2013 issuance of the final rule and zero otherwise. PostFinal takes a value of one in the quarters following (and including) the July 2013 issuance of the final rule and zero otherwise. AdvApp takes the value of one for advanced approach banks (i.e. those with greater than $250 billion in assets) and zero otherwise. The interactions of AdvApp with PostProp and PostFinal capture the incremental effects of each regulatory signal on the HTM classification behavior of advanced approach banks relative to non-advanced approach banks.

I also use a regression model adapted from Kim, Kim, and Ryan (2019), or KKR, to ensure my results are robust to both regression models being used in this strand of accounting literature. The only difference is that the two treated periods above, PostFinal and PostProp, are replaced by a single treated period, Implementation, that takes a value of one for all quarters

13 Bank managers require a reasonably high probability of the regulatory change to occur before making costly and irreversible changes, such as locking capital away in HTM portfolios (Gulen and Ion, 2016).

18 following the start of the provision’s implementation period in January 2014. In the KKR models, the variable of interest is the interaction term between Implementation and AdvApp.

14 Standard bank-level control variables (푋i,t) and bank fixed effects (λ) are included in each equation to absorb time-varying and time-invariant differences across banks, respectively.

Quarter-year fixed effects (δ) are included to absorb time-varying economic conditions common to all banks that may affect HTM classifications.

Table 4 confirms Hypothesis 1 by providing empirical support for Figure 7. All specifications imply that advanced approach banks increase HTM classifications relative to non- advanced approach banks in the periods in which only advanced approach banks are treated.

HTM classifications by advanced approach banks were approximately 1.6% greater as a percentage of assets, relative to non-advanced approach banks, following the final rule. In addition to being statistically significant at the five percent level, this amount is economically significant, representing 57.1% of the sample average for advanced approach banks. For a bank with $500 billion in assets, this represents $8 billion in previously liquid assets that the bank has chosen to render illiquid.

The CNF specifications also suggest HTM classifications were greater for non-advanced approach banks during the final period than in the previous two periods. This reflects the fact that HTM classifications made by non-advanced approach banks prior to the final rule cannot be undone and therefore persist throughout the final rule period. It is clear from Figure 7 and Table

3 that advanced approach banks increased HTM classifications more sharply following the final rule whereas non-advanced approach banks stopped increasing. Concurrent papers by Kim, Kim, and Ryan (2019) and Fuster and Vickery (2019) also conclude that dramatic increases in HTM

14 Each specification of the regression model includes standard bank-level control variables. These include Size, Deposits, Debt, return on assets (ROA), Tier 1 ratio (Tier 1), unrealized gains and losses (URGL), and Maturity.

19 classifications are a response to the AOCI filter removal. Together, we are the first papers to document this phenomenon. My unique contribution begins by answering the following question: from what parts of banks’ balance sheets is the new HTM capital being extracted?

1.5.2. Rising Held-to-Maturity Classifications

Hypothesis 2 predicts that the capital flowing into the rising HTM classifications is being withdrawn from the AFS and trading assets portfolios that facilitate market making. These are the only other classification choices for fixed income securities, and any substitution effects in response to the AOCI filter removal should be most obvious in the period most affected by the

AOCI filter removal. I examine four distinct time periods: the financial crisis period (Q3 2007 through Q2 2009), the post-crisis period (Q3 2009 through Q1 2012), the AOCI period (Q2 2012 through Q1 2014), and the Volcker period (Q2 2014 through Q4 2016, as defined by Bao,

O’Hara, and Zhou (2018)). The correlation coefficients are calculated within each period based on the aggregate dollar amount classified in each category by U.S. commercial banks.

Table 5 clearly illustrates an abrupt change in substitution effects between HTM and market making capacity in response to the AOCI filter removal. In the two periods prior to the

AOCI period, the only significant correlation between HTM and either category was a strongly positive one with AFS during the financial crisis. Suddenly, in the eight quarters following the first regulatory shock, HTM spikes to nearly perfect negative correlation with both AFS and trading assets. This is precisely what one would expect to find if banks rebalanced their portfolio classifications, diverting capital from AFS and trading assets to HTM, in response to the provision’s greater inevitability. Both HTM correlation coefficients are around -0.9 and significant at the one percent level. The correlation between HTM and trading assets weakens

20 both statistically and in magnitude in the Volcker period as the shock to classification incentives fades. At the same time, HTM and AFS resume their positive correlations.

The substitution effects between HTM and trading assets are further illustrated in Figure

8, which examines two security types, agency MBS, and U.S. Treasuries, that are exempt from the Volcker Rule and given preferential treatment under risk-based capital requirements. The greatest increase in HTM classifications of agency MBS occurs between Q1 2013 and Q1 2014.

This corresponds with the period of greatest decrease in trading assets for agency MBS. Similar comovement can be seen between Q1 2014 and Q1 2015 for U.S. Treasuries. This is the period of both the greatest increase in HTM and greatest decrease in trading assets. The Volcker Rule exempts both securities and thus cannot account for the decline in trading assets, let alone the synchronized movements across asset classes. The same can be said of risk-based capital requirements, which apply a 0% risk-weighting to U.S. Treasuries and either 0% or 20% to agency MBS. Finally, the supplementary leverage ratio, which does indeed discourage the holding of safe assets, offers no explanation for why the declines in trading assets would differ in time across these asset classes and in such a way that corresponds with increases in HTM.

I move back to the parents’ balance sheets to compare changes in fixed income (i.e. treated) and non-fixed income (i.e. control) dealer inventories.15 Non-fixed income securities

(e.g. derivatives, tradeable loans, etc.) are ineligible for HTM classification and therefore should not be affected by the AOC I filter removal. Thus, the HTM “treatment” is only administered to fixed income securities. The crowding out hypothesis predicts reductions in fixed income dealer inventories, which bear the opportunity cost of HTM, but no reductions in non-fixed income

15 The accounting term is trading assets, which consists largely of dealer inventories and proprietary trading portfolios. I refer to trading assets as dealer inventories because dealer inventories are highly correlated with trading assets, suggesting they comprise the majority of firms’ trading assets. Additionally, “dealer inventories” is more familiar terminology for a finance audience than the accounting term “trading asserts.”

21 dealer inventories, which do not. To perform these tests, I rerun the CNF and KKR regression models from Table 4, replacing the dependent variable with dealer inventories of either fixed income or non-fixed income securities scaled by total assets.

The estimates in Table 6 simultaneously support the crowding out hypothesis and help rule out regulations that penalize risky asset classes. Advanced approach banks reduce fixed income dealer inventories to a greater degree than non-advanced approach banks only after the final rule was issued. In economic terms, advanced approach banks reduced fixed income inventories by 0.75% of assets relative to non-advanced approach banks following the final rule.

That represents an 11.79% reduction relative to the sample average of total trading assets, or

$3.75 billion for a bank of $500 billion in assets. The crowding out hypothesis is further supported by the fact that the decline in fixed income trading assets is of a similar magnitude to the increase in HTM classifications over the same period. Non-fixed income inventories, on the other hand, do not significantly decline for either group in any period.

To this point, I have shown evidence consistent with substitution effects between HTM classifications and those conducive to market making along with more direct evidence that dealers’ fixed income inventories are decreasing. This is what one would expect if HTM classifications were crowding out dealer inventories, and the timing of these substitutions suggest the AOCI filter removal is a plausible catalyst. Still, the number of concurrent regulatory changes necessitates a more focused analysis of substitutions within specific asset classes if a robust causal argument is to be made.

As we saw in Figure 6, advanced approach banks are more willing to classify mortgage- backed securities (MBS) as HTM than any other type of fixed income security. Prior to the announcement of the AOCI filter removal, advanced approach banks classified less than 5% of

22 their total MBS portfolios as HTM. By the end of 2016, nearly 30% of their MBS portfolios were classified as HTM and therefore unavailable for market making activities. For no other asset class did these big banks classify more than 20% of their portfolios as HTM. If the crowding out hypothesis is correct, its effects should be most apparent within MBS.

Furthermore, revealed preferences within the MBS asset class would provide even more precise indicators of where the constraints on market making should and should not be observed. This is empirically useful because agency MBS should be unaffected by two alternative explanations, the Volcker Rule and risk-based capital requirements (agency MBS are exempt from the former and not materially penalized by the latter).

I run the CNF regression models using banks’ HTM classifications of the following security types as dependent variables: mortgage-backed securities (All MBS), Fannie Mae and

Freddie Mac pass-thru residential MBS (FNMA/FHLMC RMBS), Ginnie Mae pass-thru residential MBS (GNMA RMBS), and agency commercial MBS (Agency CMBS). Table 7 reveals a clear preference by advanced approach banks to classify Fannie Mae and Freddie Mac

RMBS as HTM. It is the only MBS category for which advanced approach banks increase HTM relative to non-advanced approach banks. Advanced approach banks increased HTM classifications of Fannie and Freddie RMBS by 0.65% (0.41%) of total assets relative to non- advanced approach banks following the final rule (initial proposal). This represents 40.9% of the difference in all HTM increases between advanced approach and non-advanced approach banks during the final rule period.

There is no evidence that the two groups of banks differ in classifying the other MBS categories as HTM. Banks may prefer to classify Fannie Mae and Freddie Mac MBS as HTM rather than Ginnie Mae MBS based on the difference in their government guarantees. Ginnie

23 Mae securities are explicitly guaranteed by the U.S. government while Fannie Mae and Freddie

Mac securities are implicitly guaranteed. This distinction creates a slight difference in risk and may encourage banks to protect their capital ratios from additional credit or liquidity risk in

Fannie Mae and Freddie Mac MBS through HTM classifications. Regardless of the reason for this preference, the crowding out hypothesis predicts that agency pass-through RMBS, particularly those of Fannie Mae and Freddie Mac, should exhibit the greatest reductions in dealer inventory and market liquidity because they are given the strongest doses of the HTM treatment.

We can see substitution effects between HTM classifications and dealer inventories of agency RMBS in Figure 9. The red lines indicate regulatory signals increasing the likelihood that banks’ AOCI filter will be removed. The quarters containing regulatory signals correspond with spikes in HTM classifications and reductions in dealer inventories for the same asset class. These are mutually exclusive categories for a specific type of security, and the increase in HTM is funded in part by withdrawing capital from dealer inventories.

I rerun the CNF specifications with dealer inventories of various agency MBS as the dependent variables.16 Table 8 confirms that the asset class most prone to HTM classification by advanced approach banks, agency RMBS, is the same asset class driving reductions in trading assets following the final rule. In fact, the entire incremental decline in advanced approach banks’ MBS dealer inventories following the final rule is driven by agency RMBS.

Prior to the final rule, these agency RMBS fell for non-advanced approach banks but had not yet decline for advanced approach banks. The crowding out effect should be most

16 HTM classifications consist of Fannie Mae and Freddie Mac pass-through RMBS, but dealer inventories also include those of Ginnie Mae. The Y-9 Call Reports from which my data was collected does not distinguish between Fannie Mae, Freddie Mac, and Ginnie Mae pass-through RMBS for dealer inventories (i.e. trading assets).

24 pronounced for advanced approach banks during the final period because that is when they most aggressively increased HTM classifications. Furthermore, fixed income trading is an important source of revenue for advanced approach banks, and we would expect them to be reluctant to withdraw capital from this line of business before the uncertainty surrounding the provisions was resolved (Gulen and Ion (2016)). In contrast, dealers’ inventories of agency CMBS, which have similar risks but demonstrate no tendency to be classified as HTM, did not fall. In fact, increased slightly across all banks following the final rule.

Bear in mind, banks are not merely shuffling accounting classifications. Dealer inventories are a major component of trading assets, and the crowding out hypothesis predicts banks withdraw capital from their dealers’ inventories to fund the rising HTM classifications. I directly test this prediction by examining changes in the amount of capital held at treated and control broker-dealers in a difference-in-difference framework.

The CFTC provides monthly data for both absolute levels of capital and capital held in excess of the amounts required at the broker-dealer level. Bank-affiliated dealers are matched to sample BHCs to incorporate the parents’ HTM classifications and advanced approach status as variables of interest. The treated broker-dealers are those affiliated with advanced approach banks, and the control broker-dealers are not affiliated with banks.17 I examine changes in dealers’ capital using the following model:

퐷푒푎푙푒푟퐶푎푝푖푡푎푙, = 훽0 + 훽1 퐻푇푀i,t∗ 퐼푚푝푙푒푚푒푛푡푎푡푖표푛t +

훽2 퐻푇푀i,t + 훽3 퐼푚푝푙푒푚푒푛푡푎푡푖표푛t + δ+ λ + ε,

17 The only broker-dealer affiliated with non-advanced approach banks in the data were associated with Royal Bank of Scotland. Rather than having just one non-advanced approach bank represented, the sample is a pure comparison of broker-dealers of advanced approach banks with non-bank-affiliated broker-dealers.

The dependent variables are the log of either adjusted net capital or excess net capital, as reported by the CFTC. All specifications include a dummy variable, Implementation, which takes a value of one once implementation of the AOCI filter removal begins in January 2014. In the main specification, I interact Implementation with the parents’ measures of HTM. This design is meant to capture heterogeneity in HTM classifications across banks. HTM is set to zero for non-bank-affiliated broker-dealers, reflecting the fact that they do not classify securities as HTM.

In an alternative specification, I interact Implementation with AdvApp to ensure the model is robust. Broker-dealer level control are not available, but each specification includes month-year

(δ) and broker-dealer fixed effects (λ) to ensure identification comes from within broker-dealer variation in the relationship of interest.

The crowding out hypothesis predicts that banks fund their HTM classifications with capital taken from their broker-dealer subsidiaries, and the amount of capital diverted away from broker-dealers should be increasing in the parents’ HTM classifications. Table 9 offers strong support for the crowding out hypothesis by demonstrating that cross-sectional differences in

HTM correspond with withdrawals of capital from those banks’ dealers following the regulatory shock. The coefficient on the Implementation*HTM interaction term is negative and highly significant for both overall capital and excess capital. In economic terms, these coefficients imply that a one-standard deviation increase in HTM (approximately 2.5% of total assets for advanced approach banks over the sample period) corresponds to a decrease in excess and net broker-dealer capital of 9.2% and 6.2%, respectively, of sample averages. Put simply, the more capital a bank allocates to HTM portfolios in response to the regulatory change, the more capital that bank removes from its broker-dealer subsidiaries.

26 The coefficient on the Implementation*AdvApp interaction term is also negative and highly significant for both overall capital and excess capital. Economically, the coefficient on this interaction is far smaller in magnitude than that of the Implementation*HTM interaction term. This suggests heterogeneity in HTM classifications across banks contains substantial information about the degree of broker-dealer capital reduction, exactly as the crowding out hypothesis predicts. In unreported results, I conduct a falsification tests by treating Q1 2011 as the date of the regulatory shock, five quarters before the Federal Reserve initially proposed the provision’s U.S. adoption. Unlike the baseline tests, these falsification tests show no incremental effect of HTM classifications during the pseudo treatment period. This provides further support that the treatment effect I document is truly the result of HTM classifications following the regulatory change.

1.5.3. Less Liquid Capital Markets

In my final tests, I examine the capital market consequences of banks’ responses to the

AOCI filter removal. Banks increasingly lock fixed income securities away in their HTM portfolios, and that constrains their dealers’ inventories of those same securities. But do constrained dealer inventories lead to less liquidity in the capital markets for those assets?

I answer this question by first testing the channel through which HTM classifications reduce capital market liquidity. The hypothesized channel is that the crowding out of banks’ dealer inventories by HTM classifications reduces the amount of market liquidity those banks provide. I therefore examine banks’ trading revenue generated over the sample period because trading revenue is the compensation banks receive for providing market liquidity in their role as market maker. Less trading revenue would indicate banks are providing less market liquidity.

However, a simple OLS estimation of trading revenue on dealer capital would not distinguish the

27 effects of HTM classifications from other regulatory changes occurring around the same time.

There is also the possibility that correlated omitted variables drive both reduced trading revenues and reduced dealer capital or that lower trading revenues cause banks to withdraw capital from their dealers. I address these concerns using a two-staged least squares (2SLS) instrumental variable approach.

The dependent variable, Trading Revenue, is a quarterly measure of fixed income trading revenue scaled by total assets and calculated by adding trading revenue from interest rate and credit products from banks call report data. The explanatory variable of interest is Dealer

Capital, which is aggregated at the parent level,18 and obtained from CFTC reports, as described earlier. In the first stage, I instrument Dealer Capital with three variables: HTM, Implementation

(fully absorbed by quarter-year fixed effects) and the interaction between the two,

Implementation*HTM. These instruments plausibly satisfy both the relevance and exclusion criteria necessary for a valid instrument. Table 10 demonstrates that HTM classifications are relevant, explaining a substantial amount of changes in dealer capital once the AOCI filters implementation began. The F-statistics are 27.93 and 25.87 in the first and second specifications, respectively. As for the exclusion criteria, the most direct path through which HTM classifications affect trading revenue is through the crowding out of dealer capital. By directly crowding out the dealer’s market making capacity, this would seem to be a first-order effect. A case could be made that HTM classifications crowd out other risk management tools, which reduces the incentive to provide market liquidity, but this is an indirect path and should therefore be at most a second-order effect.

18 Some banks have multiple broker-dealers listed in the same CFTC report. For example, capital data is provided for J.P. Morgan Clearing Corp, J.P. Morgan Futures Inc., and J.P. Morgan Securities Inc. throughout 2010.

28 In the first specification, Dealer Capital is simply the level of regulatory capital as reported by CFTC at the end of each quarter. In the second specification, it is measured as the excess dealers’ capital above regulatory requirements. Since this analysis requires each BHC to be in both the bank-level and broker-dealer-level datasets, this sample includes 224 bank-quarter observations across eight different BHCs. The instrumented estimate of dealer capital has a positive and significant coefficient in both specifications. The interpretation is that a one standard deviation increase in the portion of dealer capital reductions caused by HTM classifications leads to a reduction in fixed income trading revenue equivalent to 0.06% of total assets, or approximately one third of the sample average of quarterly trading revenue.

My final analysis compares market liquidity measures across different types of Fannie

Mae, Freddie Mac, and Ginnie Mae MBS. The primary benefit of comparing these three agencies is that they allow me to control for the effects of the Volcker Rule, the supplementary leverage ratio (SLR) and countless other post-crisis regulations that might also discourage market making but treat agency RMBS similarly.19 Indeed, these three agencies are quite similar in all respects but for the fact that advanced approach banks (i.e. the largest and most essential

OTC dealers) classify Fannie Mae and Freddie Mac MBS as HTM disproportionately more than

Ginnie Mae MBS. Thus, in my baseline tests of market liquidity, Fannie Mae and Freddie Mac

MBS will be the treatment groups (i.e. received the HTM treatment), and Ginnie Mae will be the control group.

I first estimate daily measures of market liquidity using the following model:

19 The Volcker Rule exempts agency MBS along with Treasuries, and the SLR does not differentially affect assets sharing similar risks.

29 퐿푖푞푢푖푑푖푡푦, = 훽0 + 훽1 퐹푎푛푛푖푒i,t∗ 푃표푠푡t + 훽2 퐹푎푛푛푖푒i,t +

훣3 퐹푟푒푑푑푖푒i,t∗ 푃표푠푡t + 훽4 퐹푟푒푑푑푖푒i,t +

훣5 푃표푠푡t + δ+ λ + ε,

The dependent variable is either daily turnover or daily number of trades for the MBS of agency i in trading day t (used, for example, in Anderson and Stulz (2017)). Fannie is a dummy variable taking the value of one for Fannie Mae MBS, and zero otherwise. Freddie is a dummy variable taking the value of one for Freddie Mac MBS, and zero otherwise. Post is a dummy variable taking a value of one in the quarters following the final rule, and zero otherwise.

The variables of interest are the interactions of Fannie (Freddie) with Post, which estimate the incremental change in the liquidity measure for Fannie Mae (Freddie Mac) MBS relative to Ginnie Mae MBS after the final rule. I also include the following control variables related to the economic and financial environment: TED Spread, Term Spread, Credit Spread,

S&P500 Return, VIX, and MBS Outstanding.20 Variables are defined in Table 1. Finally, I follow

Gete and Reher (2018) in including day-of-week fixed effects (δ) to control for differences in trading behavior days of the week.

Agency residential MBS are treated identically by virtually all post-crisis regulatory changes except the liquidity coverage ratio (LCR), which gives preferential treatment to Ginnie

Mae MBS relative to those issued by Fannie Mae and Freddie Mac, and risk-based capital measures, which apply a 0% weighting to Ginnie Mae MBS and a 20% weighting to Fannie and

Freddie MBS. Following the LCR’s implementation, Gete and Reher (2018) document a subsequent reduction in the liquidity premium for Ginnie Mae MBS relative to the other two. I

20 In unreported results, I include a measure of Federal Reserve holdings of Treasuries and MBS to control for the effects of quantitative easing. My results are unchanged.

30 address the issue by exploiting within-agency variation in the frequency of HTM classifications by MBS structure and by estimating models focused exclusively on Fannie Mae and Freddie

Mac MBS.

The LCR should similarly affect both pass-through RMBS and interest and principal-only

(IO/PO) collateralized mortgage obligations (CMOs) because the regulation does not treat these securities differently within agency. However, the HTM treatment should only be observed for pass-through RMBS because IO/PO CMOs are infrequently classified as HTM.21 I therefore expect to see Fannie Mae and Freddie Mac liquidity worsen relative to Ginnie Mae for pass- through RMBS but not for the IO/PO CMOs.

In a final control for the effect so the LCR, I also compare changes in liquidity of the

RMBS issued by Fannie Mae and Freddie Mac. While my data does not distinguish HTM classifications of Fannie Mae MBS from Freddie Mac MBS, J.P. Morgan Chase explicitly listed

Fannie Mae, and not Freddie Mac, MBS as comprising the bulk of its HTM classifications in its

2017 annual report. This anecdotal evidence leads me to believe that, if there is any difference between the two, the constraining effects of HTM should be most visible for Fannie Mae MBS. I therefore expect liquidity to worsen for Fannie Mae MBS relative to Freddie Mac MBS after the final rule is issued.

I examine 30-year agency pass-through RMBS and IO/PO CMOs in these falsification tests. The constraining effects of HTM should be most evident in 30-year securities because long-dated securities benefit most from HTM’s regulatory hedge of interest rate risk and are

21 This is due to the combination of the need to hedge IO CMOs with interest rate derivatives and the prohibition of hedge accounting for interest rate hedges of HTM securities under SFAS 133. Due to SFAS 133, a hedge IO CMO classified as HTM would create an accounting mismatch that exposes the bank to the very regulatory capital volatility that HTM classifications are meant to remove. If a bank hedges an HTM security with an interest rate derivative, the derivative will be market to market while the underlying HTM security will not.

31 therefore more frequently classified as HTM (Fuster and Vickery (2019)). I focus on to-be- announced, or TBA, markets for RMBS because they are the most actively traded markets for

MBS. For more details on TBA markets, see Gao, Schultz, and Song (2017).

