MASARYK UNIVERSITY Impacts of the Zero Interest Rate Environment

MASARYK UNIVERSITY Faculty of Economics and Administration Field of Study: Finance

Impacts of the Zero Interest Rate Environment: Exploring Growth Drivers of Direct Lending in Germany

Dissertation

Supervisor: Author: Prof. Dr. Dr. habil. Eric Frère Ilja Schaab, M.Sc.

Brno, 2020

Abstract The Eurozone’s historically low interest rates affect banks, corporations, and investors in different ways. This dissertation presents an explorative analysis of the behavior of these three groups in the given zero interest rate environment in order to explain the direct lending industry’s considerable growth in Germany. The bank-related results demonstrate that lower intermediation costs within a zero interest rate environment lead to stronger profit growth in the following period. The corporation-related results indicate that key interest rates significantly positively affect companies’ investment behavior, significantly negatively affect their average return on assets, significantly negatively affect their cash holdings, and exert no unambiguous effect on their credit demand. The investor-related results reveal that investors significantly change their behavior in a zero interest rate environment and demonstrably resort to riskier investments, confirming the debate about reaching for yield. Overall, these results indicate an advantageous environment for the growth of the direct lending industry, which was already able to achieve a significant market share in relation to the total corporate lending market and the total investment market in Germany.

Keywords: Corporate Direct Lending Industry in Germany Corporate Finance and Investment Strategies Financial Intermediation Zero Interest Rate Environment Zero Yield Bias

JEL Codes: E52, G11, G20, G30, G40

Declaration of Authenticity I hereby declare that the dissertation Impacts of the Zero Interest Rate Environment: Exploring Growth Drivers of Direct Lending in Germany is my own work carried out under the supervision of Prof. Dr. Dr. habil. Eric Frère, and that I have duly acknowledged all sources in accordance with the law, the internal regulations of Masaryk University, and the binding internal documents of Masaryk University and of the Faculty of Economics and Administration.

The views expressed in this dissertation are my own and do not necessarily correspond with those of my employer.

Essen, 31.05.2020 ______(Signature)

Acknowledgements

„Wen die Dankbarkeit geniert, Der ist übel dran; Denke, wer dich erst geführt, Wer für dich getan!”

Johann Wolfgang von Goethe (1814, 390)

First of all, I would like to express my gratitude to my supervisor Prof. Dr. Dr. habil. Eric Frère for his guidance and encouragement over the last years. During this time, he has not only promoted my academic but also my personal development.

Further, I would like to thank Prof. Dr. Svend Reuse who supported me with valuable advice and suggestions for improvement during the preparation of our first joint publication and thereafter. I would like to thank doc. Ing. Martin Svoboda, Ph.D. for his support and valuable guidance that considerably facilitated my cross-border studies and my first participation in the international scientific conference in Brno. My gratitude also goes to Prof. Dr. Alexander Zureck who not only recommended the doctorate program to me but also provided supportive guidance in times of study. Especially the possibility to collect data for one of my empirical studies helped me considerably in the doctoral studies.

Finally, I would like to thank my wife and daughter for their love and support on my academic journey. My gratitude also goes to my family and friends for their backing and their patience.

Thank you.

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Table of Contents List of Abbreviations ...... VII List of Figures ...... XI List of Tables ...... XII List of Symbols ...... XIV List of Formulas ...... XVII 1. Introduction ...... 1 1.1 Key Interest Rates in the Euro Area as the Starting Point of a Problem ...... 1 1.2 Objectives of the Research Project ...... 2 1.3 Course of Work ...... 3 2. Theoretical Framework of a Zero Key Interest Rate ...... 6 2.1 Role of the European Central Bank in the European Economy ...... 6 2.2 Objective Systems and Options for Action of Central Banks ...... 8 2.2.1 Instruments of an Expansive or Restrictive Monetary Policy ...... 11 2.2.2 Crisis Management by the European Central Bank ...... 13 2.3 Zero Interest Rates from a Theoretical Point of View ...... 15 2.3.1 Pre-Classical Schools of Economic Thought ...... 15 2.3.2 Classical Schools of Economic Thought ...... 18 2.3.3 Neoclassical Schools of Economic Thought ...... 20 2.3.4 Heterodox Economic Approaches ...... 25 2.3.5 Modern Theories and Microeconomic Approaches ...... 30 2.3.6 Liquidity Trap and Zero Lower Bound Problem ...... 38 2.4 Theory-Based Hypotheses Conception ...... 39 3. Financial Intermediation in a Zero Interest Rate Environment ...... 43 3.1 Components of Intermediation Costs ...... 43 3.1.1 Classical Frictions Resolved by Banks ...... 43 3.1.2 Regulatory Framework ...... 44 3.1.3 Additional Costs Emerging from the Deposit Protection Scheme ...... 46 3.2 Modelling Financial Transactions Using Utility Maximization Problem ...... 47 3.3 Competitive Landscape in the German Banking Industry ...... 49 3.3.1 Three-Pillar System ...... 49 3.3.2 Margin Development in Zero Interest Rate Environment ...... 50 3.3.3 Market Participants Utilizing Intermediation-Light Environments ...... 51 3.4 Direct Lending ...... 56 3.4.1 Definition and European Development ...... 56 3.4.2 Direct Lending in the Context of Asset Allocation ...... 57 3.4.3 Status Quo of Empirical Research ...... 58 3.5 Hypotheses Refinement – Banks ...... 58 IV

4. Corporate Finance in a Zero Interest Rate Environment ...... 60 4.1 Investment Decisions under Uncertainty ...... 60 4.2 Financing of an Investment Decision ...... 62 4.2.1 Capital Structure Theories ...... 62 4.2.2 Financing Sources in Leveraged Transactions ...... 66 4.3 Key Interest Rate Impacts on Accounting Practices ...... 67 4.3.1 Impact Analysis Focusing the Balance Sheet ...... 67 4.3.2 Impact Analysis Focusing the Profit and Loss Statement ...... 69 4.3.3 Impact Analysis Focusing the Cash Flow Statement ...... 70 4.3.4 Discrepancy between IFRS and German-GAAP Accounting ...... 70 4.4 Selection of IFRS Financials for Empirical Research ...... 71 4.5 Hypotheses Refinement – Corporations ...... 72 5. Investor Behavior in Times of Zero Key Interest Rates ...... 74 5.1 Asset Allocation in Practice ...... 74 5.2 Zero Yield Bias ...... 76 5.3 Hypotheses Refinement – Investors ...... 78 6. Testing the Bank-Related Hypotheses ...... 79 6.1 Empirical Study: Determinants of Bank Profitability Growth ...... 79 6.1.1 Selection of Variables ...... 79 6.1.2 Definition of Test and Control Group and Data Collection Process ...... 83 6.1.3 Descriptive Statistics and Elimination of Outliers ...... 87 6.1.4 Research Design ...... 90 6.1.5 Result Discussion and Limitations ...... 92 6.2 Empirical Study: Significance of Direct Lending Industry ...... 95 6.2.1 Selection of Variables ...... 95 6.2.2 Descriptive Statistics ...... 97 6.2.3 Methodological Approach ...... 98 6.2.4 Result Discussion and Limitations ...... 99