Table 11 demonstrate that, indeed, turnover worsens for Fannie Mae MBS relative to both Ginnie Mae and Freddie Mac MBS during the treatment period. Daily turnover of Fannie

Mae 30-year pass-through RMBS fell by approximately 0.0101 relative to Ginnie Mae MBS after the final rule of the AOCI filter removal was determined. In terms of economic significance, this relative declines in turnover represents approximately 20% of this security’s sample average. There is no evidence, however, that daily turnover of Freddie Mac’s 30-year pass-through MBS fell relative to Ginnie Mae. Importantly, there is no indication that turnover changed for any agency’s IO/CO CMOs relative to the others. This supports the crowding out hypothesis because such securities did not receive the HTM treatment.

I conduct the same analysis in columns 3 and 4 of Table 11 but replace daily turnover with number of daily trades as the dependent variable. When comparing all three agencies, I find no differential effects between Ginnie Mae and Fannie Mae or Freddie Mac MBS. However, the daily number of trades of Fannie Mae’s IO/PO CMOs increase following the final rule. This is consistent with Fannie Mae’s 30-year MBS being constrained by HTM classifications while liquidity improved for its unconstrained IO/PO CMOs. While I do not find clear evidence of worsening liquidity in every place I would expect, I do find evidence consistent with HTM’s constraining effect on Fannie Mae’s 30-year pass-through MBS, and that is the agency for which

I expect the greatest effects of HTM.

I next estimate monthly market liquidity measures using the following model:

32 퐿푖푞푢푖푑푖푡푦, = 훽0 + 훽1 퐹푎푛푛푖푒i,t∗ 푃표푠푡t ∗ 푄푡푟퐸푛푑t + 훣2 퐹푎푛푛푖푒i,t∗ 푃표푠푡t +

훽3 퐹푟푒푑푑푖푒i,t∗ 푃표푠푡t ∗ 푄푡푟퐸푛푑t + 훣4 퐹푟푒푑푑푖푒i,t∗ 푃표푠푡t +

훽5푃표푠푡t ∗ 푄푡푟퐸푛푑t + 훣6 푃표푠푡t + 훣7 푄푡푟퐸푛푑t + δ+ λ + ε,

In this case, the dependent variable is either the Amihud measure of liquidity (averaged monthly) or the monthly percentage of interdealer trades for agency i in month t. Amihud is calculated following Amihud (2002) and estimates the price impact of a trade while taking its size into account. I average both measures by agency across all days in month i. A greater percentage of interdealer trades is indicative of greater reluctance to provide market liquidity on the part of the dealer because it indicates dealers, rather than absorbing the customer order imbalances into their own balance sheets, are spreading order imbalances across the dealer network (Schultz (2017)).

For both measures, a larger number indicates less market liquidity (or greater illiquidity).

The Fannie, Freddie, and Post dummy variables are the same as in the previous model.

In some specifications, I also include the variables QtrEnd, which takes the value of one in the last month of the quarter and a zero otherwise. The idea here is that banks are most likely to make HTM classifications near the end of the quarter to delay their forfeiture of the right to sell those securities. This would prolong balance sheet liquidity without altering regulatory capital stability. If the end of the quarter is when capital is being shifted from dealer inventories to HTM portfolios, it is at those times I expect to see the greatest reductions in market liquidity.

Table 12 demonstrates that the Amihud measure, which is increasing in illiquidity, rises for Fannie Mae and Freddie Mac MBS relative to Ginnie Mae MBS in the months following the final rule. Importantly, these effects are largely concentrated in the last months of each quarter.

This is the time when HTM classifications are being made, and that capital is being withdrawn

33 from another place on the balance sheet, perhaps dealer inventories. I find no differential effects between Fannie Mae and Freddie Mac MBS.

Finally, I examine the intradealer trade volume as a percentage of total trade volume in

Table 13. Here I find strong statistical evidence that this measure of illiquidity rose for Fannie

Mae MBS relative to Ginnie Mae. Contrary to my predictions, this measure of liquidity improves for Freddie Mac relative to Ginnie Mae following the final rule in the first specification.

However, the effect is weak and not statistically significant once the quarter-end variables are included. Finally, there is little sign of differential changes between Fannie Mae and Freddie

Mac. However, measures of liquidity can be quite noisy and are notorious for not capturing all aspects of liquidity. That is why I test multiple measures.

The general theme across these different measures of market liquidity is that market liquidity worsened most for the securities most likely to be classified as HTM at the times they were being classified as HTM. Some measures of market liquidity for Fannie Mae and Freddie

Mac MBS fell relative to Ginnie Mae MBS. No measures showed liquidity worsening for the securities rarely classified as HTM. Furthermore, Figure 10 illustrates dramatic reductions in issuances of both Fannie Mae and Freddie Mac MBS relative to Ginnie Mae MBS. This depressed issuance activity is also consistent with reduced secondary market liquidity.

Collectively, this analysis of market liquidity completes the chain of cause and effect from the

AOCI filter removal to rising HTM classifications to falling dealer inventories to worsening market liquidity.

1.6. Conclusion

I have shown that the AOCI filter removal encourages banks to classify more fixed income securities as HTM. These HTM classifications are funded to a large extent by

34 withdrawing capital from the broker-dealer subsidiaries responsible for fixed income market making activities. At both the bank level and in aggregate, banks reduce fixed income dealer inventories at the same time they are increasing HTM classifications (i.e. following the AOCI filter removal). Importantly, there is no evidence of such treatment effects in bank-affiliated dealers’ non-fixed income holdings, ruling out alternative explanations that either exempt or do not specifically penalize fixed income securities.

I further demonstrate that agency RMBS feature most prominently in HTM portfolios and use this fact to strengthen the causal link between HTM classifications, dealer inventories, and market liquidity. I show that the same asset classes that are most often classified as HTM (i.e. agency RMBS) also experience reductions in dealer inventories, but similar assets that are less often classified as HTM do not (e.g. agency CMBS). Finally, I demonstrate that HTM classifications, through the dealer inventory channel, constrain banks’ liquidity provisions to capital markets, which consequently leads to lower levels of liquidity in the capital markets for securities most prone to HTM classification.

These findings have important implications for policymakers, practitioners, and researchers alike. They provide policymakers with clear evidence of a previously undocumented consequence of marking regulatory capital to market. This should be included in any cost-benefit analysis undertaken to assess the net effect of this precise regulatory change. Practitioners should be mindful of the fact that the largest fixed income market makers have elected to reduce their inventory capacity in favor of regulatory capital stability. Large institutional investors may find it particularly expensive to exit crowded fixed income positions as interest rates rise or some other shock causes investors to sell en masse. Finally, researcher may take note of the identification

35 strategies used herein and attempt to find other regulatory changes with similar features that facilitate clean identification of their distinct consequences.

36 Figure 1.1. Constrained Fixed Income Dealer Inventories

This graph illustrates the time series of aggregate dealer inventories of U.S. Treasury and agency securities, mortgage-backed securities, and corporate bonds from January 2006 to December 2016. The image was obtained from Jamie Dimon’s 2016 Annual Letter to J.P. Morgan shareholders.

37 Figure 1.2. Imposing a Tradeoff

This graphic illustrates the costs and benefits associated with each of the three SFAS 15 classifications before and after the AOCI filter removal. Prior to the removal, securities classified as AFS could be sold without penalty and were not marked to market in regulatory capital calculations. Following the AOCI filter removal, the unrealized gains and losses of AFS securities directly affect regulatory capital ratios. As a result, each classification decision involves a tradeoff between regulatory capital stability, provided solely by HTM, and the ability to sell securities freely, permitted by AFS and trading assets.

38 Figure 1.3. Big 6 U.S. Banks Shift Capital to HTM

The top graph illustrates the time series of aggregate HTM classifications by the big six U.S. banks from 2010 to 2016. Amounts are taken as of each fiscal year-end. The bottom diagram illustrates the crowding out channel, whereby the growth in HTM classifications is funded by capital withdrawn from banks’ dealer inventories. The black arrows indicate the direction of capital flows. U.S. regulators’ initial proposal for the AOCI filter removal was issued on June 7, 2012. The final rule was issued on July 2, 2013, and implementation began on January 1, 2014. The big six U.S. banks are Bank of America (BAC), Wells Fargo (WFC), JPMorgan Chase (JPM), Citigroup (C), Morgan Stanley (MS), and Goldman Sachs (GS).

39 Figure 1.4. Growth in HTM Crowds Out Dealer Inventories

This graph illustrates held-to-maturity (HTM) classifications and dealer inventories of agency mortgage-backed securities (MBS) as a percentage of assets for advanced approach banks between Q1 2010 and Q4 2016. GSE MBS are pass-through RMBS issued by Fannie Mae and Freddie Mac. The first red line indicates the quarter before U.S. regulators’ initial proposal of the AOCI filter in June 2012. The second red line indicates the quarter before the final rule was issued in July 2013.

40 Figure 1.5. Primary Identification Strategy

This figure illustrates the natural experiment this study exploits to identify the causal effects of the AOCI filter removal on banks’ commitment to fixed income market making. This empirical design has been adopted from Chircop and Novotny-Farkas (2016) to meet the needs of this study.

41 Figure 1.6. MBS Are the Asset Class of Choice for HTM

This graph illustrates held-to-maturity (HTM) classifications by asset class as a percentage of total portfolios for advanced approach banks between Q1 2010 and Q4 2016. Asset classes include U.S. Treasuries, mortgage-backed securities (MBS), municipal bonds, and total fixed income securities. The first red line indicates the initial Basel III framework proposal, which was announced in June 2011. The second red line indicates the June 7, 2012 initial proposal for U.S. adoption of the AOCI filter removal. The third red line indicates the July 2, 2013 final rule for U.S. adoption of the AOCI filter removal.

42 Figure 1.7. HTM Classifications Exhibit Clear Treatment Effects

This graph illustrates held-to-maturity (HTM) classifications of mortgage-backed securities (MBS) as a percentage of assets for advanced approach and non-advanced approach (CCAR) bans between Q1 2010 and Q4 2016. The first red line indicates the initial Basel III proposal, which was announced in June 2011 and included the first regulatory signal of the AOCI filter removal. The second red line indicates the January 1, 2014 start of the phase-in of the AOCI filter removal exclusive to advanced approach banks.

43 Figure 1.8. The Crowding Out is Coordinating by Asset Class

The graphs illustrate aggregate year-over-year changes in held-to-maturity (HTM) and trading assets (TA) classifications by U.S. commercial banks by asset class. The top graph illustrates agency MBS, and the bottom graph illustrates U.S. Treasuries. Both asset classes are exempt from the Volcker Rule and given preferential treatment under risk-based capital requirements. Changes are calculated for the trailing twelve months at the end of the first quarter for 2013, 2014, and 2015.

44 Figure 1.9. HTM Rises and Dealer Inventories Fall in Response to Regulatory Signals

This graph illustrates quarterly changes in held-to-maturity (HTM) classifications and dealer inventories of mortgage-backed securities (MBS) as a percentage of assets for advanced approach banks between Q1 2010 and Q4 2016. The first red line indicates the initial Basel III framework proposal, which was announced in June 2011. The second red line indicates the June 7, 2012 initial proposal for U.S. adoption of the AOCI filter removal. The third red line indicates the July 2, 2013 final rule for U.S. adoption of the AOCI filter removal.

45 Figure 1.10. Negative Consequences for MBS Issuance

This graph illustrates annual MBS issuance by Fannie Mae, Freddie Mac, and Ginnie Mae from 2010 to 2016. GSE MBS are pass-through RMBS issued by Fannie Mae and Freddie Mac. Agency MBS are pass-through RMBS issued by Fannie Mae, Freddie Mac, and Ginnie Mae. The treated years, indicated by the two red lines, are 2013 and 2014 because the final rule of the AOCI filter removal was issued in July 2013.

46 Table 1.1.Variable Definitions

These tables provide definitions of the variables used in the empirical analysis.

Variables Definition HTM HTM Securities / Total Assets TA FixInc Fixed Income Trading Assets / Total Assets TA NonFixInc (Total Trading Assets – Fixed Income Trading Assets) / Total Assets Dealer Capital Log of dealer capital or dealer capital in excess of required amount Advanced Approach Dummy Variable = 1 for advanced approach banks, 0 otherwise Proposal Dummy variable = 1 in quarters following Q2 2012, 0 otherwise Final Rule Dummy variable = 1 in quarters following Q3 2013, 0 otherwise ROA Net Income before Ex. Items / Total Assets Deposits Total Deposits / Total Assets URGL Unrealized Gains or Losses on AFS and HTM securities / Total Assets Tier 1 Ratio Risk-Based Tier 1 Capital Ratio Debt Non-Deposit Liabilities / Total Assets Size Log of Total Assets Maturity (Debt Securities Maturing in 1 year or less * 1 + Debt Securities Maturing over 1 to 5 year * 3 + Debt Securities Maturing over 5 years * 7.5 years) / Total Assets TED Spread Daily or monthly average of the TED Spread Term Spread Daily or monthly average of the Term Spread Credit Spread Daily or monthly average of the Credit Spread S&P500 Return Daily or monthly return on the S&P500 VIX Daily or monthly average of the VIX MBS Outstanding Outstanding single-family MBS by agency as of quarter end

47 Table 1.2. Summary Statistics

This table provides summary statistics for bank-level variables used in the empirical analysis. Means and standard deviations are calculated by bank group (advanced approach banks are defined as banks with greater than $250 billion in total assets or greater than $10 billion in foreign assets) over the sample period, which begins in Q1 2010 and ends in Q4 2016.

Non-Advanced Approach Banks Variable Mean Standard Deviation HTM 0.0179 0.025 TA FixInc 0.0048 0.0092 TA NonFixInc 0.0061 0.0050 Size 18.44 0.408 Deposits 0.699 0.130 Loans 0.681 0.078 Debt 0.1552 0.1356 ROA 0.74% 0.81% Tier 1 Ratio 15.65 65.26 URGL 0.001 0.004 Maturity 3.322 1.250 Advanced Approach Banks HTM 0.0280 0.0333 TA FixInc 0.0496 0.0589 TA NonFixInc 0.0515 0.0598 Size 20.08 1.001 Deposits 0.522 0.222 Loans 0.382 0.220 Debt 0.355 0.240 ROA 0.91% 0.86% Tier 1 Ratio 13.64 2.359 URGL 0.002 0.002 Maturity 2.714 1.117

48 Table 1.3. Fixed Income SFAS 115 Allocations by Period This table provides the average percentage of banks’ fixed income portfolios classified as held- to-maturity (HTM), available-for-sale (AFS), and trading assets, by subperiod, for each bank group. Salable for market making is the sum of AFS and trading assets, as these are the SFAS 115 classifications that are able to be sold and can therefore facilitate market making. Advanced approach banks are defined as banks with greater than $250 billion in total assets or greater than $10 billion in foreign assets, and non-advanced approach banks include all other banks. Pre- proposal period includes quarters Q1 2010 through Q1 2012. Proposal period includes quarters Q2 2012 through Q2 2013 (the initial proposal to remove the AOCI filter was issued by regulators on June 7, 2012). Final rule period includes quarters Q3 2013 through Q4 2016 (the final rule to remove the AOCI filter was issued by regulators on July 2, 2013).

Non-Advanced Approach Banks SFAS 115 Pre-Proposal Period Proposal Period Final Rule Period Classification HTM % 5.78% 10.02% 14.78% AFS % 91.20% 87.86% 81.87% Trading Assets % 3.02% 2.13% 3.35% Salable for Market 94.22% 89.98% 85.22% Making

Advanced Approach Banks SFAS 115 Pre-Proposal Period Proposal Period Final Rule Period Classification HTM % 4.64% 7.13% 15.88% AFS % 70.37% 68.97% 62.42% Trading Assets % 24.99% 23.90% 21.71% Salable for Market 95.36% 92.87% 84.12% Making

49 Table 1.4. Changes in HTM Classifications

This table shows changes in banks’ held-to-maturity (HTM) classifications in response to Basel III’s AOCI filter removal between Q1 2010 and Q4 2016. The dependent variable is HTM/Assets. The CNF columns use model specifications based on Chircop and Novotny-Farkas (2016) in which the variables of interest are PostFinal*AA and PostProp*AA. The KKR columns use model specifications based on Kim, Kim, and Ryan (2017) in which the variable of interest is PostImp*AA. These are interactions of the dummy variable AdvApp, which takes a value of one for advanced approach banks, with the dummy variables PostProposal, PostFinal, and PostImplementation, which take the value of one after Q4 2013, from Q3 2012 to Q2 2013, and after Q2 2013, respectively. Standard bank-level controls are included and defined in Table 1. Standard errors are two-way clustered by bank and time, and test statistics are reported in parentheses ***, **, and* denote significance at one percent, five percent, and ten percent, respectively, in two-tailed tests.

Coefficient (CNF) (KKR) (CNF) (KKR) PostFinal*AA 0.0157** 0.0159** (2.18) (2.25) PostFinal 0.0058 0.0112*** (1.69) (2.85) PostProp*AA -0.0004 -0.0014 (-0.06) (-0.24) PostProposal 0.0050 0.0042 (1.50) (1.04) PostImp*AA 0.0163** 0.0162** (2.31) (2.31) PostImplementation 0.0057 0.0010 (1.33) (0.21) Bank Controls Yes Yes Yes Yes Bank FE Yes Yes Yes Yes Quarter-Year FE No No Yes Yes Obs. 784 784 784 784 R^2 0.43 0.42 0.45 0.46

50 Table 1.5. SFAS 115 Classification Correlations by Period

This table provides correlation coefficients for pairs of the dollar values of aggregate SFAS 115 classifications made by U.S. commercial banks over different subperiods. The crisis period includes Q3 2007 through Q2 2009, as commonly defined in the literature. The post-crisis period includes Q3 2009 through Q1 2012. The AOCI period includes Q2 2012 (the quarter in which the initial proposal was issued) through Q 2014. The Volcker period begins in Q2 2014, as defined by Bao, O’Hara, and Zhou (2018), and ends with the sample at Q4 2015. P-values are presented in parentheses. ***, **, and* denote significance at one percent, five percent, and ten percent, respectively, in two-tailed tests.

Crisis Period: Q3 2007 – Q2 2009 Correlation HTM AFS Trading Assets Coefficients HTM 1.0000 AFS 0.7259** 1.0000 (0.0415)

Trading Assets -0.3917 -0.5939 1.0000 (0.3372) (0.1206)

Post-Crisis Period: Q3 2009 – Q1 2012 Correlation HTM AFS Trading Assets Coefficients HTM 1.0000 AFS 0.4991 1.0000 (0.1180)

Trading Assets 0.0579 0.0142 1.0000 (0.8656) (0.9669)

51 Table 1.5 (Continued)

AOCI Period: Q2 2012 – Q1 2014 Correlation HTM AFS Trading Assets Coefficients HTM 1.0000 AFS -0.8802*** 1.0000 (0.0039)

Trading Assets -0.9016*** 0.9504*** 1.0000 (0.0022) (0.0003)

Volcker Period: Q2 2014 – Q4 2016 Correlation HTM AFS Trading Assets Coefficients HTM 1.0000 AFS 0.7781*** 1.0000 (0.0048)

Trading Assets -0.5856* -0.5118 1.0000 (0.0584) (0.1075)

52 Table 1.6. Withdrawing Dealer Capital

This table shows changes in banks’ dealer inventories in response to Basel III’s AOCI filter removal between Q1 2010 and Q4 2016. The dependent variables are either FixInc or NonFixInc, which are banks’ dealer inventories of fixed income and non-fixed income securities, respectively, scaled by assets. Columns 1 and 3 use model specifications based on Chircop and Novotny-Farkas (2016) in which the variables of interest are PostFinal*AA and PostProp*AA. The KKR columns use model specifications based on Kim, Kim, and Ryan (2017) in which the variable of interest is PostImp*AA. These are interactions of the dummy variable AdvApp, which takes a value of one for advanced approach banks, with the dummy variables PostProposal, PostFinal, and PostImplementation, which take the value of one after Q4 2013, from Q3 2012 to Q2 2013, and after Q2 2013, respectively. Standard bank-level controls are included and defined in Table 1. Standard errors are two-way clustered by bank and time, and test statistics are reported in parentheses ***, **, and* denote significance at one percent, five percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Net Capital) (Excess Capital) (Net Capital) (Excess Capital) Implement*HTM -1.3471** -1.9824*** (-2.30) (-3.39) HTM 2.0339** 1.3458 (2.41) (1.62) Implementation 0.2229*** 0.1973** 0.2512*** 0.2267*** (2.84) (2.42) (3.20) (2.78) Implement*AA -0.0738*** -0.1139*** (-2.86) (-4.21) Broker-Dealer Yes Yes Yes Yes FE Quarter-Year FE Yes Yes Yes Yes Obs. 3,423 3,422 3,423 3,422 Adj. R^2 0.98 0.98 0.98 0.98

53 Table 1.7. Bank-Affiliated Dealer Inventories

This table shows changes in banks’ held-to-maturity (HTM) classifications in response to Basel III’s AOCI filter removal between Q1 2010 and Q4 2016. The dependent variable is HTM/Assets where the HTM classifications are limited to the category named in the column header. FNMA/FHLMC (GNMA) RMBS are pass-through residential mortgage-backed securities issued by Fannie Mae and Freddie Mac (Ginnie Mae). Model specifications based on Chircop and Novotny-Farkas (2016) in which the variables of interest are PostFinal*AA and PostProp*AA. These are interactions of the dummy variable AdvApp, which takes a value of one for advanced approach banks, with the dummy variables PostProposal and PostFinal, which take the value of one after Q4 2013 and from Q3 2012 to Q2 2013, respectively. Standard bank- level controls are included and defined in Table 1. Standard errors are two-way clustered by bank and time, and test statistics are reported in parentheses ***, **, and* denote significance at one percent, five percent, and ten percent, respectively, in two-tailed tests.

Coefficient (FixInc) (FixInc) (NonFixInc) (NonFixInc) PostFinal*AA -0.0075*** -0.0000 (-2.82) (-0.01) PostFinal -0.0113*** -0.0002 (-3.06) (-0.11) PostProp*AA 0.0027 -0.0006 (0.79) (-0.34) PostProposal -0.0026 0.0021 (1.02) (1.38) PostImp*AA -0.0062** -0.009 (-2.12) (-0.27) PostImplement -0.0079** -0.0012 (-2.62) (-0.98) Bank Controls Yes Yes Yes Yes Bank FE Yes Yes Yes Yes Quarter-Year FE Yes Yes Yes Yes Obs 784 784 784 784 R^2 0.45 0.45 0.14 0.14

54 Table 1.8. Changes in HTM Classifications by MBS Type

This table shows changes in banks’ dealer inventories between Q1 2010 and Q4 2016. The dependent variable All MBS, Agency RMBS, and Agency CMBS are banks’ dealer inventories of mortgage-backed securities (MBS), pass-through residential MBS issued by agencies (i.e. Fannie Mae, Freddie Mac, and Ginnie Mae), and commercial MBS issued by federal agencies. Model specifications based on Chircop and Novotny-Farkas (2016) in which the variables of interest are PostFinal*AA and PostProp*AA. These are interactions of the dummy variable AdvApp, which takes a value of one for advanced approach banks, with the dummy variables PostProposal and PostFinal, which take the value of one after Q4 2013 and from Q3 2012 to Q2 2013, respectively. Standard bank-level controls are included and defined in Table 1. Standard errors are two-way clustered by bank and time, and test statistics are reported in parentheses ***, **, and* denote significance at one percent, five percent, and ten percent, respectively, in two- tailed tests.

Coefficient (All MBS) (FNMA/FHLMC (GNMA RMBS) (Agency CMBS) RMBS) PostFinal*AA 0.0073 0.0065* -0.0006 0.0002 (1.06) (1.90) (-0.37) (0.17) PostFinal 0.0114** 0.0001 0.0039*** 0.0028 (2.67) (0.07) (2.94) (1.60) PostProp*AA 0.0006 0.0041** 0.0004 0.0003 (0.11) (2.22) (0.19) (0.91) PostProposal -0.0003 -0.0033** 0.0003 -0.0000 (-0.10) (-2.45) (0.21) (-0.03) Bank Controls Yes Yes Yes Yes Bank FE Yes Yes Yes Yes Quarter-Year FE Yes Yes Yes Yes Obs. 784 784 784 672 R^2 0.39 0.43 0.28 0.21

55 Table 1.9. Bank-Affiliated Dealers MBS Inventories

This table shows the effects of HTM classifications on the capital allocated to bank-affiliated broker-dealers between Q1 2010 and Q4 2016. The dependent variables is Net Capital in columns 1 and 3 and Excess Capital in columns 2 and 4. Model specifications are adapted from Kim, Kim, and Ryan (2018) such that the variable of interest is Implementation*HTM in columns 1 and 2 and Implementation*Adv.App. in columns 3 and 4. Implementation takes the value of one after Q4 2013. Standard errors are clustered by broker-dealer, and test statistics are reported in parentheses. ***, **, and * denote significance at the one percent, five percent, and ten percent levels, respectively, in two-tailed tests.

Coefficient (All MBS) (Agency RMBS) (Agency CMBS) PostFinal*AA -0.0014* -0.0014** 0.0000 (-2.04) (-2.53) (0.12) PostFinal -0.0038** -0.0016* 0.0001*** (-2.75) (-1.72) (3.09) PostProp*AA 0.0023* 0.0023** -0.0001 (1.78) (2.21) (-0.92) PostProposal -0.0020** -0.0015** 0.0001* (-2.37) (-2.57) (1.84) Bank Controls Yes Yes Yes Bank FE Yes Yes Yes Quarter-Year FE Yes Yes Yes Obs. 784 784 672 R^2 0.35 0.27 0.19

56 Table 1.10. Fixed Income Trading Revenue

This table estimates banks’ quarterly fixed income trading revenue using two-stage least squares instrumental variable approach. The endogenous variable of interest, Dealer Capital, is instrumented by held-to-maturity (HTM) classifications in response to Basel III’s AOCI filter removal. In the first two columns, Dealer Capital is the log of regulatory capital. In the second two columns, Dealer Capital is the log of regulatory capital in excess of required levels. The dependent variable is quarterly trading revenue scaled by total assets. The instruments are HTM and the interaction term Imp*HTM. Implementation is a dummy variable taking the value of one during quarters following the January 1, 2014 implementation of the AOCI filter removal. Standard bank-level controls Size and ROA are included and defined in Table 1. Robust standard errors are used. Test statistics are reported in parentheses. ***, **, and * denote significance at one percent, five percent, and ten percent, respectively, in two-tailed tests.

Coefficient (1st Stage) (2nd Stage) (1st Stage) (2nd Stage) Dealer Capital 0.0006** 0.0007** (2.17) (2.16) Implement*HTM -10.111** -10.030** (-2.51) (-2.55) HTM -3.687 -3.287 (-1.09) (-1.00) Size & ROE Yes Yes Yes Yes Controls Quarter-Year FE Yes Yes Yes Yes F-Stat 27.93 25.87 Obs. 224 224 224 224 R^2 0.50 0.02 0.50 0.03

57 Table 1.11. Turnover and Daily Trades by Agency

This table tests for changes in daily turnover and number of trades for 30-year TBA single family RMBS and interest-only (IO) and principal-only (PO) collateralized mortgage obligations (CMOs) issued by Fannie Mae, Freddie Mac, and Ginnie Mae. The sample period is May 17, 2011 through December 30, 2016. The variables of interest are Fannie*Post and Freddie*Post. These are interactions of the dummy variable Post, which takes a value of one after the final rule regarding the AOCI filter removal was issued July 2, 2013, with the dummy variables Fannie and Freddie, which take the value of one for Fannie Mae and Freddie Mac observations, respectively, and zero otherwise. Macroeconomic controls are included and defined in Table 1. Standard errors are clustered by issuer. Test statistics are reported in parentheses ***, **, and * denote significance at one percent, five percent, and ten percent, respectively, in two-tailed tests.

Daily Turnover Number of Trades Coefficient (30yr MBS) (IO/PO CMO) (30yr MBS) (IO/PO CMO) Fannie*Post -0.0101** -0.0000 -537.27 9.819** (-4.37) (-1.46) (-2.89) (4.91) Freddie*Post -0.0023 -0.0001 -182.27 7.502 (-0.78) (-2.04) (-0.75) (2.72) Post -0.0037 0.0001 -294.37 -3.024 (-0.84) (1.52) (-0.65) (-0.89) Controls Yes Yes Yes Yes Weekday FE Yes Yes Yes Yes Agency FE Yes Yes Yes Yes Obs. 4,140 4,140 4,140 4,140 Adj. R^2 0.61 0.21 0.86 0.05

58 Table 1.12. Monthly MBS Amihud Measure by Agency

This table tests for changes in the average monthly Amihud liquidity measure for mortgage- backed securities issued by Fannie Mae, Freddie Mac, and Ginnie Mae and traded in the TBA market. The sample period is June 2011 through May 2015. The variables of interest are Fannie*Post, Freddie*Post, Fannie*Post*QtrEnd, and Freddie*Post*QtrEnd. These are interactions of the dummy variable Post, which takes a value of one after the final rule regarding the AOCI filter removal was issued July 2, 2013, with the dummy variables Fannie and Freddie, which take the value of one for Fannie Mae and Freddie Mac observations, respectively, and zero otherwise. The last two are also interacted with QtrEnd, which takes a value of one during the last month of each quarter (i.e. March, June, September, and December), and zero otherwise. Macro controls are included and defined in Table ***. Coefficients are scaled to facilitate interpretation, and standard errors are clustered by issuer. Test statistics are reported in parentheses ***, **, and * denote significance at one percent, five percent, and ten percent, respectively, in two-tailed tests.

All 3 Agencies Fannie & Freddie Coefficient (1) (2) (3) (4) Fannie*Post 2.59** 67.5 -1.06 -1.17 (6.97) (1.37) (4.28) (-0.87) Fannie*Post*QtrEnd 6.10*** 49.00 (23.96) (0.50) Freddie*Post 3.33** 1.60* (6.10) (3.61) Freddie*Post*QtrEnd 5.37** (6.07) Post*QtrEnd -2.32** 3.36 (-5.95) (4.24) Post 2.94 3.72 6.56 5.69 (0.48) (0.55) (1.04) (0.71) QtrEnd 39.6 -2.38 (1.44) (-2.52) Macro Controls Yes Yes Yes Yes Agency FE Yes Yes Yes Yes Obs. 124 124 129 129 Adj. R^2 0.14 0.11 0.10 0.07

59 Table 1.13. Monthly MBS Intradealer Trade Percentage by Agency

This table tests for changes in the monthly percentage of intradealer trades for mortgage-backed securities issued by Fannie Mae, Freddie Mac, and Ginnie Mae and traded in the TBA market. The sample period is June 2011 through May 2015. The variables of interest are Fannie*Post, Freddie*Post, Fannie*Post*QtrEnd, and Freddie*Post*QtrEnd. These are interactions of the dummy variable Post, which takes a value of one after the final rule regarding the AOCI filter removal was issued July 2, 2013, with the dummy variables Fannie and Freddie, which take the value of one for Fannie Mae and Freddie Mac observations, respectively, and zero otherwise. The last two are also interacted with QtrEnd, which takes a value of one during the last month of each quarter (i.e. March, June, September, and December), and zero otherwise. Macro controls are included and defined in Table ***. Coefficients are scaled to facilitate interpretation, and standard errors are clustered by issuer. Test statistics are reported in parentheses ***, **, and * denote significance at one percent, five percent, and ten percent, respectively, in two-tailed tests.

All 3 Agencies Fannie & Freddie Coefficient (1) (2) (3) (4) Fannie*Post 0.2071** 0.2054* 0.3513 0.3774 (4.28) (3.36) (2.66) (2.98) Fannie*Post*QtrEnd 0.0131 -0.0699 (0.13) (-0.56) Freddie*Post -0.1337* -0.1700 (-3.30) (-2.61) Freddie*Post*QtrEnd 0.1030 (1.18) Post*QtrEnd 0.0878 0.1728 (1.20) (1.41) Post 0.0400 -0.0220 -0.0739 -0.1707 (0.83) (-0.24) (-0.44) (-2.52) QtrEnd -0.0634 -0.1494 (-0.82) (-1.24) Macro Controls Yes Yes Yes Yes Agency FE Yes Yes Yes Yes Obs. 129 129 129 129 Adj. R^2 0.41 0.40 0.49 0.49

60 CHAPTER 2: ON THE MARKET EFFECTS OF INSIDER TRADING: EVIDENCE FROM THE NBA’S MOST NOTORIOUS REFEREE

2.1. Introduction

Does insider trading degrade financial markets? Academic theories predict both desirable and undesirable consequences stemming from insider trading, and empirical tests – hamstrung by selection bias, omitted variables, and confounding corporate events – come to similarly conflicting conclusions. An ideal experiment would randomly assign inside traders across a sample of securities to estimate their causal effect on different measures of market quality. Of course, this ideal experiment is virtually impossible to find in financial markets. Researchers typically do not observe when insiders trade on material, non-public information, and even when they do, nothing about the assignment of the treatment is random. Traders are not randomly assigned the inside information. Nor are they randomly assigned the inclination to trade upon it.

Nor are they randomly assigned regulators’ scrutiny and decision to prosecute (thereby making the incident known to researchers). In this study, I exploit a unique setting in which inside traders were as good as randomly assigned to different securities, thus providing a potentially cleaner experiment than can be found in financial markets.

The improprieties of disgraced NBA referee Tim Donaghy provide an appealing laboratory for testing the theoretical predictions of insider trading’s effects on market quality.

Donaghy was indicted following the 2006-2007 NBA seasons for placing bets on games he refereed. Perhaps unsurprisingly, his bets demonstrated remarkable foresight. Whether one believes that foresight came from the official’s unique insights to the game or from his ability to influence the outcomes, he was an insider with material, non-public information about the value of the Arrow-Debreau securities (i.e. sports betting contracts) associated with his games.

61 The most appealing feature of this setting is that the inside trader treatment is, in effect, randomly assigned. The NBA assigns its referees to games rather than the referees choosing their own games. This importantly keeps the assignment of the insider treatment out of the insider’s control. It is hard to conceive that the NBA was aware of and allowing for Donaghy’s activities, let alone that it was adjusting its referee assignments in response. Thus, the insider’s assignments to different securities (i.e. games) are almost certainly exogenous to the causal relationships of interest. This assumption is supported by comparisons of key variables between the treated and control group. During the sample period, Donaghy referees 27 different home teams and 25 different away teams out of 29 teams in the sample.22

Another appealing feature of this unique setting is that it is not hampered by the joint hypothesis problem that plagues tests of market efficiency in financial markets (Fama (1970)).

That is the idea that any test of market efficiency in financial markets is also inherently a test of the chosen equilibrium asset pricing model. If one finds evidence of market inefficiency, it is unclear whether the market is truly inefficient, or the asset pricing model is flawed. Contrary to publicly traded stocks, the terminal values of sports betting contracts are revealed quickly, definitively, and frequently (Moskowitz (2018)). Each point spread and probability of winning implied by the money line is a forecast of the outcome for that particular game, and the predictive powers of these forecasts can be evaluated against observed outcomes. Furthermore, the well-known feedback loop between stock prices and corporate behavior is not present in sports betting markets in which athletes generally try to win each game regardless of betting prices. Put simply, I can more definitively compare the relative price efficiencies of two different

22 There are 30 NBA franchises. However, I did not have complete data for the Seattle Supersonics (later the Oklahoma City Thunder) franchise.

62 sports betting contracts than I can for the relative price efficiencies between Apple stock and

Microsoft stock.

The primary data source is sportsbookreview.com, which provides historical data for sports betting contracts going back more than a decade. Each game in the sample contains time series data for three different contracts: the spread, the money line, and the over/under for total points.23 For example, the January 18, 2007 game between the Indiana Pacers and the Miami

Heat contains a time series of 18 different money line prices posted between 5:20 am EST and

2:06 pm EST on the day of the game. I construct several variables associated with the bid-ask spread, volatility, and market efficiency for each game-contract observation. My sample consists of approximately 1,197 games with point spreads (1,190 games with money lines) played during the 2006-2007 NBA season, 66 of which were officiated by Donaghy. I do not examine over/under bets as Donaghy was not known to bet these contracts, and it is unclear in which direction his preference for a team would influence such bets.

I start by validating my identification strategy’s key assumption, that Donaghy’s assignment to different games is orthogonal to any other variables correlated with the variable of interest. In other words, the Donaghy treatment is as good as randomly assigned. To do so, I compare the treated and control groups along several ex-anted game characteristics. I find no evidence of systematic differences between the groups in terms of home or away teams’ absolute or relative skills or attendance at the games. Furthermore, every team played in multiple treated and control games. Of the 29 different teams in my sample, Donaghy officiated 27 different home teams and 25 different away teams. This league-wide dispersion across just 66 treated

23 These different types of sports betting contracts will be explained more fully in Section IV.

63 games alleviates any concern that the treatment simply clustered in a small subset of teams that were inherently different from the control group.

Next, I investigate whether the trading strategy employed by Donaghy and his associates caused abnormal market behavior. I find clear evidence that treated games were associated with greater price movements, and these movements typically favored away teams. After controlling for the opening (by far the strongest predictor of the closing spread), the closing spread is pushed

0.49 points in favor of the away team for Donaghy’s games relative to games not officiated by

Donaghy. This effect removes approximately 14.2% of the sample’s average home field advantage and suggests the insiders preferred betting on the away teams.

Furthermore, Donaghy reportedly arranged the scheme with Jimmy Batista on December

12, 2006 (ESPN (2019)). This dramatically expanded the information network, from Donaghy himself to a mob-affiliated co-conspirator willing to bet far greater sums of money. It is therefore reasonable to expect the treatment effects to become more significant after this date, and that is precisely what I find. Multiple measures of market volatility increase for Donaghy’s games relative to non-Donaghy games only after the agreement was reached between Donaghy and his co-conspirator. For example, both the standard deviation and absolute move of prices approximately doubled for money line contracts following the December 12 meeting. Similarly, the standard deviation of the point spread increased between 50-75% for those same games.

Furthermore, Donaghy’s games are associated with point spread ranges that are larger than point spread moves from open to close. This implies a greater degree of price fluctuation for

Donaghy’s games as the price is not merely moving directly from the opening to closing line in a manner that efficiently incorporates new information. It is precisely the time period in which the information network expanded that we see the most abnormal price behavior occur.

64 I then move on to test the insiders’ effects on market liquidity.24 The seminal papers on informed trading, such as Kyle (1985), predict that market makers will protect themselves by moving the price further for a given customer order imbalance when they suspect a heightened risk of adverse selection. Consistent with this theory, I find that the prices for Donaghy’s games during this post-December 12 period are approximately 12% more likely to exhibit discontinuous jumps, which I define as movements in the point spread greater than the minimum size of 0.5 points. Furthermore, the number of price updates by the market makers increased approximately

50% for spread contracts in the period immediately following the December 12 meeting. Again, this is consistent with a greater sensitivity of market prices to customer order flow as market makers provide less liquidity. Finally, I examine bid-ask spreads (“vig” or “vigorish,” in the gamblers’ parlance). In the final sample period, from March 1 to the end of the season, market makers widen the bid-ask spread for the opening line in Donaghy’s games, before any customer orders have been taken. This is again consistent with theories such as Glosten and Milgrom

(1985) and suggests that it took some time for the market makers to learn the common link between the games with abnormal price behavior (i.e. they were all games officiated by

Donaghy).

In my last group of tests, I examine the insiders’ effect on market efficiency. One benefit of this setting is that market efficiency is far easier to test in sports betting markets than in financial markets. A point spread is efficient if it is an unbiased estimate of the realized point differential between the two teams. I find the closing spreads of Tim Donaghy’s games become

24 Throughout this paper, any mention of “liquidity” is in reference to market liquidity, as distinct from funding liquidity (see, for example, Brunnermeir and Pederson (2009)). I examine market liquidity because its provision is quite comparable between sports betting markets and financial markets. Furthermore, theories of market maker response to insider trading focus on the provision of market liquidity by dealers to traders. Finally, the sources of funding for sports book market makers and financial market makers are too different to conduct meaningful tests of the inside traders’ effects on funding liquidity.

65 more efficient in the period immediately following the December 12 meeting. This coincides with the period of greatest price movements and suggests those price movements made markets more efficient. In the final sample period, however, closing point spreads prove less predictive of realized outcomes. Donaghy creates an average forecast error in that period that is similar in magnitude to ignoring the average team’s home court advantage. This period was also characterized by less dramatic movements in the point spreads for Donaghy’s games along with an increased bid-ask spread for those same contracts. Naturally, less aggressive trading by insiders will do less to incorporate their information into prices, leading to less efficient markets.

My paper contributes to the extensive literature related to insider trading. I speak to the debate on whether insider trading is beneficial or harmful to market efficiency, which dates back at least as far as Manne (1996) and includes contributions from Carlton and Fischel (1983) as well as Fishman and Hagerty (1992). I find that insiders do improve the informational efficiency of market prices by incorporating their information via trading. However, my evidence suggests there may be strategic manipulation by insiders to obtain better prices. Therefore, the insiders’ information is impounded over time and not always fully rather than in a quick one-time adjustment that fully incorporates the insiders’ the insiders’ information set.

I also provide evidence supporting theories of market makers’ defensive responses to insider trading. Many of the empirical tests to date provide conflicting evidence, but my setting circumvents the many empirical challenges that likely contribute to the confusion. I show that prices are more likely to exhibit jump behavior and are updated more frequently when exposed to the insider treatment. In other words, market makers seem more prone to adjust prices for a given level of order flow. Furthermore, market makers increase the bid-ask spread, another

66 mechanism for decreasing the profitability of insider trading. In short, market makers provide less liquidity when they fact greater adverse selection risk.

2.2. Literature Review

Researchers and policymakers have focused much attention on the topic of insider trading. Researchers propose and have made efforts to test theories related to its effects on market functioning. Policymakers impose regulatory burdens on insiders, forcing disclosure or prohibiting trades during certain periods, to mitigate the negative consequences or perception of unfairness in financial markets. Names like Ivan Boesky and Martha Stewart quickly come to mind as examples of high-profile cases. The resulting literature is therefore quite immense and can be broken up into two subsections: liquidity provisions and price discovery. Moreover, an interesting but underdeveloped branch of the literature has emerged which uses the sports betting setting as a unique laboratory to conduct tests of the literature’s theoretical predictions.

2.2.1. Liquidity Provisions

Bagehot (1971)25 explained that market makers gain when transacting with liquidity and noise traders but lose when transacting with informed traders. To run a profitable operation, the market maker should therefore maximize her dealings with the former two and minimize her dealings with the latter. The difficulty, however, is in accurately assessing in what proportions each kind comprise the market at any given time.

As this literature developed, the market maker’s dilemma has been given more rigorous treatment and been applied to various specialized settings. Early works by Copeland and Galai

(1983) and Glosten and Milgrom (1985) model the bid-ask spread as the market maker’s tool for

25 This is Jack Treynor writing under a pseudonym of the late 19th-century financial journalist.

67 maximizing profit. The market maker adjusts the width of the bid-ask spread, trading off the reduction in losses to informed traders caused by a wider spread against the foregone profits from uninformed traders with reservation prices inside that spread. Kyle (1985) emphasized the price impact, or sensitivity of price for a given level of customer order flow, and theorized that prices would become more sensitive to order flow if the market maker suspected a greater degree of informed trading.

Later models by Kavajecz (1998) and Dupont (2000) recognized quote depth as an additional tool in the market maker’s tool kit. Market makers can protect themselves from informed traders by reducing depth, thereby making prices more sensitive to order flow. This increases trading costs and discourages informed trading by making it less profitable. While these models suggest market makers will adjust both the bid-ask spread and market depth,

Dupont shows that quoted depth will be adjusted proportionally more than bid-ask spreads in the presence of large informed orders.

Many empirical studies have attempted to test these theoretical predictions over the years, but the accumulated evidence remains inconclusive. Some studies find evidence supporting the theories above, demonstrating that market liquidity does in fact worsen in scenarios more likely to be associated with informed trading. However, other studies find no evidence of change or even counterintuitive results that suggest liquidity actually improves in the presence of informed trading. The reason for such conflicted findings is the difficulty of identifying settings that provide clean empirical tests.

One of the difficulties for such tests is that researchers do not observe traders’ information sets. Many studies have inferred cases of informed trading based on the positions of the trader. These are usually company insiders, but researchers do not know whether the trades

68 are based on inside information or based simply on insiders’ liquidity needs. For example,

Garfinkel and Nimalednran (2003) find evidence that inside traders are better able to remain anonymous, and therefore less likely to discourage liquidity provisions, in the NASDAQ dealer system relative to the NYSE specialist system. However, these tests only infer insider trading based on the position of the traders (i.e. corporate insiders) and the size of their trades (i.e. medium-sized quantities).

Other studies have attempted to address this challenge by examining a smaller sample of cases in which traders were later prosecuted. This provides greater assurance that the cases examined do in fact involve informed trading, but it creates other empirical challenges, primarily selection biases, that may confound the results. For example, regulators may only be made aware of the insider trading when it produces sufficiently abnormal market activity, or they may only choose to prosecute the most egregious (i.e. winnable) cases. As such, these studies come to conflicting conclusions.

Cornell and Sirri (1992) examine market quality surrounding Anheuser-Busch’s 1982 acquisition Campbell-Taggart in which 38 insiders engaged in illegal insider trading. Contrary to theoretical predictions, they find no effect on bid-ask spreads and an improvement in liquidity as measured by the cost of trading an additional share. Similarly, Chakravarty and McConnell

(1997, 1999) do not find any effect on market quality stemming from Ivan Boesky’s illegal trades in Carnation stock prior to its acquisition. One explanation is that insiders chose to trade at times of heightened liquidity so as to avoid detection and maximize profits.

Fishe and Robe (2004) employ similar methodology by examining trades that were later prosecuted for insider trading. Their insiders were five stockbrokers with advanced knowledge of the stocks covered in Business Week’s Inside Wall Street column. Stocks included in the article

69 subsequently outperformed the market, and these pre-informed stockbrokers trade a subset of the

116 stocks mentioned. Fishe and Robe find that market makers adjusted both depth and spreads, as theory predicts, during the period of illegal trading. However, the experimental design is impaired by the fact that their primary treatment and control groups are segregated based on the insiders’ decision to trade or not trade each stock mentioned in the articles. This introduces self- selection bias as the insiders themselves decided which stocks would be treated.

2.2.2. Price Discovery

Price discovery is the primary basis for pro-insider trading arguments. The seminal market microstructure models of Glosten and Milgrom (1985) and Kyle (1985) predict that informed traders’ information will be impounded into market prices as market makers adjust their prices in responses to informed order flow. This process will make prices more informationally efficient than they otherwise would be, and that is an outcome we want in a market-based system that relies on price signals. However, opponents of insider trading argue that outside investors will produce and incorporate less information because insiders have made such activities less profitable (Fishman and Hagerty (1992)). The extent to which either side’s argument is valid, along with the net effect, is therefore an empirical question.

The same studies that tested market makers’ responses to insider trading also used those settings to test price discovery. Earlier studies found evidence that insiders do in fact improve price discovery (see Meulbroek (1992); Cornell and Sirri (1992); Chakravarty and McConnell

(1997)). However, Chakravarty and McConnell (1999) note a methodological error that led to those studies arriving at erroneous conclusions. The latter fails to find any evidence that insider buy orders improve stocks’ information efficiency any more than outsider buy orders.

70 Notwithstanding the methodological improvement, these studies still suffer various forms of selection bias described above.

Another strand of this literature examines how country-level measures of information efficiency are affected by the enforcement of insider trading laws. Bhattacharya and Daouck

(2002) show the cost of equity at the country level decreases following a country’s initial enforcement of insider trading laws. Similarly, Bushman, Piotrosk, and Smith (2005) find that analyst coverage increases following initial enforcement, suggesting that insider trading enforcement provides greater incentives for analysts to produce information. Finally, Fernandes and Ferreira (2009) find that the initial enforcements of insider trading laws lead to improvements in stock price informativeness. Taken together, these studies demonstrate that restrictions on insider trading improve the informational efficiency of stock prices, leading to lower costs of capital.

2.2.3. Sports Betting Markets

Many recent studies have exploited the sports betting setting to test theories of financial markets. As Moskowitz (2018) notes, one of the key features of this setting is that betting contracts’ terminal values are exogenous to betting activity. This alleviates concerns about the feedback loop between market activity and the intrinsic value of publicly traded stocks. He examines betting markets in baseball, basketball, football, and hockey to test theories of asset pricing anomalies and finds dynamics most consistent with models of investor overreaction.

Croxson and Reade (2014) and Choi and Hui (2014) each choose soccer goals as their laboratories for evaluating price discovery. Croxson and Reade (2014) examine goals scored close to halftime, a period in which there is no change in either team’s prospects of winning, to see how quickly in-game betting markets updates each team’s probability of winning. They

71 conclude that prices update swiftly and fully. Choi and Hui (2014), on the other hand, find markets generally underreact to more predictable news events (i.e. goals scored by favorites) while overreacting to those that are more surprising (i.e. goals scored by underdogs).

Finally, Brown (2012) finds evidence that bid-ask spreads increase around high- information events such as tie-breaking sets during the 2009 Wimbledon men’s tennis final. He concludes these results stem from some bettors receiving information more quickly, due to delayed television signals, rather than a superior ability to process and analyze information. In a similar setting, Bizzozero, Flepp, and Franck (2017) find that more than 60% of the full price reaction to completed sets are incorporated by those with earlier news of the results, a group they term insiders.

2.3. Empirical Setting

At a fundamental level, sports betting markets and financial markets are quite similar. A sports betting contract is simply an Arrow-Debreau security that pays off in one state (e.g. the home team wins) and does not pay off in the other (e.g. the away team wins). As noted by

Moskowitz (2018) and Levitt (2004), both markets contain investors with heterogenous beliefs and information who seek to profit from their trades. In both cases, these beliefs and information are impounded into market prices as the market maker adjusts price quotes in response to customer order flow.

There are several reasons for which the 2006-2007 NBA season provides a potentially cleaner laboratory for testing insider trading theories than those used in previous empirical studies. First, sports betting markets do not suffer from the confounding feedback loop between security market prices and the terminal value of the security that plagues the study of common stocks. The lack of influence of sports betting activity on the contracts’ terminal values allows

72 mispricing to be more cleanly detected and tests of market efficiency to be more directly performed. Furthermore, the terminal values are revealed at the ends of each game, which occur quickly and frequently (Moskowitz (2018)). This provides a direct test of market efficiency, thereby circumventing the joint hypothesis problem (Fama (1970)).

Another appealing feature of the sports betting setting is the homogenous nature of betting contracts associated with games across the league. There is little difference in the data- generating process that produces outcomes for Indiana versus Chicago, for example, relative to the one for New York versus San Antonio, particularly when compared to the far greater heterogeneity across publicly traded companies and their stocks. There are a vast range of differences across publicly traded firms when it comes to life cycles, leverage, industry dynamics, and exposures to systematic risk, just to name a few. Each could correlate with the effects of insider trading on outcomes of interest, and that makes it difficult to identify appropriate treatment and control groups. None of these are present in basketball games.

Finally, the malfeasance of NBA referee Tim Donaghy during the 2006-2007 provides an insider trading treatment that is as good as randomly assigned across the league. Following the

2006-2007 season, Tim Donaghy was indicted for betting on NBA games in which he officiated.

Not only did Tim Donaghy bet on these games, Donaghy communicated his betting advice to associates who bet significant sums of money. The inside knowledge of an NBA referee, along with the bias his bets almost certainly introduced into his officiating, is material, non-public information on the value of those sports betting securities. Furthermore, the insiders had no control over the games to which Donaghy was assigned, and those making the referee assignments almost certainly did not do so based on knowledge of Donaghy’s insider trading.

73 Thus, the assignment of the Donaghy, or insider, treatment was effectively random, and, as Table

2 shows, the treatment was dispersed widely across the league.

One concern readers may raise in the context of the market makers’ responses is that the market makers must first be aware the threat of adverse selection exists in order to respond. How can we know market makers were aware of this heightened risk? First, my initial tests of price behavior show dramatic evidence of abnormal price behavior that would be difficult for a market maker to miss and not eventually identify the common link. Second, knowledge of the scheme had spread far and wide by the spring of 2007 and may well have reached the sports betting world through the proverbial grapevine. The FBI began investigating the scheme after hearing it discussed during unrelated phone surveillance on members of the mob. Donaghy’s co- conspirators, Jimmy Batista, approached Donaghy because he learned about Donaghy’s previous, small-time betting activities. Finally, if the market makers were somehow not aware of the situation, this will simply work against my finding any treatment effects.

2.4. Data

My sample consists of all regular season and postseason games during the 2006-2007

NBA season for which I have non-missing data. I use the 2006-2007 season because that was

Tim Donaghy’s final season as a referee, and it was the season in which the information network expanded dramatically through Donaghy’s association with Jimmy Batista. This is also the earliest season for which my primary data source maintains time series data for each game’s betting prices. I obtain data from multiple sources for the 2006-2007 NBA basketball season.

Game outcomes, referees, attendance, and day of week were obtained from basketballreference.com. I also obtained ELO ratings from fivethirtyeight.com. These are time-

74 varying, team-specific ratings that estimate the team’s ability at any point in time based on a moving average of its recent games’ outcomes.26

There are three primary types of sports betting contract: the point spread, the money line, and the total, or over/under. A spread contract is a bet on either the favorite to win by at least a certain number of points (i.e. the “point spread”) or the underdog to not lose by more than that same number of points. For example, if Indiana is listed as a 3.5-point favorite over Chicago, the spread will be quoted as Indiana -3.5 as well as Chicago +3.5. Thus, a bet on Indiana to cover the point spread pays off if Indiana wins by at least four (half points are not possible), and a bet on

Chicago to cover the spread pays off if Chicago loses by no more than three or wins. A point spread is efficiently priced if it is an unbiased estimate of the realized point differential between the two teams.

A similar type of betting contract is the total, or over/under. Much like the point spread, it is an estimate of a linear combination of the two teams’ points, but in this case, the number is the sum of the teams scores rather than the difference. For example, if the total is quoted at 199.5, a bet on the over (under) will pay off if the two teams combined scores are greater (less) than

199.5. I do not examine over/under bets as Donaghy was not known to bet these contracts, and it is unclear in which direction his preference for a team would influence such bets. Finally, the money line is simply a bet on which team will win regardless of the margin of that victory. See

Moskowitz (2018) for a more comprehensive discussion.

Betting prices were manually collected from sportsbookreview.com. There are multiple sportsbooks for which sportsbookreview.com posts historical betting prices. I use Pinnacle’s betting prices for consistency and because Pinnacle is one of the “market setting” sportsbooks

26 The data, methodology, and rationale for ELO calculations are provided at https://projects.fivethirtyeight.com/complte-history-of-the-nba/.

75 whose prices other sportsbooks largely follow (Moskowitz (2018)). Each game has three contracts - point spread, money line, and total – and each of these has a time series of betting prices. My final sample consists of 2,387 betting contracts on 1,197 games. Of these, 132 contracts across 66 games were given the Donaghy treatment. A screen shot of one such game- contract observation is shown in Figure 4. Table 1 summarizes the data on sports betting contracts. There do not appear to be any statistically significant differences between Donaghy games and non-Donaghy games regarding the teams’ skill levels or attendance.

2.5. Empirical Results

2.5.1. Trading Behavior

I begin my analysis with a search for the insiders’ trading footprints. Kyle (1985) predicts sophisticated, informed traders will strategically camouflage their trading to avoid detection.

However, media reports suggest Donaghy’s co-conspirators were unsophisticated and aggressive traders (ESPN (2019)). It is safe to assume they never read Kyle (1985). Does market pricing data show any trace of abnormal trading activity for Donaghy’s games?

Figure 1 shows that Donaghy’s games (in pink) were characterized by far greater movements in the point spreads than non-Donaghy games (in blue). The point spread movements from open to close for non-Donaghy are approximately normally distributed with most of the probability mass clustering around small or even no movements. In each of the five bins in the center, which correspond to minimal point spread movements, non-Donaghy games have a much greater mass. However, as we move out further on the distribution in either direction, Donaghy’s games generally have a greater mass than non-Donaghy games. This suggests that market makers are posting less informed opening point spreads in Donaghy’s games. Figure 2 tells a similar story. The U-shaped curve indicates that games with extreme point movements in either direction

76 are disproportionately more likely to be officiated by Tim Donaghy. Although he only officiated approximately 5.78% of sample games, Donaghy officiated over 20% of games in which the point spread moved at least 3 points in either direction.

My baseline regression model estimating the closing point spread for each game is

퐶푙표푠푖푛푔푆푝푟푒푎푑, = 훽0 + 훽1 푇푟푒푎푡i,t + 훽2 푂푝푒푛푆푝푟푒푎푑i,t +

훽3 퐸퐿푂i,t + 훽4 퐴푡푡푒푛푑푎푛푐푒i,t + δ + ε,

where Treat is a dummy variable equal to one if Donaghy is one of the game’s officials, and zero otherwise. OpenSpread is the opening, or first, point spread posted by the bookmaker prior to receiving any customer bets. ELO is a measure of the difference in ability between the two teams

(calculated as away minus home). Attendance is the official attendance for the game, and day of the week dummy variables (δ) are included to absorb differences in betting activity throughout the week. If markets are informationally efficient, the opening spread should be the only variable with any predictive power. However, we would expect the Treat to have incremental predictive power if the spreads of Donaghy’s games consistently move away from their opening values, as the figures suggest.

Consistent with Figure 1, I find the closing spread moves approximately 0.5 points on average in favor of the away team in Donaghy’s games relative to what the opening spread would predict. This suggests Donaghy’s co-conspirators were consistently betting on the away team in Donaghy’s games and in large enough quantities to cause the bookmaker to move the line. Furthermore, we know from media accounts the information network expanded dramatically following the December 12 meeting between Donaghy and Jimmy Batista (ESPN

77 (2019)). I therefore split my sample into three separate time periods and rerun the same regression to determine in which period the insiders’ effects are primarily concentrated. The results in Table 3 demonstrate abnormal trading activity is clearly concentrated in the second time period, which spans from December 13 to February 28. During this time, the closing point spread in Donaghy’s games is nearly a full point more in favor of the away team beyond the line the opening point spread would suggest.

Another lever the bookmaker can adjust is the payoff of a certain wager. These payoffs are convertible into Arrow-Debreau security prices, or the implied market probability of that wager being a winning one. I run similar regressions, replacing the dependent variable with the absolute value of the change, from open to close, in price of an Arrow-Debreau security paying off one if the away team wins. In this case, I can examine the prices of both spread and money line contracts. The results are show in Table 4.

Again, I find the prices of these securities moved significantly more for Donaghy’s games than non-Donaghy games. This finding holds for both spread and money line contracts and is driven by the subperiods after the December 12 meeting. The market’s implied probability of the away team winning outright changes an average of 1.3-2.8% more for Donaghy’s games in the post-December 12 period. The market’s implied probability of the away team covering the point spread changes an average of 0.58-0.86%. The moves are smaller for the point spread bets because, in addition to changing the price of the Arrow-Debreau security, the bookmaker is also moving the point spread itself.

Now that I have characterized the trading behavior of the inside traders, I will turn my attention to answering the main research question of this paper: what effects does inside trading

78 have on market quality? There are three aspects of market quality, and I will examine each in turn. These three aspects are volatility, liquidity, and efficiency.

2.5.2. Price Volatility

There are two ways to interpret price volatility as it relates to market quality. On one hand, excessive price volatility implies a lack of liquidity because less stable prices suggests greater sensitivity to order flow. Theory predicts on way in which market makers will protect themselves from informed traders is to reduce order book depth. This would manifest itself through greater and more frequent price changes. On the other hand, a well-functioning market will quickly absorb new information and reflect that new information in a new price. We would like market prices to change as they incorporate Donaghy’s information, but we would like them to do so quickly and fully as opposed to doing so in a drifting or fluctuating fashion.

I start by examining the standard deviations of contract prices and point spreads. Each game-contract observation contains a standard deviation of contract price, and point spread observations contain a standard deviation of point spreads as well. Each is derived from the time series of posted prices and spreads for that game and contract. My baseline regression model is

푆푡푎푛푑푎푟푑퐷푒푣푖푎푡푖표푛, = 훽0 + 훽1 푇푟푒푎푡i,t + 훽2퐸퐿푂i,t + 훽3 퐴푡푡푒푛푑푎푛푐푒i,t + δ + ε,

where Treat is a dummy variable equal to one if Donaghy is one of the game’s officials, and zero otherwise. ELO is a measure of the difference in ability between the two teams. Attendance represents the official attendance for the game, and day of the week dummy variables (δ) are included to absorb differences in betting activity throughout the week. The dependent variable is one of the measures of standard deviation described above.

79 Table 7 shows the price for Donaghy’s games become more volatile relative to non-

Donaghy games after the December 12 meeting. This holds for both point spread and money line contracts, as it did for absolute changes in the prices of those contracts. However, the effects for point spread contracts are again far more economically and statistically significant in the period immediately following the December 12 meeting than in the first or third sample periods.

Differences in effects across the second and third sample periods are not as pronounced for the money line contracts.

These results are consistent with market makers reducing order book depth to protect themselves against insider trading. However, another explanation is that Donaghy’s games are associated with particularly unbalanced order flow. The latter is almost certainly the case, but the two explanations are not mutually exclusive. It is impossible to decipher the degree to which each effect is driving the results. However, we can glean additional insights by examining the frequency of price changes along with how directly the point spread moved from its opening to closing level.

I next run similar regressions, replacing measures of price volatility with the number of price updates for contracts and point spreads posted by the bookmaker. Each timestamped observation represents a change in either the price of the contract or, in the case of point spread contracts, the point spread itself. In each specification, the results clearly show a greater number of price updates by market makers in Donaghy’s games, and the effects are once again concentrated in the period immediately following the December 12 meeting. Donaghy’s games have approximately 10 more price updates in both point spread and money line contracts than non-Donaghy games. This represents a roughly 25% and 40% increase over the sample average, respectively. The point estimate increases to about 13.5 for each contract in the model including

80 a post-December 12 period. Finally, in regressions distinguishing each of the three periods, the estimated treatment effect is even greater in the second period, the only period that is significantly different from zero.

Furthermore, we can see in Table 5 that point spreads do not move monotonically from their opening to closing levels for Donaghy’s games. If that were the case, the absolute change in point spreads between the open and close would fully and precisely predict the range of point spreads, and no other variable would have any explanatory power. However, we can see that the

Donaghy treatment increases the range of the point spread above and beyond the absolute change from open to close. In other words, Donaghy’s games show greater multi-directional fluctuations than do non-Donaghy games. While a change in price to a more efficient one is evidence of efficient price discovery, this back and forth effect indicates that Donaghy’s insider trading treatment leads to less direct price discovery and therefore lower market quality.

2.5.3. Liquidity Provisions

The preceding results demonstrate abnormal price behavior for Donaghy’s games, primarily concentrated in the period immediately following the December 12 meeting. These prices exhibit greater absolute movement, greater volatility, and more frequent updates by market makers. Since market makers are the ones initiating these abnormal price changes (in response to customer betting activity), it is reasonable to assume they would be aware of the threat of inside traders. Theory predicts the market makers will respond to this threat by some combination of reducing order book depth and widening the bid-ask spread. In short, they will provide less market liquidity. The evidence in the preceding section is consistent with reduced order book depth. I will now test whether insider trading causes more frequent price

81 discontinuities and if market makers widen their bid-ask spreads, or, using the terminology of the gambling industry, increase the vigorish.

In Table 6, I run multiple specifications of probit models in which the dependent variable

Jump takes a value of one if that contract’s point spread exhibits any jumps, which I define as changes by one or more points in either direction at any point in time. The minimum, and by far most frequent, change in point spread is 0.5 points. One can think of this as the minimum tick size. It is therefore a sign of greater price impact and less liquid markets if a contract exhibits a discontinuous price jump of one point or more. The marginal effects at the means presented in

Table 6 indicate that Donaghy’s games are approximately 7.41 percentage points more likely to experience price jumps than non-Donaghy games. Going further, we see that this effect is once again concentrated in the period immediately following the expansion of the information network. This supports the theories of Kyle (1985) and others, which predict that prices will become more sensitive to order flow when market makers suspect increased proportions of informed trading.

Next, I test whether market makers increase the explicit cost of trading, the bid-ask spread. I am most interested in the bid-ask spreads implicit in market makers’ opening lines because those are posted before any customer orders have been placed. These bid-ask spreads are therefore immune from the potentially confounding effects of customer order imbalances. If market makers identify Donaghy as a source for potential adverse selection risk, and they do not know the direction in which he might influence the game, they can discourage insider trading by charging a greater spread, thereby making the insider trading less profitable.

Table 9 illustrates that market makers did indeed increase the vig for bets on the spreads of Donaghy’s games. The increase is statistically significant, with a p-value of 0.018, but the

82 economic significance appears to be trivial. At 0.0001, the estimated treatment effect represents an increase of less than 0.4% over the sample average. From this perspective, it may seem unlikely that market makers consciously take such a trivial step. However, it should be noted there is very little variation in the opening vig. The sample standard deviation is only 0.0005, and the range between the first and 99th percentile is just 0.0017. From this perspective, the treatment effect is far more material, representing 16.4% of the sample standard deviation and 5.2%m of the difference between the first and 99th percentiles.

Columns 2-4 show the treatment effect is concentrated in the final period of the sample.

This makes sense if it represents a response by market makers to the unusual trading activity in

Donaghy’s games, which was concentrated in the second period. Despite observing the unusual trading activity in the second period, it may have taken market makers time to realize Donaghy was the common link and likely source of these abnormalities. It is, however, curious that market makers did not appear to charge a higher vig for money line contracts even though such bets also demonstrated abnormal price behavior.

2.5.4. Market Efficiency

Finally, I examine what effect the inside traders have on the efficiency of market prices.

It is not clear whether insider trading should improve or inhibit efficient pricing. On the one hand, those trading on inside information are incorporating their previously unincorporated, value-relevant information into market prices. In that sense, prices become more informed, or efficient, through insider trading. On the other hand, market maker may respond by providing less market liquidity, thereby discouraging insider trading by reducing its profitability.

Furthermore, other market participants with value-relevant information of their own may be

83 reluctant to trade in markets swarming with insiders, thus depriving market prices of that group’s information production.

I test Donaghy’s net effect on the efficiency of prices using multiple methods. First, I test whether the Donaghy treatment can explain predictable deviations, or forecast errors, between the closing spread of each game and the realized outcome. I then test whether there existed a profitable trading strategy based solely on observing whether Donaghy was one of the game’s officials. Once again, the nature of sports betting, which provides quick and numerous realizations of outcomes across homogenous securities, provides a substantial advantage over financial markets when performing these tests.

The spread of a sporting event is an efficient one if it represents an unbiased forecast of the game’s realized point differential. Any variable that has incremental power in predicting the outcome beyond that of the closing point spread represents value-relevant information that is not fully incorporated into that closing spread. The existence of such a variable is therefore evidence of market inefficiency. In my test of forecast errors, I use the following regression model to predict the realized point differential of each game in my sample.

푃표푖푛푡퐷푖푓푓푒푟푒푛푡푖푎푙, = 훽0 + 훽1 푇푟푒푎푡i,t + 훽2퐶푙표푠푖푛푔푆푝푟푒푎푑i,t +

훽3 퐸퐿푂i,t + 훽4 퐴푡푡푒푛푑푎푛푐푒i,t + δ + ε,

The dependent variable, PointDifferential, is the realized difference in points scored by each team. ClosingSpread is the final point spread posted for the game before the start of play. I also include other variables defined in previous sections, such as Treat, ELO, etc.

84 Table 11 demonstrates a clear change in the closing spread’s predictive efficiency for

Donaghy’s games after the December 12 meeting. While there is no significant difference for

Donaghy’s games when we do not distinguish between time periods, the coefficient on PostTreat dummy in the second column indicates away teams perform about 5 points better in Donaghy’s games after December 12 than the closing spread would suggest. This is a larger effect than the

3.5-point average advantage of playing in front of one’s own home crowd. As we split the sample into three periods, we see that the effect is largely concentrated in the third period. Recall that, compared to the period immediately following the December 12 meeting, the third period is one in which the point spread was moving less dramatically, while book makers were charging a greater opening vig. This suggests, during the third period, markets were not fully pricing in

Donaghy’s effect on the game’s final score.

Having shown the inefficiency of point spreads in Donaghy’s games in the first sample period, I now examine the process of price discovery with regard to those point spreads. The basic question, adapted from Moskowitz (2018), is whether the point spreads’ movements from open to close have any predictive power for the forecast error of those spreads. I use the following regression model to test whether Donaghy has any effect on price discovery, and if so, in which periods.

퐹표푟푒푐푎푠푡퐸푟푟표푟, = 훽0 + 훽1 푇푟푒푎푡i,t * 푆푝푟푒푎푑푀표푣푒+ 훽2푇푟푒푎푡i,t +

훽3푆푝푟푒푎푑푀표푣푒i,t + 훽4 퐸퐿푂i,t + 훽5 퐴푡푡푒푛푑푎푛푐푒i,t + δ + ε,

If markets underreact to the information input by Donaghy and his associates’ bets, the variable(s) of interest, Treat*SpreadMove, will have a positive and statistically significant

85 coefficient, indicating that the move from open to close was incomplete and will be observable as a forecast error in the same direction between the closing spread and realized point differential. If, however, markets overreact to Donaghy’s information, the forecast error will retrace the overreaction, leading to a negative coefficient on the variable(s) of interest. If the point spreads’ movements are complete noise, the coefficient should equal negative one, consistent with a complete retracement of any noise-induced move when terminal “values” are revealed. Finally, if the point spread movements accurately reflect Donaghy’s information, the point spreads’ movements should be unpredictive of forecast errors, resulting in a statistically insignificant coefficient.

The results in Table 12 suggest Donaghy’s information is only partially incorporated in the closing point spreads for games in the first sample period but is fully incorporated for games in the latter two periods. Columns two, three, and four suggest that a point spread move of one standard deviation between the open and close predicts a forecast error between 3.1 and 4.6 point in the same direction. This effect is not present in the second and third sample periods, whether they are analyzed together (Column two) or separately (Columns three, five, and six). In

Columns two and three, the sums of the coefficients for TreatMove and TreatPostMove as well as both TreatMove2 and TreatMove3 are not statistically different than zero. These results are consistent with the presumption that far less money was bet on Donaghy’s information in the first sample period, prior to the expansion of his information network. As such, less (but apparently still some) information was impounded into market prices, resulting in less efficient point spreads.

Another way to test a market’s efficiency is to analyze whether a systematic trading strategy can generate excess expected profits in that market. In theory, a bet on either side of the

86 point spread should be equally likely to win. The bookmaker earns an expected profit because each side of the bet is expected to win approximately 50% of the time, but the bookmaker typically only pays out $10 of profit for every $11 bet. With this payout structure, the sports bettor needs to predict the correct side of the wager at least 52.38% of the time. I use the following probit model to estimate the probability of the away team covering the point spread.

The dependent variable, AwayWins takes the value of one if a bet on the away team, either to cover the point spread or to win the money line, is a winner and zero otherwise.

퐴푤푎푦푊푖푛푠, = 훽0 + 훽1 푇푟푒푎푡i,t + 훽2 퐸퐿푂i,t + 훽3 퐴푡푡푒푛푑푎푛푐푒i,t + δ + ε,

As in the previous analysis of the forecast error, the market seems to fail to price

Donaghy’s effect on games in the third period. Relative to the difference between treated and control games in the first period, the away team is more likely to cover the point spread in the third period if Donaghy is one of the game’s officials. In this period, the odds of the away team covering the point spread are 30 percentage points greater. Based on the same regression model, the away team is far more (less) likely to win the game outright after (before) the information network expands than the closing prices of the money line contracts imply. The magnitudes seem quite large and may be overstated due to statistical flukes. However, the important result is that the Donaghy treatment contains explanatory power beyond that which market prices alone suggest. This indicates a market inefficiency.

Finally, I examine the money line contracts to see if the efficiency of their prices is aso influenced by Donaghy. For money line contracts, the payouts posted for each team imply the market-based probabilities of each team winning. A money line contract with a payoff listed as

87 +300 implies a 25% probability of that team winning.27 In the following probit model, the null hypothesis is that the probability of the away team winning, as implied by the money line payoff, will be the significant predictor of the likelihood of the away team winning. This is akin to the earlier null hypothesis in which the point spread would be the only variable to predict the realized point differential. The existence of another variable with predictive power suggests there is additional value-relevant information that is not fully incorporated in the money line price. In other words, there exists an inefficiency.

Table 13 shows that, for Donaghy’s games, the away team is significantly more likely to win than the closing money line price would imply, but only in those periods after the December

12 meeting. Unlike the effects on the point spread, however, there appear to be no difference in

Donaghy’s effect between the second and third periods.28

2.6. Discussion

My analysis suggests there were three distinct periods in the saga of Tim Donaghy and the 2006-2007 NBA season. First, there was the period prior to the December 12 meeting between Donaghy and Batista. Based on media accounts, Donaghy was likely placing wagers, but the information network was small and did not yet include those betting large amounts of money. As such, we see minimal evidence of abnormal price behavior for Donaghy’s games.

After Donaghy teamed up with Batista, however, the inside information flowed to larger pools of money. This combination led to extraordinary movements in market prices that suggest

27 A bet of $100 pays back the original $100 plus $300 in profits. 100/400 = 0.25 = 25%. 28 In unreported results, I analyze money line betting prices in a similar manner to the tests performed in Table 12. I do not find any evidence that price movements, or returns, on money line bets between the open and close are predictive of returns on money line bets between the close and final payoff, or terminal value.

88 aggressive trading by the insiders. In the last period, starting on March 1, the intensity of informed trading seemed to wane as market prices in Donaghy’s games behaved more normally.

Taken as a whole, the evidence is consistent with the following narrative: insider trading increases dramatically as soon as the link is created between Donaghy, Batista, and Batista’s associates. Donaghy’s assignment to referee a game is associated with a nearly one-point swing in favor of the away team between December 12 and February 28. Furthermore, the payoffs associated with both spread and money line contracts move significantly from open to close, and bookmakers update their lines 25-40% more frequently. As a result, the informed trading seems to have largely impounded the inside information into the market’s spreads. During this period, there is no evidence of systematic forecasting errors by the point spread or the probability that a bet on the away team turned out to be a winning one. However, the money line contracts did not seem to fully price in Donaghy’s impact as his presence as a game’s referee increased the away team’s probability of winning significantly beyond that implied by the closing money line.

In the period spanning March 1 to the end of the playoffs, the informed traders seem to bet far less aggressively. This may have been self-imposed as the informed traders realized the abnormal price behavior they were causing and decided to scale back to avoid detection. They may also have been discouraged by bookmakers’ response during the period, which was to increase the vigorish implied in the opening prices of point spread contracts. Granted, the increase in vigorish was quite small in absolute terms and therefore not large enough to plausibly discourage the insiders’ trading, but it does demonstrate that bookmakers responded in at least one of the ways theory predicts. As a result of less aggressive insider trading, the insiders’ information does not get fully incorporated into the market price. Donaghy’s presence explains a significant difference between the closing point spread and the game’s realized outcome as well

89 as the likelihood the away team covers. It also continues to increase the probability that the away team wins beyond what is predicted by the closing money lines. Without the insiders fully exploiting their information, perhaps due in part to bookmakers’ protective actions, market prices remained less informationally efficient.

2.7. Conclusion

In this paper, I have tested several predictions regarding the effect of insider trading on market quality using NBA betting markets as my laboratory. Sports betting markets are useful for analyzing market efficiency because the terminal values of sports betting contracts are exogenous to betting activity and are revealed quickly and frequently. Furthermore, sports betting contracts are far more homogenous than publicly traded stocks, which allows for far greater comparability between treated and control groups. Finally, the unique circumstances of the 2006-2007 NBA season provide a randomly assigned inside trader across a subset of the season’s games, an ideal setting for testing insider trading theories that does not exist in financial markets.

I find that inside traders bet aggressively on their information, particularly in the period immediately after the information network expanded. Point spreads and payoffs moved significantly more for Donaghy’s games relative to non-Donaghy games in these periods. Not only were the moves larger in magnitude, but the bookmakers updated their lines far more frequently, and changes in the point spread fluctuated back and forth rather than monotonically moving toward the more efficient price. As a result, the inside information appears to have been fully incorporated into the point spread during the period of most aggressive trading. During the final period, however, I find systematic bias in the spreads suggesting that the less aggressive

90 insider trading strategy led to less efficient prices. Conversely, I find systematic bias in money line prices in both periods following the expansion of the trading network.

The behavior of both inside traders and market makers seemed to be quite strategic. After the initial period of aggressive betting, which caused abnormal movements in the spreads of

Donaghy’s games, the inside traders seem to trade on their information less aggressively.

Perhaps they were weary of being caught, given the highly visible nature of the spread movements they were causing. They may also have been discouraged by bookmakers’ reduced liquidity provisions. Consistent with theoretical predictions, bookmakers increased the vigorish they charged bettors, which is akin to the bid-ask spread in financial markets. In that final period,

I no longer found evidence of more frequent price changes by bookmakers, suggesting there may be some substitution effects between these two protective methods of liquidity reduction.

In summary, I find significant evidence of strategic action, consistent with theoretical predictions, on both the part of the inside traders and the market makers. The interplay between these agents determines the effect on market efficient, as greater freedom for insiders to trade leads to more informationally efficient market prices.

91 Figure 2.1. Distribution of Point Spread Changes This graph illustrates the distributions of point spread changes for sample games during the 2006-2007 NBA season by group, treated and control. The y-axis is the percentage of all sample games in that histogram bucket by group. The blue bars represent the control games, which are those not officiated by Tim Donaghy. The light red bars represent the treatment games, which are those officiated by Tim Donaghy. The dark red areas represent overlap between the two distributions.

92 Figure 2.2. Donaghy Games as Percentage of Sample Games by Point Spread Move

This graph illustrates the percentage of sample games in each histogram bin officiated by Tim Donaghy. The black line market 5.78%, which is the percentage of all sample games officiated by Tim Donaghy. If point spread movements were independent of Tim Donaghy’s influence, we would expect a uniform distribution of 5.78% across all bins.

93 Figure 2.3. Number of Games Officiated by Donaghy (by team)

This graph illustrates the number of games officiated by Tim Donaghy by team during the 2006- 2007 NBA season. The Seattle Supersonics/Oklahoma City Thunder franchise is not included because complete data was unavailable for this franchise.

94 Figure 2.4. Screenshot of Primary Data Source

This figure is a screenshot taken from sportsbookreview.com (SBR). SBR provides time series data on point spreads and bet payoffs for various sporting events over more than ten years.

95 Table 2.1. Summary Statistics

This table provides summary statistics for ex-ante game variables during the 2006-2007 NBA season. Means and standard deviations are calculated by group. Non-Donaghy Games are the control games, which were not officiated by Tim Donaghy. There are 1,131 such games in the sample. Donaghy Games are the treated games, which were officiated by Tim Donaghy. There are 66 such games in the sample.

Non-Donaghy Games (Control Group) Variable Mean Standard Deviation ELO Home 1508.76 94.49 ELO Away 1508.24 93.28 ELO Difference -0.52 129.20 Attendance 17,999.18 2,491.87

Donaghy Games (Treatment Group) Variable Mean Standard Deviation ELO Home 1493.09 80.53 ELO Away 1495.64 92.35 ELO Difference 2.55 112.44 Attendance 17,487.44 2,251.44

96 Table 2.2. Donaghy Games by Team

This table provides the number of home games (Home), away games (Away), and total games (Total) officiated by Tim Donaghy by team in the sample. Team names are current location of franchise, rather than location during the 2006-2007 NBA season.

Team Home Away Total Atlanta 2 4 6 Boston 1 1 2 Brooklyn 2 2 4 Charlotte 2 4 6 Chicago 1 2 3 Cleveland 2 0 2 Dallas 0 3 3 Denver 5 0 5 Detroit 1 1 2 Golden State 4 0 4 Houston 3 1 4 Indiana 4 2 6 LA Clippers 1 4 5 LA Lakers 1 1 2 Memphis 0 4 4 Miami 3 4 7 Milwaukee 2 2 4 Minnesota 1 2 3 New Orleans 2 3 5 New York 2 1 3 Orlando 5 3 8 Philadelphia 3 4 7 Phoenix 1 5 6 Portland 2 1 3

97 Table 2.2. (Continued)

Sacramento 3 0 3 San Antonio 2 2 4 Toronto 3 4 7 Utah 3 2 5 Washington 4 3 7

98 Table 2.3. Donaghy's Effect on Point Spread Movements

This table shows the results of OLS regressions estimating the closing point spreads of NBA basketball games during the 2006-2007 season. The dependent variable is CloseSpread. The Baseline column does not distinguish the sample’s subperiods. The Pre/Post column distinguishes the periods before and after the information network expansion on December 12. The Period 1, Period 2, and Period 3 columns estimate the regression solely in the periods prior to December 12, between December 12 and February 28, and from March 1 to the end of the season, respectively. Treat is an indicator equal to one for Donaghy’s games and zero otherwise. Post is an indicator equal to one for all games occurring on December 12 or later. PostTreat is an interaction term multiplying Post by Treat. Additional controls are included and summarized in Table 1. Robust standard errors are used and test statistics are reported in parentheses. ***, **, and * denote significance at one percent, give percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Baseline) (Pre/Post) (Period 1) (Period 2) (Period 3) Treat -0.5171** -0.8593** -0.6160 -0.9550** 0.2638 (-2.17) (-2.50) (-1.50) (-2.37) (0.72) OpenSpread 1.0266*** 1.0284*** 1.0020*** 0.9963*** 1.0933*** (46.02) (46.00) (25.04) (29.18) (28.68) PostTreat 0.5202 (1.11) Post 0.1274 (1.40) DOW FE Yes Yes Yes Yes Yes Obs. 1196 1196 284 512 400 R^2 0.94 0.94 0.92 0.94 0.95

99 Table 2.4. Donaghy's Effect on Contract Price Movements

This table shows the results of OLS regressions estimating the absolute move in the prices of betting contracts of NBA basketball games during the 2006-2007 season. The dependent variable is AbsPriceMove. The columns Spread 1, Spread 2, and Spread 3 estimate the regression for point spread contracts with no subperiods, two subperiods (split on December 12), and three (split at December 12 and March 1), respectively. The ML columns do the same for money line betting contracts. Treat is an indicator equal to one for Donaghy’s games and zero otherwise. Post is an indicator equal to one for all games occurring on December 12 or later. Time2 (Time3) is an indicator taking the value of one for games played between December 12 and February 28 (after February 28). PostTreat, Treat2, and Treat3 are interactions terms multiplying Treat by Post, Time2, and Time3. Additional controls are included and summarized in Table 1. Robust standard errors are used and test statistics are reported in parentheses. ***, **, and * denote significance at one percent, give percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Spread 1) (Spread 2) (Spread 3) (ML1) (ML 2) (ML 3) Treat 0.0027* -0.0015 -0.0030 0.0093*** -0.0062* -0.0110*** (1.82) (-0.86) (-1.32) (2.74) (-1.71) (-3.21) PostTreat 0.0066** 0.0240*** (2.45) (4.21) Post -0.0015* -0.0009 (-1.92) (-0.65) Treat2 0.0086*** 0.0252*** (2.64) (4.10) Treat3 0.0058* 0.0280*** (1.66) (3.91) Time2 -0.0019** -0.0025 (-2.25) (-1.58) Time3 -0.0012 0.0004 (-1.31) (0.21) DOW FE Yes Yes Yes Yes Yes Yes Obs. 1196 1196 1196 1190 1190 1190 R^2 0.00 0.01 0.00 0.03 0.04 0.04

100 Table 2.5. Donaghy's Effect on Point Spread Range

This table shows the results of OLS regressions estimating the range of point spreads of NBA basketball games during the 2006-2007 season. The dependent variable is Range. The first, second, and third columns estimate the regression for point spread contracts using the full and split based on no subperiods, two subperiods (split on December 12), and three (split at December 12 and March 1), respectively. Columns four, five, and six estimate the regression using only observations from the first, second, and third subperiods, respectively. Treat is an indicator equal to one for Donaghy’s games and zero otherwise. Post is an indicator equal to one for all games occurring on December 12 or later. Time2 (Time3) is an indicator taking the value of one for games played between December 12 and February 28 (after February 28). PostTreat, Treat2, and Treat3 are interactions terms multiplying Treat by Post, Time2, and Time3. Additional controls are included and summarized in Table 1. Robust standard errors are used and test statistics are reported in parentheses. ***, **, and * denote significance at one percent, give percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Full (Pre/Post) (3 Periods) (Period 1) (Period 2) (Period 3) Sample) Treat 0.0714 0.0594 -0.0659 -0.0635 0.2559** -0.0534 (1.15) (0.62) (-0.68) (-0.70) (2.49) (-0.50) PostTreat 0.0186 (0.15) Treat2 0.3060** (2.18) Treat3 0.0130 (0.09) Time2 -0.0208 (-0.61) Time3 -0.0053 (-0.14) DOW FE Yes Yes Yes Yes Yes Yes Obs. 1196 1196 1196 284 512 400 R^2 0.78 0.78 0.78 0.79 0.78 0.79

101 Table 2.6. Donaghy's Effect on Point Spread Jumps

This table shows the results of Probit regressions estimating the likelihood of point spread jumps of NBA basketball games during the 2006-2007 season. The dependent variable is Jump. The first, second, and third columns estimate the regression for point spread contracts using the full and split based on no subperiods, two subperiods (split on December 12), and three (split at December 12 and March 1), respectively. Columns four, five, and six estimate the regression using only observations from the first, second, and third subperiods, respectively. Treat is an indicator equal to one for Donaghy’s games and zero otherwise. Post is an indicator equal to one for all games occurring on December 12 or later. Time2 (Time3) is an indicator taking the value of one for games played between December 12 and February 28 (after February 28). PostTreat, Treat2, and Treat3 are interactions terms multiplying Treat by Post, Time2, and Time3. Additional controls are included and summarized in Table 1. Robust standard errors are used and test statistics are reported in parentheses. ***, **, and * denote significance at one percent, give percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Full (Pre/Post) (3 Periods) (Period 1) (Period 2) (Period 3) Sample) Treat 0.0741** 0.0507 0.0124 0.0115 0.1205** 0.0364 (2.09) (0.87) (0.17) (0.13) (2.49) (0.71) PostTreat 0.0393 (0.54) Post -0.0621*** (-3.04) Treat2 0.1125 (1.28) Treat3 0.0389 (0.40) Time2 -0.0596*** (-2.65) Time3 -0.0739*** (-2.99) DOW FE Yes Yes Yes Yes Yes Yes Obs. 1197 1197 1197 284 513 400 R^2 0.028 0.039 0.042 0.045 0.047 0.083

102 Table 2.7. Donaghy's Effect on Contract Price Volatility

This table shows the results of OLS regressions estimating the standard deviations of point spreads (Columns 1 & 2) as well as contract prices for both point spread (Columns 3 & 4) and money line (Columns 5 & 6) contracts of NBA basketball games during the 2006-2007 season. The dependent variable is StandardDev. The first, third, and fifth columns estimate the regression without time period distinctions for the full sample period. Columns two, four, and six estimate the regression with indicator variables distinguishing three distinct subperiods. Treat is an indicator equal to one for Donaghy’s games and zero otherwise. Post is an indicator equal to one for all games occurring on December 12 or later. Time2 (Time3) is an indicator taking the value of one for games played between December 12 and February 28 (after February 28). PostTreat, Treat2, and Treat3 are interactions terms multiplying Treat by Post, Time2, and Time3. Additional controls are included and summarized in Table 1. Robust standard errors are used and test statistics are reported in parentheses. ***, **, and * denote significance at one percent, give percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Spread) (Spread) (Spread (Spread (ML Price) (ML Price) Price) Price) Treat 0.1418*** -0.0033 0.0017*** -0.0016* 0.0035*** -0.0040*** (3.07) (-0.04) (3.12) (-1.77) (2.71) (-4.40) Treat2 0.2391** 0.0052*** 0.0102*** (2.10) (4.20) (4.48) Treat3 0.1226 0.0031*** 0.0091*** (1.07) (2.86) (3.66) Time2 -0.0074 -0.0005* 0.0001 (-0.33) (-1.74) (0.15) Time3 -0.0136 -0.0024*** 0.0012 (-0.56) (-8.42) (1.54) DOW FE Yes Yes Yes Yes Yes Yes Obs. 1197 1197 1197 1197 1190 1190 R^2 0.03 0.03 0.01 0.09 0.01 0.02

103 Table 2.8. Donaghy's Effect on Contract Price Changes

This table shows the results of OLS regressions estimating the number of price observations of NBA basketball games during the 2006-2007 season. The dependent variable is Observations. The columns Spread 1, Spread 2, and Spread 3 estimate the regression for point spread contracts with no subperiods, two subperiods (split on December 12), and three (split at December 12 and March 1), respectively. The ML columns do the same for money line betting contracts. Treat is an indicator equal to one for Donaghy’s games and zero otherwise. Post is an indicator equal to one for all games occurring on December 12 or later. Time2 (Time3) is an indicator taking the value of one for games played between December 12 and February 28 (after February 28). PostTreat, Treat2, and Treat3 are interactions terms multiplying Treat by Post, Time2, and Time3. Additional controls are included and summarized in Table 1. Robust standard errors are used and test statistics are reported in parentheses. ***, **, and * denote significance at one percent, give percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Spread 1) (Spread 2) (Spread 3) (ML 1) (ML 2) (ML 3) Treat 10.5478*** 1.8372 -1.0772 9.3368*** 0.3535 -1.1925 (3.99) (0.59) (-0.29) (3.82) (0.21) (-0.57) PostTreat 13.4815*** 13.7038*** (2.87) (3.70) Post -2.8429*** 2.9770*** (-2.60) (3.71) Treat2 18.7220*** 16.9073*** (3.64) (4.36) Treat3 10.4171 8.9522 (1.57) (1.62) Time2 -3.8530*** 0.6488 (-3.20) (0.73) Time3 -2.0013 5.8848*** (-1.41) (5.30) DOW FE Yes Yes Yes Yes Yes Yes Obs. 1197 1197 1197 1190 1190 1190 R^2 0.07 0.08 0.08 0.10 0.12 0.13

104 Table 2.9. Donaghy's Effect on Point Spread Vigorish (Bid-Ask)

This table shows the results of OLS regressions estimating the vigorish charged in point spread betting contracts of NBA basketball games during the 2006-2007 season. The dependent variable is Vig. The first column estimates the regression for point spread contracts with no subperiods. Columns two, three, and four estimate the regression for Period 1 (prior to December 12), Period 2 (between December 12 and February 28), and Period 3 (after February 28). Treat is an indicator equal to one for Donaghy’s games and zero otherwise. Post is an indicator equal to one for all games occurring on December 12 or later. PostTreat is an interaction term multiplying Treat by Post. Additional controls are included and summarized in Table 1. Robust standard errors are used and test statistics are reported in parentheses. ***, **, and * denote significance at one percent, give percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Full Sample) (Period 1) (Period 2) (Period 3) Treatment 0.0001** 0.0001 0.0001 0.0001** (2.37) (0.92) (0.95) (2.33) ELO Diff 0.0000 0.0000 0.0000 0.0000 (1.14) (0.30) (0.93) (0.50) Attendance 0.0000 -0.0000 0.0000 0.0000 (0.08) (-1.28) (0.81) (0.25) DOW FE Yes Yes Yes Yes Obs. 1,196 284 512 400 R^2 0.01 0.02 -0.00 0.00

105 Table 2.10. Donaghy's Effect on Money Line Vigorish (Bid-Ask)

This table shows the results of OLS regressions estimating the vigorish charged in money line betting contracts of NBA basketball games during the 2006-2007 season. The dependent variable is Vig. The first column estimates the regression for point spread contracts with no subperiods. Columns two, three, and four estimate the regression for Period 1 (prior to December 12), Period 2 (between December 12 and February 28), and Period 3 (after February 28). Treat is an indicator equal to one for Donaghy’s games and zero otherwise. Post is an indicator equal to one for all games occurring on December 12 or later. PostTreat is an interaction term multiplying Treat by Post. Additional controls are included and summarized in Table 1. Robust standard errors are used and test statistics are reported in parentheses. ***, **, and * denote significance at one percent, give percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Full Sample) (Period 1) (Period 2) (Period 3) Treatment -0.0000 -0.0003 0.0000 -0.0000 (-0.03) (-0.33) (0.04) (-0.03) ELO Diff 0.0000*** 0.0000*** 0.0000*** 0.0000*** (6.92) (5.18) (4.78) (2.76) Attendance -0.0000 0.0000 -0.0000** -0.0000 (-1.59) (0.18) (-1.98) (-0.95) DOW FE Yes Yes Yes Yes Obs. 1,190 284 511 395 R^2 0.07 0.08 0.08 0.03

106 Table 2.11. Donaghy's Effect on Point Spread Forecast Error

This table shows the results of OLS regressions estimating the realized point differential of NBA basketball games during the 2006-2007 season. The dependent variable is PointDiff. The first, second, and third columns estimate the regression for point spread contracts with no subperiods, two subperiods (split on December 12), and three (split at December 12 and March 1), respectively. Columns four, five, and six estimate the regression using only observations from the first, second, and third subperiods, respectively. Treat is an indicator equal to one for Donaghy’s games and zero otherwise. Post is an indicator equal to one for all games occurring on December 12 or later. Time2 (Time3) is an indicator taking the value of one for games played between December 12 and February 28 (after February 28). PostTreat, Treat2, and Treat3 are interactions terms multiplying Treat by Post, Time2, and Time3. CloseSpread is the closing point spread for the game. Additional controls are included and summarized in Table 1. Robust standard errors are used and test statistics are reported in parentheses. ***, **, and * denote significance at one percent, give percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Full (Pre/Post) (3 Periods) (Period 1) (Period 2) (Period 3) Sample) Treat -0.0002 3.2208 5.0777* 5.2136* -0.4609 -3.0644 (-0.00) (1.33) (1.74) (1.73) (-0.23) (-1.40) PostTreat -4.9421* (-1.72) Post -0.2596 (-0.33) Treat2 -5.3885 (-1.52) Treat3 -8.1156** (-2.22) Time2 -0.0496 (-0.06) Time3 -0.2411 (-0.26) DOW FE Yes Yes Yes Yes Yes Yes Obs. 1197 1197 1197 284 513 400 R^2 0.17 0.17 0.17 0.10 0.18 0.19

107 Table 2.12 Donaghy's Effects on Price Discovery

This table shows the results of OLS regressions estimating the realized forecast errors, or difference between closing point spreads and realize point differentials, of NBA basketball games during the 2006-2007 season. The dependent variable is ForecastError. The first, second, and third columns estimate the regression for point spread contracts with no subperiods, two subperiods (split on December 12), and three (split at December 12 and March 1), respectively. Columns four, five, and six estimate the regression using only observations from the first, second, and third subperiods, respectively. Treat is an indicator equal to one for Donaghy’s games and zero otherwise. Post is an indicator equal to one for all games occurring on December 12 or later. Time2 (Time3) is an indicator taking the value of one for games played between December 12 and February 28 (after February 28). PostTreat, Treat2, and Treat3 are interactions terms multiplying Treat by Post, Time2, and Time3. SpreadMove is the difference between the opening and closing point spread for the game. This variable is also interacted with Treat and the various time period indicator variables. Additional controls are included and summarized in Table 1. Robust standard errors are used and test statistics are reported in parentheses. ***, **, and * denote significance at one percent, give percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Full (Pre/Post) (3 Periods) (Period 1) (Period 2) (Period 3) Sample) TreatSpreadMove 0.4572 3.1138** 4.077*** 4.5792*** -0.0920 0.2335 (0.68) (2.57) (3.48) (3.42) (-0.11) (0.24) SpreadMove 0.0879 0.0944 0.0857 -0.5407 0.1134 0.3851 (0.32) (0.35) (0.31) (-1.02) (0.27) (0.80) TreatPostSpreadMove -2.7771** (-2.15) TreatSpreadMove2 -4.0137*** (-2.99) TreatSPreadMove3 -3.4994*** (-2.51) DOW Fixed Effects Yes Yes Yes Yes Yes Yes Obs. 1196 1196 1196 284 512 400 R^2 0.00 0.01 0.01 0.01 -0.01 0.00

108 Table 2.13. Donaghy's Effect on Away Bet Win Percentage

This table shows the results of Probit regressions estimating the likelihood of a bet on the away team winning of NBA basketball games during the 2006-2007 season. The dependent variable is RoadWin. The columns Spread 1, Spread 2, and Spread 3 estimate the regression for point spread contracts with no subperiods, two subperiods (split on December 12), and three (split at December 12 and March 1), respectively. The ML columns do the same for money line betting contracts. Treat is an indicator equal to one for Donaghy’s games and zero otherwise. Post is an indicator equal to one for all games occurring on December 12 or later. Time2 (Time3) is an indicator taking the value of one for games played between December 12 and February 28 (after February 28). PostTreat, Treat2, and Treat3 are interactions terms multiplying Treat by Post, Time2, and Time3. Additional controls are included and summarized in Table 1. Robust standard errors are used and test statistics are reported in parentheses. ***, **, and * denote significance at one percent, give percent, and ten percent, respectively, in two-tailed tests.

Coefficient (Spread 1) (Spread 2) (Spread 3) (ML 1) (ML 2) (ML 3) Treat 0.0083 -0.1203 -0.1901 0.0346 -0.1022 -0.2318* (0.13) (-1.12) (-1.39) (0.56) (-0.98) (-1.78) PostTreat 0.1983 0.2083 (1.50) (1.63) Post -0.0123 -0.0057 (-0.35) (-0.16) Treat2 0.2230 0.3439** (1.34) (2.15) Treat3 0.3013* 0.3321** (1.71) (1.99) Time2 -0.0335 -0.0148 (0.87) (-0.38) Time3 0.0057 -0.0124 (0.14) (-0.30) DOW FE Yes Yes Yes Yes Yes Yes Obs. 1197 1197 1197 1190 1190 1190 R^2 0.005 0.007 0.008 0.075 0.076 0.078

109 CHAPTER 3: DUAL OWNERSHIP AS A MARKET SOLUTION TO RISK SHIFTING: EVIDENCE FROM LOAN COVENANT VIOLATIONS29

3.1. Introduction

Agency costs are central to corporate finance research, and the threat of wealth transfers

(via asset substitution or excessive shareholder payouts) is the primary agency cost of debt.

Theoretical papers provide several debt-contracting solutions to protect creditor claims: covenants (Smith and Warner (1979)), convertible debt (Green (1984)), and debt maturity

(Barnea, Haugen, and Senbet (1980)). However, little work has been done to examine a market- based solution, initiated by investors, to mitigate agency costs between shareholder creditors: dual ownership.

Dual ownership is the simultaneous ownership of debt and equity claims in the same firm by a single investor. Jensen and Meckling (1976) states such strip financing can eliminate all conflicts between shareholders and creditors if every claimant holds the same claim on the firm.

Intuitively, there are no conflicts between shareholders and creditors if they are one in the same.

We examine whether investors adjust their degree of dual ownership in response to changes in the wealth transfer risk they face.

In addition to shareholder-creditor conflicts, wealth transfer risk can be a source of conflict between different types of creditors. Consider the 2017 debt swap by financially distressed retailer J. Crew. Facing a potential bankruptcy, J. Crew transferred its best collateral, the intellectual property behind the J. Crew brand, to an unrestricted subsidiary upon which its term-loan holders held no claim. The subsidiary then issued bonds secured by the brand-name

29 This chapter was coauthored with two of my fellow Finance PhD candidates at the University of Iowa, Steven Irlbeck and Eric McKee. This was a truly collaborative effort with each coauthor contributing to each aspect of the project (e.g. data collection, statistical analyses, writing the manuscript, etc.).

110 assets to replace unsecured bonds that had been junior to the term-loan. As a result, the new bondholders moved ahead of incumbent lenders in line for the claims on the J. Crew brand, transferring wealth from lenders to bondholders.30 Situations such as these illustrate the importance of separately analyzing different types of creditors.

We exploit loan covenant violations as a source of exogenous variation in bondholders’ wealth transfer risk. Loan covenant violations are commonly used in both the finance and accounting literates as a shock to violating firms’ financial distress (e.g., Chava and Roberts

(2008); Nini, Smith, and Sufi (2012); Falato and Liang (2016)), and it is during times of financial distress that wealth transfer risk is most acute. Lenders are well-positioned to protect themselves against this wealth transfer risk because loan contracts grant them additional control rights once their loan covenants are violated. Bondholders, on the other hand, do not receive this control and therefore remain exposed to wealth transfer risk from shareholders and as well as the newly empowered lenders. If anyone is incentivized to protect their interests following loan covenant violations, it is public bondholders.

We test this hypothesis by assembling a unique and comprehensive dataset on dual ownership categorized by creditor type. Early studies on dual ownership, such as Jiang, Li, and

Shao (2010), largely focus on lenders that also hold equity in the borrowing firm. More recent studies, however, such as Bodnaruk and Rossi (2016), examine bondholders who also own equity in the same firm. Our paper, along with concurrent work by Chu, Nguyen, Wang, Wang, and Wang (2018), is the first to include both dual holding lenders31 and dual holding bondholders as well as those investors owning all three categories of claims. This unique dataset allows us to

30 WSJ article: “The Case of the Disappearing Collateral,” by Sam Goldfarb and Soma Biswas, November 15, 2018. 31 Banks are commonly dual holding lenders (i.e. lenders that also hold equity in the borrowing firm). They lend to corporate borrowers and often own equity through their mutual fund families. Our sample also includes insurers and asset managers.

111 examine differences in the strategic responses of creditors that do receive control rights (lenders) from the creditors that do not (bondholders) following loan covenant violations.

Our tests demonstrate that loan covenant violations cause investors to increase dual ownership during the year following violation. The percentage of equity held by dual owners increases by roughly 10% of the sample average for violating firms relative to non-violating firms. We also find that these changes are driven by bond-equity dual holders, not loan-equity dual holders. This supports the idea that corporate control, and the associated ability to protect the value of claims, is the motivating force behind changes in dual ownership. We further support the wealth transfer risk channel by showing results are driven by financially distressed firms and firms in which the marginal benefit of dual ownership is greater.

We contribute to a large literature examining partial solutions to shareholder-creditor conflicts. Several papers examine loan covenants both theoretically (e.g. Smith and Warner

(1979); Garleanu and Zwiebel (2009)) and empirically (e.g., Chava and Roberts (2008); Nini,

Sufi, and Smith (2012), etc.). Other mechanisms, such as convertible debt (Green (1984)) and debt maturity (Barnea, Haugen, and Senbet (1980)), have also been theoretically shown to mitigate risk-shifting incentives. Gilje (2016) finds a lack of risk-shifting behavior in one of the literature’s few empirical tests and attributes this to the effectiveness of debt contracting. Our study examines dual ownership as an alternative mechanism, notable in that it is driven solely by investor decisions. This self-regulating mechanism complements the shortcomings of incomplete contracting, responding in real time to both hard and soft information as it becomes available to the market.

We also contribute a new perspective to the growing literature on dual ownership. Prior studies have focused on firm outcomes associated with dual ownership. Bodnaruk, Massa, and

112 Simonov (2009) show that dual holding investment banks reduce wealth transfer associated with share repurchases in firms for which they previously underwrote. Jiang, Li, and Shao (2010) find lower syndicated loan spreads for borrowers that are dually owned and interpret this as a reduction in shareholder-creditor conflicts by means of dual ownership. Chu (2018) examines mergers that increase creditors’ holdings of the borrowers’ equity. The increase in dual ownership leads to a reduction in shareholder payouts. Bodnaruk and Rossi (2016, 2017) demonstrate the effects of bondholding dual owners on merger and acquisition premia and the costs of initial public bond offerings.

We contribute to this strand of literature by examining dual ownership from the investors’ perspective. We show capital market participants respond to changes in wealth transfer risk by adjusting their degree of dual ownership. Further, our findings demonstrate interesting differences in the dual ownership incentives between bondholders and lenders. In addition, our study is one of the first studies to consider differences among the different types of dual holders: bondholders who simultaneously hold equity (bond-equity dual holders), lenders who simultaneously hold equity (loan-equity dual holders), and bondholders who are also lenders and simultaneously hold equity in the borrowing firm (bond-loan-equity dual holders).32

Finally, this study contributes to the literature examining the consequences of loan covenant violations. Prior studies find that covenant violations cause reductions in capital expenditures, debt issuance, leverage, acquisitions, and shareholder payouts. In addition, following debt covenant violations, the likelihood of CEO turnover increases and the violating firm experiences improvements in operating and stock performance (Nini, Sufi, and Smith

(2012); Chava and Roberts (2008); Roberts and Sufi (2009)). Chava, Wang, and Zou (2016) take

32 Chu et al. (2018) have a working paper studying each type of dual holder and how they impact the resolution of firms’ financial distress.

113 the first step in incorporating dual ownership into this strand of literature by demonstrating its ability to facilitate firm investment in the aftermath of loan covenant violations.

This study extends the loan covenant literature by focusing on reactions by capital market participants, as opposed to the company in violation. In doing so, we complement the above- mentioned papers by showing that, in addition to causing significant changes in corporate policies and performance, loan covenant violations provoke a rebalancing in the ownership structure of the firm. We also demonstrate the different responses of bondholders and lenders to loan covenant violations, consistent with substitution effects between dual ownership and state- contingent control rights. Finally, we introduce one reason why creditors end up holding equity and present evidence consistent with the conjecture that dual ownership is a strategic choice of investors and a response to heightened wealth transfer risks.

3.2. Literature

Wealth transfer risk is the primary agency cost of debt (Jensen and Meckling (1976)), typically taking the form of risk-shifting or excess shareholder payouts. Creditors are aware of these costs and force shareholders to internalize them through higher interest rates, shorter debt maturities, and restrictive loan covenants. Indeed, Gilje (2016) finds a notable lack of risk- shifting and attributes this to the effectiveness of debt contracting in reducing risk-shifting incentives. Rather than eliminating the agency costs of debt, contracting mechanisms such as loan covenants merely transfer the costs between holders of different claims on the firm. Dual ownership, on the other hand, eliminates these costs altogether, thereby creating an opportunity for all claim holders to gain.

The empirical literature on dual ownership has grown dramatically in recent years, but it is a concept that was considered in the seminal papers on agency cost. Jensen and Meckling

114 (1976) argues that agency costs would be largely eliminated if managers were compensated with a combination of debt and equity claims, thus making them dual owners. Similarly, Jensen

(1987) argues that shareholder-creditor conflicts would be resolved if every security holder held the same claim on the firm. Recent empirical findings largely support this early theoretical work by demonstrating dual ownership’s effects on firms’ investment and payout policies and, as a result, its borrowing costs.

Risk-shifting theory predicts that shareholders will make risky investments in high- leverage states of the world because they will reap most of the benefits from positive outcomes while creditors will bare most of the costs from negative outcomes. Despite these predictions, the empirical literature has shown little evidence of risk-shifting taking place. Chava, Wang, and

Zou (2018) examines firms’ investment policies and finds that dually owned firms do not engage in risk-shifting despite being less constrained by investment-related loan covenants. Such firms are also less likely to reduce investment or debt issuance following loan covenant violations.

These findings imply that state-contingent control rights are less necessary in the presence of dual ownership. They are alternative methods of asset substitution risk.

Black and Scholes (1973) identify excessive dividends as another way in which shareholders can enrich themselves at creditors’ expense. Intuitively, dividends take assets on which creditor held a claim and give them directly to shareholders, but what if shareholder and creditors are one in the same? Chu (2018) finds that firms reduce shareholder payouts following mergers between financial institutions that increase dual ownership. There is less to gain from excessive dividends when the value is simply transferred from one pocket to the other.

Creditors force shareholders to internalize the agency costs of debt by requiring debt covenants and charging higher rates of interest. These mechanisms have themselves been shown

115 to be substitutes, but recent studies show dual ownership can reduce the need for both by directly reducing the agency costs themselves. Wang, Xie, and Xin (2010) find lower loan spreads, fewer covenant restrictions, and less collateral requirements for bank loans in which the borrowing firms’ mangers simultaneously own equity and hold pension and deferred compensation (i.e. debt) claims on the firm. Similarly, Jiang et al. (2010) find that the presence of dual holding participants in syndicated loans reduces those loans’ spreads by 18-32 basis points relative to those without dual owners. The authors further demonstrate longer investment horizons for dual owners and less risk-shifting by borrowing firms following the dual ownership loans. Finally,

Bodnaruk and Rossi (2017) demonstrate similar cost of capital benefits in a bond IPO setting.

They find lower spreads and fewer payout-restricting covenants for firms whose institutional owners tend to dually own firms in their portfolios.

Theoretical predictions and empirical evidence that dual ownership reduces shareholder- creditor conflicts lead us to our first hypothesis.

Hypothesis 1: Dual ownership will increase following an increase in the risk of wealth transfer.

The potential for substitution effects between loan covenants and dual ownership suggest that dual ownership changes should be driven by creditors not protected by increase state- contingent control rights. Given that wealth transfer risk, and asset substitution risk more specifically, is most acute when firms are in financial distress, we expect dual ownership to respond most strongly to wealth transfer risk in financially distressed firms.

116 Hypothesis 2: Dual ownership increases following increases in wealth transfer risk will be strongest for financially distressed firms.

3.3. Identification Strategy

We hypothesize that greater levels of wealth transfer risk cause investors to resolve this agency cost via dual ownership. However, basic OLS regressions would be unable to distinguish rule out an alternative story that firms with high dual ownership are given more leniency in engaging in wealth transfer risk (i.e. reverse causality). Further, it could be the case that a third variable causes both greater levels of dual ownership and greater measures of wealth transfer risk

(i.e. correlated omitted variable). Causal identification requires exogenous variation in wealth transfer risk, unrelated to dual ownership, that enables us to test investors response. For such exogenous variation, we exploit loan covenant violations.

Loan covenant violations provide an ideal setting to examine the effects of discrete jumps in shareholder-bondholder conflicts for several reasons. First, shareholder-creditor conflicts are most acute during times of financial distress (Smith and Warner (1979)), and Falato and Liang

(2016) describe the violation-induced transfer of control rights to creditors as a “traditional financial distress mechanism.” This mechanism tightens the borrower’s financial constraints by affording creditors the right to accelerate loan repayments or terminate any unused portions of existing lines of credit (Gorton and Kahn (2000)). For this reason, loan covenant violations have been used to analyze the ways in which financial constrains relate to employment (Falato and

Liang (2016)) and investment (Chava and Roberts (2008)). In the accounting literature, Tan

(2013) uses a similar approach to document discontinuous increases in conservatism, which is an alternative mechanism to mitigate shareholder-creditor conflicts, following loan covenant violations. We therefore adopt the loan covenant violation literature’s regression discontinuity

117 framework to analyze the relationship between wealth transfer risk, via increased financial distress, and dual ownership.

Our empirical approach offers three appealing features by focusing solely on financial covenant violations related to net worth and current ratio. First, this methodology is consistent with prior studies, such as Dichev and Skinner (2002), Chava and Roberts (2008), and Falato and

Liang (2016), that analyze changes in corporate behavior in response to loan covenant violations.

We adapt this procedure and instead analyze changes in the behavior of capital providers.

Second, these financial covenants are common features of commercial loans and therefore allow us to analyze a substantial proportion of the universe of commercial loans. Finally, violations of these thresholds are unambiguous and easily calculated from standardized accounting variables in the Compustat database.

The identifying assumption in our setting is that covenant violations are as good as randomly assigned for firms within a sufficiently small distance to violation. It is possible, however, that firms strategically avoid violating loan covenants, thereby making covenant violations endogenous to the firm. We alleviate this concern by providing a density plot of sample firms’ distance to default in Figure 1. If firms strategically avoid covenant violations, we would expect to see a clustering of observations just above zero (i.e. narrowly satisfying loan covenants). However, there is a smooth distribution of distances to violation around the covenant thresholds. We therefore feel reasonably assured that loan covenant violations are as good as randomly assigned within our bandwidth.

Other papers in this literature identify covenant violations from firms’ self-reporting in either their 10-Q or 10-K SEC filings. This violation data was graciously made available by

Amir Sufi on his website, and we use this database to ensure our baseline results are robust to

118 both procedures. Our primary identification strategy, however, is a regression discontinuity approach that requires a measure of the distance to violation that can only be calculated using financial covenants with known thresholds and measurable accounting variables.

3.4. Data

We use four main data sources to conduct our empirical analysis (1) Compustat for firm- level accounting data, (2) Thomson Reuter’s 13F data set for quarterly equity ownership data for institutional investors, (3) Lipper eMaxx database for quarterly bond ownership data for institutional investors, and (4) Reuters-Loan Pricing Corporation’s DealScan database for commercial loans and commercial lenders.

Our sample period spans the calendar years 1998 through 2015. Prior to 1998, the availability of bond ownership data is quite limited. We exclude financial firms (SIC codes

6000-6999) and utilities (SIC codes 4900-4999), firms with less than $10 million in total assets and firm-quarter observations in which data is missing for total assets, total sales, common shares outstanding, or the calendar quarter of the filing. Following Nini et al (2012), we exclude firm-quarter observations associated with bankruptcy, M&A, spin-offs, and divestitures as identified in Capital IQ’s Key Development database. Our regression discontinuity sample, which requires firms to be within specified distance from covenant violation, consists of 5,051 firm-quarter observations.

3.4.1. Calculating Dual Ownership

We define dual ownership as the percentage of share outstanding held by investors who either own the same company’s bonds33 or have made a loan to the same firm that is currently

33 Following Bodnaruk and Rossi (2016), dual ownership is conditional on the value of the investor’s bondholdings representing at least 5% and no more than 95% of its total exposure to the firm.

119 outstanding (following to Jiang et al. (2010)). Following Bodnaruk and Rossi (2016), we calculate dual ownership at the parent (i.e. financial conglomerates, often referred to as “brand”) level for financial institutions under the assumption that any strategic changes in response to loan covenant violations would be driven by the incentives of the parent as opposed to the individual funds manager.34 This necessitates an extensive matching procedure to aggregate holding

(lending) data from the fund (subsidiary) to the parent level. For example, we might be interest in

BlackRock’s holdings at the parent level, but 13F, DealScan, and eMaxx data are provided at the fund level without a clear link to the parent company. Each fund in our database must be matched to a parent in order to perform the aggregation.

Furthermore, each firm that is potentially dually owned may have multiple subsidiaries that each issue their own debt securities and have distinct issuer CUSIP identifiers. Our analysis assumes, however, that based on the internal capital markets, financial distress and shareholder- creditor conflicts manifest at the parent-firm level. Therefore, bond issuance data at the subsidiary level must be aggregated to the parent-firm level in order to analyze changes in investor exposure and incentive alignment in their truest sense. We employ a matching algorithm to assign funds to institutional parents and subsidiaries to parent firms and manually check the matches for accuracy.35 These matches must also account for mergers that change the ultimate ownership of funds or firms over time.

Table I provides an illustrative example of how we aggregate debt and equity claims to the parent level to form our variables of interest. We report the dual ownership by type of dual holders and at the parent level for Gibraltar Industries in the first quarter of 2010. For each of the

34 Several studies document coordination within mutual fund families. Bodnaruk and Rossi (2016) demonstrates coordination of shareholder votes at the conglomerate level. 35 Refer to Bondaruk et al. (2009) or Bodnaruk and Rossi (2016) for a more detailed discussion of matching procedures associated with this data.

120 three types of dual holders (bond-equity, loan-equity, and bond-loan-equity), we report the parents/brand that simultaneously hold equity and either bonds or loans outstanding. In addition, we report the number of funds found in eMaxx and DealScan that report holdings bonds and/or loans outstanding int eh given period. Table I shows that as of 2010 Q1, there are 6 unique bond- equity dual holders that hold a total of 3.99% of Gibraltar Industries’ shares outstanding; there are 4 unique loan-equity dual holders that hold a total of 2.81% equity and J.P. Morgan is the only financial conglomerate that simultaneously holds bonds, lends, and holds equity in Gibraltar

Industries in the period. Therefore, the third type of dual holders (bond-loan-equity) only hold

2.50% of Gibraltar Industries’ shares outstanding in the given period. The total number of dual holders of all three types and the total dual ownership by type is reported in Table II. There are

11 unique dual holders who hold 19.30% of Gibralatar Industries in 2010 Q1. We calculate similar variables for all firm-quarters in our sample period.

3.4.2. Loan Covenant Violations

Our initial sample consists of all firm-quarter observations in the quarterly merged

CRSP-Compustat database. We exclude financial firms (SIC codes 6000-6999) and utilities (SIC codes 4000-4999) and drop observations where we are missing any of the following accounting variables required for our analysis: current assets, current liabilities, total assets, total liabilities, net income, price, shares outstanding, redeemable preferred stock, deferred taxes, or property, plant, and equipment. Data from CRSP-Compustat are then merged with loan information from

Loan Pricing Corporation’s (LPC) DealScan database via the DealScan-Compustat Link Table.

Because loan covenant information is limited to prior to 1994, we focus on the sample of loans with start dates in 1995 or later.

121 Following Chava and Roberts (2008) and Falato and Liang (2016), we require each sample loan to contain a covenant restricting either the current ratio or net worth. As pointed out by Chava and Roberts (2008) and Dichev and Skinner (2002), these loans appear frequently in the DealScan database and are standardized and unambiguous. Since covenants generally apply to all loans (i.e. facilities) within a package, we define the time period over which the firm is bound by the covenant as beginning with the earliest loan start date in the package and ending with the latest end date.

For each firm-quarter, we determine if the firm is in compliance or violation of its covenant(s). A firm is in violation of a covenant if the value of its accounting variable (current ratio or net worth) falls below the corresponding threshold. We follow Chava and Roberts (2008) and Falato and Liang (2016) to overcome known covenant measurement issues. Table III illustrates the distribution of loan covenant violations over our sample period. Covenant violations are evenly spread across year. This empirical fact and our inclusion of time fixed effects provide assurance that time effects are not driving our results.

First, when firms enter multiple deals that overlap (i.e., one deal matures after another deal begun), we must determine the relevant covenant. In the case of refinancing deals, we define the relevant covenant to be the one specified by the refinancing regardless of start date or tightness. In the case of non-refinancing deals, we define the relevant covenant to be the

“tightest,” meaning the covenant with the smallest percentage distance from its respective threshold regardless of violation status. Second, in the case of dynamic current ratio covenants that change over the life of the loan, we linearly interpolate the covenant thresholds over the life of the loan. We also drop observations in which dynamic net worth covenants are missing relevant criteria (if there are additional requirements included or a missing percent of net income

122 that is added to the to the initial amount of the threshold, or base amount). Finally, we exclude loans already in violation of a financial covenant in the period of origination.

This process leaves us with 20,374 firm-quarter observations for 1,434 unique firms. We then merge in data on dual ownership and only keep observations in which we have relevant accounting data, current or net worth covenants, and equity ownership data. Observations with missing dual ownership values are set to zero as they represent firms without dual ownership.

We winsorize continuous variables (controls variables) at the top and bottom 1% of our sample to mitigate the impact of outliers.

Descriptive statistics for our variables of interest and control variables are reported in

Table IV and Table V. We report the total number of dual holders (TotalNumberDO), the number of loan-equity dual holders (LoanNumberDO), the number of bond-equity dual holders

(BondNumberDO), as well as the total amount of dual ownership (TotalOwnershipDO), the equity ownership by loan-equity dual owners (LoanOwnershipDO), and the equity ownership by bond-equity dual holders (BondOwnershipDO). We also report the size, Tobin’s Q, leverage, interest expense, market-to-book ratios, and operating income ratio for both firm-quarters in violation and firm-quarters not in violation. As many firms are in both samples at some points in time, it is not surprising that the firms are fairly comparable in most aspects. As a preview of our results, we see that both the total level and the total number of dual holders is higher for firms in violation than for firms not in violation. Even in the simple univariate comparisons, we can see that the average level and the average number of dual holders is higher in the violating firms for bond-equity dual holders, but not for loan-equity dual holders.

123 3.5. Empirical Results

3.5.1. Model

We begin our analysis by restricting our sample to firms within a narrow bandwidth on either side of their respective violation thresholds. The empirical appeal of focusing on this

“regression discontinuity sample” is that it is homogenizes violating (treated) and non-violating

(control) firms such that the only difference between the groups is whether they are in violation of a financial covenant. This methodology is commonly used in the covenant violation literature.

Chava and Roberts (2008) postulate that the violation dummy is such a valid instrument in this homogenous sample that smooth functions of the distance to violation are unnecessary, and results can be interpreted as being caused by the violation. This methodology therefore provides an appropriate test for investors’ strategic reactions to loan covenant violations. Figure 2 illustrates our results. The x-axis shows the distance from the violation threshold in the previous quarter and the y-axis shows the dual ownership or number of dual owners in the current quarter.

For both the level of dual ownership and the number of dual owners we see a discontinuous jump in dual ownership if a loan covenant violation has occurred.

We run regression discontinuities examining the effect of loan covenant violations on the future level of and changes in dual ownership as well as changes in the number of dual owners for all firm-quarters in our sample that are within 0.5 (50% above or below the required ratio), on either side, of financial covenant thresholds.36 The most comprehensive specification is

퐷푢푎푙푂푤푛푒푟푠ℎ푖푝, = 훽0 + 훽1 푉푖표푙푎푡푖표푛i,t-1 + 훾푋i,t + η+ λ + ε,

36 We based our regression discontinuity bandwidth on Chava, Nanda, and Xiao (2015).

124 where DualOwnership is the natural log of dual ownership (or, more precisely, the natural log of

1 + the percentage of equity held by dual owners), 훽0 is an intercept term, Violationi,t-1 is the

covenant violation dummy, which equals one if 푧, - 푧, < 0 and zero otherwise, where

푧,is the financial ratio level and 푧,is that ratio’s covenant threshold. Control variables measured at the calendar quarter t-1 (푋i,t-1), industry fixed effects (η), year fixed effects

(λ), and a random error term (ε,) are also included, consistent with prior literature.

3.5.2. Results Discussion

We first examine the broadest definition of dual ownership, which sums the percentage of equity held by all investors that either hold the firm’s bonds or have at least one loan outstanding to the firm. Table VI shows that dual ownership s greater for firms that are barely in violation of loan covenants than for firms that narrowly avoid violating loan covenants. The regression discontinuity design allows us to interpret these results as the causal effect of loan covenant violations on the level of dual ownership. Specifically, loan covenant violations cause dual ownership to be 14.0% (0.0090/0.0644) higher relative to the sample average of dual ownership of non-violating firm-quarters.

We repeat this analysis for two subsets of our dual ownership sample: dual owners that have loans outstanding to the firm (loan-equity dual holders) and dual owners that hold the firm’s bonds (bond-equity dual holders). Recall that lenders receive significant control and negotiating power to protect their interests when loan covenants are violated, but bondholders, who receive no additional control following loan covenant violations, remain exposed to wealth transfer risk. We therefore expect bondholders to increase their equity held in violating firms, but we do not expect this response from lenders.

125 The results in Table VII and Table III clearly support these hypotheses. Table VII demonstrates that bondholding dual owners drive the increase in total dual ownership shown in

Table VI. These results are economically significant, representing between 10% and 20% of the sample average. The estimate loses statistical significance once industry fixed effects are added but are found to be significant in later tests.

Table VIII provides no evidence that lending dual owners respond to loan covenant violations by changing dual ownership. Again, these are the creditors whose covenants are violated. They receive additional control upon violation and are therefore able to protect their claims on the firm. The differential responses between the two groups demonstrates a difference in dual ownership incentives following loan covenant violations, and the most obvious difference between the groups is that one group becomes better positioned to protect against wealth transfer risk while the other does not.

The most appropriate test of our hypotheses is to examine changes in the dual ownership variable following loan covenant violations because we hypothesize that dual owners respond or change exposure to increases in wealth transfer risk triggered by covenant violations. We again conduct our regression discontinuity tests for all dual owners that are sufficiently close to covenant thresholds as well as the two subsets partitioned on creditor type. For these specifications, the dual ownership variables are calculated by subtracting dual ownership in the quarter of violation from dual ownership in the quarters one, two, three, and four quarters after the violation. This provides insight into whether the same dual owners are simply increasing equity exposures or if more investors decide to become dual owners in response to covenant violations.

126 The results in Table IX show that equity held by dual owners increases more in violating firms in the three and four quarters following covenant violations than it does for non-violating firms. Again, this suggests that creditors have an incentive to increase dual ownership following an exogenous increase in wealth transfer risk. Interestingly, the number of dual owners increases immediately and in a monotonically increasing fashion in each of the four quarters following the covenant violation. Specifically, violating firms gain 0.68 dual owners (about 8.8% of the sample average) in the year following a covenant violation relative to non-violating firms. At least part of the increase in violating firms’ dual ownership is driven by investors that previously were not dual owners and decided to become dual owners in response to the covenant violation.

Furthermore, the magnitude of the changes increases monotonically over time, suggesting that the reactions of dual ownership to loan covenant violations are gradual in nature as opposed to an immediate reaction that occurs when capital markets learn about the violation.

We further partition our sample into a subsample of dual holding lenders and dual holding bondholders. Consistent with our hypothesis and the results in Table VII and Table VIII, we once again find clear evidence of a response by bondholders and no evidence of a response by lenders. Table X shows a positive and statistically significant increase in dual ownership by bondholders three to four quarters after covenant violations and no evidence of a response by dual holding lenders. One year after the quarter in which a loan covenant is violated dual ownership by bondholders increases 0.80 percentage points, or about 9.3% of the sample average, for violating firms relative to non-violating firms.

The change in number of dual holding bondholders increases even more quickly, showing a statistically significant change two quarters after violation. This effect is also statistically significant. The number of bondholding dual owners increases by approximately

127 10% of the sample average for violating firms relative to non-violating firms. Table XI, on the other hand, shows no evidence that lending dual owners respond to loan covenant violations.

These results are similar to the results found for all dual holders in the regression discontinuity sample.

We test hypothesis three by partitioning our sample firms based on financial distress.

Hypothesis three predicts stronger investor reactions to loan covenant violations by financially distressed firms because wealth transfer risk is increasing in financial distress. Following Chu

(2018), we divide our Regression Discontinuity Sample into three terciles based on leverage, a commonly used proxy for financial distress. The High Financial Distress subsample is comprised of sample firms in the top tercile of leverage, and the Low Financial Distress subsample is comprised of sample firms in the bottom tercile of leverage. We then rerun our baseline regressions with the high and low financial distress terciles for all dual owners, bondholding dual owners and lending dual owners.

Consistent with hypothesis three, Table XII shows increases in bondholder dual ownership following covenant violations are concentrated in financially distressed firms. The coefficients of the High Financial Distress sample in Panel B are positive and statistically significant two, three, and four quarters after the violation. Importantly, there is no evidence that bondholding dual owners of low financial distress firms respond to covenant violations by increasing dual ownership. Firms that are not financially distressed pose little wealth transfer risk to debtholders, so these bondholding dual owners have little incentive to hedge wealth transfer risk via increased dual ownership. As in previous tables, we find no evidence that lending dual owners, of neither high nor low financial distress firms, change dual ownership in response to covenant violations. Again, their enhanced negotiating position following loan covenant

128 violations protect them from wealth transfer risk. This finding strongly supports wealth transfer risk reduction as the motivation because we only find the results our hypothesis predicts in the subsample of investors that face the greatest wealth transfer risk.

Finally, we partition our sample based on the initial level of dual ownership. The results in Table XIII confirm the prediction of hypothesis four. Increases in dual ownership following covenant violations are driven by bondholding dual owners in firms with less initial dual ownership. This is intuitive because an increase in dual ownership of five percentage points will have a greater marginal impact on shareholder-creditor conflicts if it increases dual ownership from five percent to ten percent (i.e. a 100% increase) as opposed to 50% to 55% (i.e. a 10% increase). It is also interesting to note that lending dual owners decrease dual ownership following covenant violations by those firms with high initial dual ownership. The combination of a lower marginal benefit of dually owning such firms and the enhanced negotiating position lenders enjoy following loan covenant violations encourages these creditors to reduce dual ownership. This result also highlights the differing nature of dual owning bondholders and lenders.

3.6. Conclusion

This paper documents dual ownership’s role as a capital market solution for resolving shareholder-creditor conflicts prior to the restructuring stage. We do so by exploiting loan covenant violations in a regression discontinuity framework to establish the causal effect of wealth transfer risk on creditors’ dual ownership. We show that dual ownership is a strategic choice by investors. Both the percentage of equity held by dual owners and the number of dual owners increase following covenant violations. In addition, this result is more pronounced when the borrowers are in financial distress and when there is a larger marginal benefit of dual

129 ownership, further supporting increased wealth transfer risk as the strategic consideration driving the dual ownership decision.

Identification of the wealth transfer risk mitigation channel is strengthened by our unique and comprehensive dataset, which enables us to demonstrate different responses by different types of investors. Dual holding lenders, who receive state-contingent control rights when a covenant violation occurs, are well-positioned to protect their interests following violations.

Thus, there is less need to hedge wealth transfer risk via dual ownership. Bondholders, on the other hand, do not receive additional control rights following the violation and therefore remain exposed to wealth transfer risk. They consequently resort to dual ownership as an alternative method of risk mitigation.

Our findings complement a vast literature on debt-contracting and loan covenants by showing that capital market investors can resolve agency costs without contribution from the issuing firm. In this way, dual ownership and loan covenants can be viewed as alternatives means to achieve the same end. These results highlight the need to consider dual ownership and the diverging incentives among creditors in future studies of agency costs.

130 Figure 3.1. Distances from Covenant Violation Histogram

Figure 1 depicts the frequency of observations in our regression discontinuity sample. These firm-quarter observations are included as the relevant current ratio or net worth ratio is within 50% above or below the binding covenant threshold. The x-axis shows the percentage difference between the firm’s actual accounting ratio and the required threshold, where positive numbers distances implies being in compliance.

131 Figure 3.2. Discontinuities Around Covenant Thresholds

Figure 2 presents the level of total dual ownership and number of total dual owners (both debt holders and lenders) within the 0.5 bandwidths of distance away from the relevant threshold. Each dot is the average dual ownership within the bin. Observations to the left of 0 are in violation of the covenant. Local quadratic regression estimates and 95% confidence bands are added.

132 Table 3.1. Illustrative Example Aggregating to Parent Level

Table I illustrates how we form our dual ownership variables of interest (number of dual holders and dual ownership) by each type of dual holder. We first aggregate to the parent (brand) level all the funds in our datasets who own equity, hold bonds outstanding, or are an active lender to Gibraltar Industries in the first quarter of 2010. Parents that hold equity and have bonds outstanding but not loans outstanding are labeled as loan-equity dual holders; and parents that hold equity, bonds, and loans outstanding are labeled as bond-loan-equity dual holders. For each parent that is a creditor (loans outstanding or bondholder) who simultaneously holds equity, we report the number of bond funds and lending funds in the given quarter. We also report the percent of total shares outstanding held by each parent in the given quarter. To see the total ownership by type of dual holder, we report the cumulative stock ownership by each dual holder type. The total number of dual holders is calculated as the total number of parents (6+4+1) who are any of the three types of dual holders and the total equity held by dual holders is the sum of all the stock ownership held reported in this table (13.99%+2.81%+2.50%).

Number Brand Name # Bond # Lending Stock Cumulative Funds Funds Ownership Stock Ownership Panel A: Bond-Equity dual holders 1 Ameriprise 10 0 1.74% 1.74% Financial 2 Invesco 7 0 1.39% 3.13% 3 Manulife 2 0 0.08% 3.21% Financial 4 Principal 1 0 0.37% 3.58% Financial Group 5 Prudential 1 0 0.46% 4.04% Financial 6 T. Rowe 4 0 9.95% 13.99% Price

Panel B: Loan-Equity dual holders 1 Bank of 0 1 2.68% 2.68% America 2 Comerica 0 1 0.11% 2.79% 3 PNC 0 2 0.01% 2.80% 4 US Bancorp 0 1 0.01% 2.81%

Panel C: Bond-Loan-Equity dual holders 1 J.P. Morgan 2 1 2.50% 2.50%

133 Table 3.2. Illustrative Example Construction of Dual Ownership

Table II reports the aggregate number of dual holders and total dual ownership for Gibraltar Industries in the first quarter of 2010. Using the numbers provided in Table I, we aggregate and create our main variables of interest (number of dual owners and dual ownership) by the three types of dual holders: bondholders who simultaneously hold equity, lenders who simultaneously hold equity, and bondholders who are also lenders and hold equity. The first column reports the type of dual holder, the second column reports the number of dual holders (at the brand level) of the respective type, and the third column reports the total equity ownership as a percent of total shares outstanding held by all the parents of its respective type in the given quarter.

Dual Holder Type Number of Dual Holders Ownership Bond-Equity 6 13.99% Loan-Equity 4 2.81% Bond-Loan-Equity 1 2.50% Total (all three types) 11 19.30%

134 Table 3.3. Violations Per Year

This table presents the distribution of violations over our sample period. Frequency lists the number of violations that occurred in the year and Percentage lists the percentage of the total violations (in our sample) that occurred in the given year.

Year Frequency Percentage 1998 114 5.57 1999 184 8.99 2000 162 7.92 2001 163 7.97 2002 180 8.80 2003 170 8.31 2004 139 6.79 2005 97 4.74 2006 105 5.13 2007 108 5.28 2008 123 6.01 2009 79 3.86 2010 83 4.06 2011 95 4.64 2012 81 3.96 2013 60 2.93 2014 61 2.98 2015 42 2.05

135 Table 3.4. Summary Statistics for Firms in Violation

This table presents the summary statistics of firms in violation of their loan covenant. Size is the natural log of total assets. TobinsQ is (Market Value + Total Liabilities) / Total Assets. Lev is the ratio of total debt to total assets. Mtb is the ratio of Market Value and Book Value. Intexp is the ratio of Interest expense and average assets. Operasst is the ratio of operating income before depreciation to average assets. TotalNumberDO is the number of all dual owners. LoanNumberDO is the number of lenders only dual owners. BondNumberDO is the number of bondholders only dual owners. TotalOwnershipDO is the amount of equity owned by all dual owners. LoansOwnershipDO is the amount of equity owned by lender only dual owners. BondOwnershipDO is the amount of equity owed by bondholder only dual owners.

Variable N Mean SD 5th 25th Median 75th 95th Perc. Perc. Perc. Perc. Size 2046 7.0475 1.1920 5.1798 6.2417 7.0033 7.8816 8.9691 Lev 2046 0.4016 0.2164 0.0951 0.2701 0.3804 0.4908 0.7689 Intexp 2046 0.0079 0.0055 0.0015 0.0044 0.0069 0.0100 0..0181 TobinsQ 2046 1.5211 0.9633 0.8240 1.0624 1.3043 1.7060 2.6046 Mtb 2046 1.2199 0.9535 0.5266 0.7799 1.0136 1.3911 2.2874 Operasst 2046 0.0304 0.0507 -0.0219 0.0184 0.0337 0.0496 0.0844 Total 2046 7.7664 10.0058 1.0000 1.0000 3.0000 10.0000 29.0000 Number DO Loan 1568 2.8935 2.1937 1.0000 1.0000 2.0000 4.0000 7.0000 Number DO Bond 1266 8.4992 9.1259 1.0000 2.0000 5.0000 12.0000 28.0000 Number DO Total 2046 0.0644 0.0845 0.0000 0.0037 0.0231 0.1010 0.2585 Ownership DO Loan 1568 0.0155 0.0232 0.0000 0.0011 0.0062 0.0205 0.0583 Ownership DO Bond 1266 0.0826 0.0853 0.0003 0.0103 0.0557 0.1304 0.2596 Ownership DO

136 Table 3.5. Summary Statistics for Firms not in Violation

This table presents the summary statistics of firms not in violation of their loan covenant. Size is the natural log of total assets. TobinsQ is (Market Value + Total Liabilities) / Total Assets. Lev is the ratio of total debt to total assets. Mtb is the ratio of Market Value and Book Value. Intexp is the ratio of Interest expense and average assets. Operasst is the ratio of operating income before depreciation to average assets. TotalNumberDO is the number of all dual owners. LoanNumberDO is the number of lenders only dual owners. BondNumberDO is the number of bondholders only dual owners. TotalOwnershipDO is the amount of equity owned by all dual owners. LoansOwnershipDO is the amount of equity owned by lender only dual owners. BondOwnershipDO is the amount of equity owed by bondholder only dual owners.

Variable N Mean SD 5th 25th Median 75th 95th Perc. Perc. Perc. Perc. Size 5216 7.0338 1.2358 5.1676 6.1691 6.9389 7.7855 9.3472 Lev 5216 0.2903 0.1814 0.0027 0.1670 0.2828 0.3966 0.6041 Intexp 5216 0.0051 0.0040 0.0003 0.0024 0.0044 0.0070 0.0124 TobinsQ 5216 1.5880 0.9909 0.8487 1.0913 1.3425 1.7466 3.0188 Mtb 5216 1.3045 0.9930 0.5589 0.8022 1.0541 1.4760 2.7325 Operasst 5216 0.0350 0.0365 0.0017 0.0224 0.0339 0.0487 0.0824 TotalNumberDO 5216 6.3357 9.0840 1.0000 1.0000 3.0000 7.0000 22.0000 LoanNumberDO 4394 3.0005 2.4630 1.0000 1.0000 2.0000 4.0000 8.0000 BondNumberDO 2459 7.7060 9.2926 1.0000 2.0000 5.0000 9.0000 26.0000 TotalOwnershipDO 5216 0.0454 0.0643 0.0000 0.0033 0.0169 0.0610 0.1886 LoanOwnershipDO 4394 0.0181 0.0243 0.0000 0.0012 0.0082 0.0252 0.0694 BondOwnershipDO 2459 0.0623 0.0692 0.0006 0.0074 0.0351 0.0990 0.2029

137 Table 3.6. Level of All Dual Ownership

This table presents coefficients from the Regression Discontinuity Sample. The sample is restricted to only observations within the 0.5 bandwidth. The dependent variable is the level of dual ownership held by any type of dual owner. Each model progressively adds controls and fixed effects. Size is the natural log of total assets. TobinsQ is (Market Value + Total Liabilities) / Total Assets. Lev is the ratio of total debt to total assets. Mtb is the ratio of Market Value and Book Value. Intexp is the ratio of Interest expense and average assets. Operasst is the ratio of operating income before depreciation to average assets. All independent variables are lagged one period. Standard errors are clustered at the firm level. All variable definitions are in the Appendix. T-stats appear in parentheses below the coefficient estimates. *, **, and *** indicate significance at the 10%, 5%, and 1%, respectively.

TotalOwnershipDO 1 2 3 4 Violation 0.0256*** 0.0206*** 0.0143*** 0.0090* (4.17) (3.48) (2.59) (1.87) Size 0.0306*** 0.0296*** (8.71) (8.33) TobinsQ 0.0127 0.0107 (0.57) (0.48) Lev 0.0401 0.0423 (1.53) (1.58) Mtb -0.0152 -0.0143 (-0.71) (-0.65) Intexp 0.3396 -0.0948 (0.34) (-0.09) Operasst 0.0660* 0.0420 (1.93) (1.26) Cluster Firm Firm Firm Firm Time FE No Yes Yes Yes Industry FE No No No Yes Obs. 5051 4866 3801 3801 Adj. R^2 0.03 0.09 0.32 0.33

138 Table 3.7. Level of Bond Dual Ownership

This table presents coefficients from the Regression Discontinuity Sample. The sample is restricted to only observations within the 0.5 bandwidth. The dependent variable is the level of dual ownership held by any dual owners that hold public debt. Each model progressively adds controls and fixed effects. Size is the natural log of total assets. TobinsQ is (Market Value + Total Liabilities) / Total Assets. Lev is the ratio of total debt to total assets. Mtb is the ratio of Market Value and Book Value. Intexp is the ratio of Interest expense and average assets. Operasst is the ratio of operating income before depreciation to average assets. All independent variables are lagged one period. Standard errors are clustered at the firm level. All variable definitions are in the Appendix. T-stats appear in parentheses below the coefficient estimates. *, **, and *** indicate significance at the 10%, 5%, and 1%, respectively.

BondOwnershipDO 1 2 3 4 Violation 0.0269*** 0.0163** 0.0153** 0.0085 (3.35) (2.23) (2.16) (1.44) Size 0.0339*** 0.0348*** (5.97) (6.29) TobinsQ 0.0156 0.0104 (0.46) (0.31) Lev 0.0501 0.0537 (1.26) (1.28) Mtb -0.0112 -0.0081 (-0.35) (-0.26) Intexp 0.2714 0.0206 (0.18) (0.01) Operasst 0.1361** 0.0916 (2.31) (1.47) Cluster Firm Firm Firm Firm Time FE No Yes Yes Yes Industry FE No No No Yes Obs. 2523 2433 1979 1979 Adj. R^2 0.03 0.12 0.27 0.31

139 Table 3.8. Level of Lender Dual Ownership

This table presents coefficients from the Regression Discontinuity Sample. The sample is restricted to only observations within the 0.5 bandwidth. The dependent variable is the level of dual ownership held debt only through a lending relationship. Each model progressively adds controls and fixed effects. Size is the natural log of total assets. TobinsQ is (Market Value + Total Liabilities) / Total Assets. Lev is the ratio of total debt to total assets. Mtb is the ratio of Market Value and Book Value. Intexp is the ratio of Interest expense and average assets. Operasst is the ratio of operating income before depreciation to average assets. All independent variables are lagged one period. Standard errors are clustered at the firm level. All variable definitions are in the Appendix. T-stats appear in parentheses below the coefficient estimates. *, **, and *** indicate significance at the 10%, 5%, and 1%, respectively.

LoanOwnershipDO 1 2 3 4 Violation 0.0013 0.0002 -0.0005 0.0007 (0.81) (0.12) (-0.30) (0.43) Size 0.0049*** 0.0055*** (5.63) (6.05) TobinsQ -0.0184** -0.0174** (-2.20) (-2.10) Lev 0.0037 0.0052 (0.37) (0.60) Mtb 0.0172** 0.0162* (2.08) (1.95) Intexp -0.6138* -0.5126* (-1.89) (-1.83) Operasst -0.0109 -0.0016 (-0.75) (-0.11) Cluster Firm Firm Firm Firm Time FE No Yes Yes Yes Industry FE No No No Yes Obs. 4219 4065 3189 3189 Adj. R^2 0.00 0.09 0.13 0.15

140 Table 3.9. Change of All Dual Ownership

This table presents coefficients from the Regression Discontinuity Sample. The sample is restricted to only observations within the 0.5 bandwidth. The dependent variables are the change in dual ownership held by any type of dual owner and the change in the number of any type of dual owners. F1 is the change in dual ownership defined as Dual Ownership in t+1 – Dual Ownership in t; F2 is change in dual ownership defined as Dual Ownership in t + 2 – Dual Ownership in t; F3 is change in dual ownership defined as Dual Ownership in t + 3 – Dual Ownership in t; and F4 is change in dual ownership defined as Dual Ownership in t + 4 – Dual Ownership in t. Size is the natural log of total assets. TobinsQ is (Market Value + Total Liabilities) / Total Assets. Lev is the ratio of total debt to total assets. Mtb is the ratio of Market Value and Book Value. Intexp is the ratio of Interest expense and average assets. Operasst is the ratio of operating income before depreciation to average assets. All independent variables are lagged one period. Standard errors are clustered at the firm level. All variable definitions are in the Appendix. T-stats appear in parentheses below the coefficient estimates. *, **, and *** indicate significance at the 10%, 5%, and 1%, respectively.

TotalOwnershipDO Coefficient F1 F2 F3 F4 Violation 0.0016 0.0024 0.0046* 0.0050* (1.58) (1.34) (1.96) (1.75) Size 0.0009** 0.0015** 0.0020** 0.0025** (2.07) (2.15) (2.07) (1.97) TobinsQ -0.0021 -0.0006 -0.0025 -0.0026 (-0.57) (-0.09) (-0.29) (-0.23) Lev -0.0038 0.0021 0.0050 0.0082 (-0.85) (0.27) (0.47) (0.64) Mtb 0.0030 0.0021 0.0043 0.0045 (0.80) (0.33) (0.49) (0.39) Intexp -0.0214 -0.2613 -0.2905 -0.5609 (-0.11) (-0.76) (-0.64) (-0.94) Operasst -0.0076 -0.0329 -0.0094 -0.0272 (-0.63) (-1.58) (-0.29) (-0.74) Time FE Yes Yes Yes Yes Cluster Firm Firm Firm Firm Obs. 3666 3526 3396 3287 Adj. R^2 0.01 0.04 0.05 0.09

141 Table 3.9. (Continued)

Number of All DO Coefficient F1 F2 F3 F4 Violation 0.1908** 0.3903** 0.5055** 0.6845*** (2.06) (2.21) (2.44) (2.94) Size 0.1189** 0.2295** 0.3313** 0.3272 (2.26) (2.30) (2.19) (1.64) TobinsQ -0.1262 -0.5334 -1.2191 -1.2367 (-0.41) (-1.01) (-1.52) (-1.13) Lev 0.2416 0.8491 0.8279 1.3344 (0.70) (1.30) (0.89) (1.11) Mtb 0.1996 0.6334 1.3544* 1.4120 (0.64) (1.19) (1.68) (1.27) Intexp -8.6001 -43.3785 -42.4877 -53.8899 (-0.59) (-1.52) (-1.10) (-1.05) Operasst 1.6918** 2.1722 3.8585 5.1914* (2.09) (1.48) (1.59) (1.84) Time FE Yes Yes Yes Yes Cluster Firm Firm Firm Firm Obs. 3666 3526 3396 3287 Adj. R^2 0.05 0.13 0.16 0.22

142 Table 3.10. Change of Bondholder Dual Ownership

This table presents coefficients from the Regression Discontinuity Sample. The sample is restricted to only observations within the 0.5 bandwidth. The dependent variables are the change in dual ownership held by bondholder dual owners and the change in the number of bondholder dual owners. F1 is the change in dual ownership defined as Dual Ownership in t+1 – Dual Ownership in t; F2 is change in dual ownership defined as Dual Ownership in t + 2 – Dual Ownership in t; F3 is change in dual ownership defined as Dual Ownership in t + 3 – Dual Ownership in t; and F4 is change in dual ownership defined as Dual Ownership in t + 4 – Dual Ownership in t. Size is the natural log of total assets. TobinsQ is (Market Value + Total Liabilities) / Total Assets. Lev is the ratio of total debt to total assets. Mtb is the ratio of Market Value and Book Value. Intexp is the ratio of Interest expense and average assets. Operasst is the ratio of operating income before depreciation to average assets. All independent variables are lagged one period. Standard errors are clustered at the firm level. All variable definitions are in the Appendix. T-stats appear in parentheses below the coefficient estimates. *, **, and *** indicate significance at the 10%, 5%, and 1%, respectively.

BondholderOwnershipDO Coefficient F1 F2 F3 F4 Violation 0.0003 0.0015 0.0066** 0.0080* (0.22) (0.61) (1.97) (1.96) Size 0.0014* 0.0017 0.0028 0.0027 (1.85) (1.32) (1.58) (1.25) TobinsQ 0.0003 -0.0020 -0.0155 -0.0187 (0.04) (-0.21) (-1.27) (-1.19) Lev -0.0072 0.0011 -0.0058 -0.0049 (-1.14) (0.10) (-0.38) (-0.27) Mtb 0.0010 0.0041 0.0184 0.0225 (0.16) (0.41) (1.44) (1.38) Intexp 0.2737 -0.3401 -0.2060 -0.4546 (0.96) (-0.66) (-0.33) (-0.59) Operasst -0.0068 -0.0646** -0.0367 -0.0301 (-0.38) (-2.10) (-0.69) (-0.63) Time FE Yes Yes Yes Yes Cluster Firm Firm Firm Firm Obs. 1868 1798 1717 1658 Adj. R^2 0.00 0.01 0.02 0.02

143 Table 3.10. (Continued)

Number of Bondholder DO Coefficient F1 F2 F3 F4 Violation 0.0794 0.3284* 0.5677** 0.8351*** (0.88) (1.94) (2.41) (2.75) Size 0.1782** 0.3095** 0.3626* 0.3404 (2.28) (2.06) (1.70) (1.40) TobinsQ 0.0520 -0.5461 -0.7729 -0.7729 (0.13) (-0.76) (-0.72) (-0.56) Lev -0.2857 0.1724 -0.2468 -0.2885 (-0.63) (0.21) (-0.21) (-0.21) Mtb 0.0965 0.7429 1.1191 1.2548 (0.24) (1.02) (1.04) (0.91) Intexp 9.9850 -43.6597 -49.5936 -55.5089 (0.53) (-1.24) (-1.04) (-0.91) Operasst 1.7531 1.7357 1.7190 3.1712 (1.31) (0.74) (0.47) (0.72) Time FE Yes Yes Yes Yes Cluster Firm Firm Firm Firm Obs. 1868 1798 1717 1658 Adj. R^2 0.01 0.04 0.04 0.05

144 Table 3.11. Change of Lender Dual Ownership This table presents coefficients from the Regression Discontinuity Sample. The sample is restricted to only observations within the 0.5 bandwidth. The dependent variables are the change in dual ownership held by lender dual owners and the change in the number of lender dual owners. F1 is the change in dual ownership defined as Dual Ownership in t+1 – Dual Ownership in t; F2 is change in dual ownership defined as Dual Ownership in t + 2 – Dual Ownership in t; F3 is change in dual ownership defined as Dual Ownership in t + 3 – Dual Ownership in t; and F4 is change in dual ownership defined as Dual Ownership in t + 4 – Dual Ownership in t. Size is the natural log of total assets. TobinsQ is (Market Value + Total Liabilities) / Total Assets. Lev is the ratio of total debt to total assets. Mtb is the ratio of Market Value and Book Value. Intexp is the ratio of Interest expense and average assets. Operasst is the ratio of operating income before depreciation to average assets. All independent variables are lagged one period. Standard errors are clustered at the firm level. All variable definitions are in the Appendix. T-stats appear in parentheses below the coefficient estimates. *, **, and *** indicate significance at the 10%, 5%, and 1%, respectively.

LoanOwnershipDO Coefficient F1 F2 F3 F4 Violation -0.0001 -0.0003 -0.0007 -0.0009 (-0.23) (-0.42) (-0.77) (-0.82) Size -0.0000 0.0003 0.0005 0.0005 (-0.20) (1.06) (1.33) (1.01) TobinsQ 0.0003 0.0021 0.0028 0.0015 (0.19) (0.89) (0.83) (0.30) Lev 0.0001 -0.0020 -0.0046 -0.0030 (0.03) (-0.55) (-0.99) (-0.50) Mtb -0.0000 -0.0018 -0.0025 -0.0010 (-0.02) (-0.73) (-0.74) (-0.22) Intexp 0.0314 0.1064 0.2099 0.1262 (0.36) (0.73) (1.09) (0.49) Operasst 0.0040 -0.0021 0.0061 0.0045 (0.70) (-0.24) (0.54) (0.48) Time FE Yes Yes Yes Yes Cluster Firm Firm Firm Firm Obs. 3079 2947 2839 2754 Adj. R^2 0.00 0.00 0.01 0.02

145 Table 3.11. (Continued)

Number of Lender DO Coefficient F1 F2 F3 F4 Violation 0.0139 0.0067 -0.0204 0.0214 (0.48) (0.16) (-0.34) (0.31) Size 0.0238** 0.0656*** 0.1268*** 0.1376*** (2.36) (3.60) (4.60) (3.82) TobinsQ 0.0989 0.2438* -0.0146 -0.0523 (1.08) (1.78) (-0.08) (-0.21) Lev 0.0463 -0.0769 -0.3264 -0.4277 (0.47) (-0.43) (-1.33) (-1.51) Mtb -0.0870 -0.2203 0.0491 0.0957 (-0.96) (-1.62) (0.26) (0.39) Intexp 0.7369 6.5321 16.4382 21.2853* (0.15) (0.84) (1.57) (1.81) Operasst 0.3332 -0.3714 0.7808 1.1957* (1.14) (-0.87) (1.30) (1.77) Time FE Yes Yes Yes Yes Cluster Firm Firm Firm Firm Obs. 3079 2947 2839 2754 Adj. R^2 0.02 0.04 0.07 0.07

146 Table 3.12. Cross Section Firm Heterogeneity of Violation on Dual Ownership

This table presents coefficients from the Regression Discontinuity Sample. The sample is restricted to only observations within the 0.5 bandwidth. All three panels show the changes in dual ownership split between firms with high financial distress and low financial distress. All firms in the upper tercile of leverage are high financial distress and all firms in the bottom tercile of leverage are low financial distress. F1 is the change one quarter after violation, F2 is the change two quarters after violation, etc. Panel A shows the change in ownership for all dual owners. Panel B shows the change in ownership for bondholder dual owners. Panel C shows the change in ownership for all lender dual owners. Control variables are the same as the previous tables and are unreported for brevity. All variable definitions are in the Appendix. T-statistics are adjusted for heteroskedasticity and firm-level clustering and appear in parentheses below the coefficient estimates. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Total Dual Ownership High Financial Distress Low Financial Distress F1 F2 F3 F4 F1 F2 F3 F4 Violation 0.0023 0.0032 0.0078* 0.0073 0.0008 0.0036 0.0016 -0.0006 (1.16) (1.04) (1.84) (1.37) (0.34) (0.83) (0.33) (-0.10) Firm Yes Yes Yes Yes Yes Yes Yes Yes Controls Time FE Yes Yes Yes Yes Yes Yes Yes Yes Cluster Firm Firm Firm Firm Firm Firm Firm Firm Obs. 1208 1159 1121 1088 976 938 902 873 Adj. R^2 0.02 0.04 0.04 0.05 0.02 0.08 0.13 0.17

Panel B: Bondholder Dual Ownership High Financial Distress Low Financial Distress F1 F2 F3 F4 F1 F2 F3 F4 Violation 0.0032 0.0083* 0.0183*** 0.0176*** -0.0025 0.0032 0.0076 0.0144 (1.07) (1.78) (3.05) (2.78) (-0.63) (0.45) (0.95) (1.48) Firm Yes Yes Yes Yes Yes Yes Yes Yes Controls Time FE Yes Yes Yes Yes Yes Yes Yes Yes Cluster Firm Firm Firm Firm Firm Firm Firm Firm Obs. 628 606 575 549 501 482 464 446 Adj. R^2 -0.00 0.02 0.05 0.07 -0.02 0.01 0.02 0.02

147 Table 3.12. (Continued)

Panel C: Lender Dual Ownership High Financial Distress Low Financial Distress F1 F2 F3 F4 F1 F2 F3 F4 Violation 0.0003 -0.0006 -0.0012 -0.0024 -0.0006 -0.0001 0.0003 -0.0011 (0.33) (-0.46) (-0.79) (-1.18) (-0.48) (-0.05) (0.13) (-0.33) Firm Yes Yes Yes Yes Yes Yes Yes Yes Controls Time FE Yes Yes Yes Yes Yes Yes Yes Yes Cluster Firm Firm Firm Firm Firm Firm Firm Firm Obs. 1018 973 937 907 811 775 744 723 Adj. R^2 -0.00 0.01 0.02 0.03 0.00 0.01 0.03 0.04

148 Table 3.13. Cross Sectional Investor Heterogeneity of Violation on Dual Ownership

This table presents coefficients from the Regression Discontinuity Sample. The sample is restricted to only observations within the 0.5 bandwidth. All three panels show the changes in dual ownership split between firms with high initial dual ownership and low initial dual ownership. All firms above the median dual ownership are high initial dual ownership and all firms below the median dual ownership are low initial dual ownership. F1 is the change one quarter after violation, F2 is the change two quarters after violation, etc. Panel A shows the change in ownership for all dual owners. Panel B shows the change in ownership for bondholder dual owners. Panel C shows the change in ownership for all lender dual owners. Control variables are the same as the previous table and are unreported for brevity. All variable definitions are in the Appendix. T-statistics are adjusted for heteroskedasticity and firm-level clustering and appear n parentheses below the coefficient estimates. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Total Dual Ownership High Initial Dual Ownership Low Initial Dual Ownership F1 F2 F3 F4 F1 F2 F3 F4 Violation 0.0009 0.0020 0.0049 0.0047 0.0028* 0.0039* 0.0051** 0.0065** (0.43) (0.66) (1.22) (1.05) (1.92) (1.95) (1.97) (2.22) Firm Yes Yes Yes Yes Yes Yes Yes Yes Controls Time FE Yes Yes Yes Yes Yes Yes Yes Yes Cluster Firm Firm Firm Firm Firm Firm Firm Firm Obs. 1849 1815 1772 1736 1817 1711 1624 1551 Adj. R^2 0.03 0.06 0.08 0.12 0.02 0.03 0.05 0.06

Panel B: Bondholder Dual Ownership High Initial Dual Ownership Low Initial Dual Ownership F1 F2 F3 F4 F1 F2 F3 F4 Violation -0.0014 0.0031 0.0055 0.0055 0.0038* 0.0026 0.0104** 0.0149*** (-0.56) (0.75) (0.98) (0.85) (1.67) (0.75) (2.41) (3.05) Firm Yes Yes Yes Yes Yes Yes Yes Yes Controls Time FE Yes Yes Yes Yes Yes Yes Yes Yes Cluster Firm Firm Firm Firm Firm Firm Firm Firm Obs. 1021 998 960 930 847 800 757 728 Adj. R^2 0.01 0.01 0.03 0.02 0.02 0.01 0.04 0.05

149 Table 3.13. (Continued)

Panel C: Lender Dual Ownership High Initial Dual Ownership Low Initial Dual Ownership F1 F2 F3 F4 F1 F2 F3 F4 Violation -0.0001 -0.0017* -0.0025** -0.0028* -0.0002 0.0011 0.0009 0.0006 (-0.14) (-1.70) (-1.97) (-1.86) (-0.37) (1.02) (0.74) (0.45) Firm Yes Yes Yes Yes Yes Yes Yes Yes Controls Time FE Yes Yes Yes Yes Yes Yes Yes Yes Cluster Firm Firm Firm Firm Firm Firm Firm Firm Obs. 1446 1403 1360 1328 1633 1544 1479 1426 Adj. R^2 0.00 0.01 0.03 0.04 0.02 0.02 0.03 0.03

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159 APPENDIX

Variable Definitions

Dual Ownership (DO) = Natural log of (1 + percentage of equity held by dual owners)

Change in Dual Ownership (Fx DO) = 퐷푢푎푙푂푤푛푒푟푠ℎ푖푝 – 퐷푢푎푙푂푤푛푒푟푠ℎ푖푝

Total Assets = atq

Average Assets = ((Total assets) + (Total assets lagged one period)) / 2

Market-to-Book Ratio = (Market Value) / (Total Assets), where

Market Value = Market value of equity + book value of equity + total assets,

Market value of equity = prccq*cshoq,

Book value of equity = Total assets – ltw + txditcq

Total Debt = dlcq + dlttq

Leverage Ratio = (Total debt) / (Total assets)

Total Liabilities = ltq

Net Worth = (Total assets) = (Total Liabilities)

Firm Size = ln(Total assets)

Leverage = (Total debt)/(Total assets)

Current Ratio = atcq/lctq

Tobin’s Q = (Market value + Total Liabilities) / Total Assets

Interest Exp / Avg Assets = (xintq) / Average Assets

Op. ROA = (oibdpq) / Average Assets

Tightness (current ratio threshold) = Current Ratio = Current Ratio Covenant Threshold

Tightness (net worth threshold) = Net Worth – Net Worth Covenant Threshold

160 Distance (current ratio threshold) = (Current Ratio Value – Current Ratio Covenant Threshold) /

Current Ratio Covenant Threshold

Distance (net worth threshold) = (Net Worth Value – Net Worth Covenant Threshold) / Net

Worth Covenant Threshold

161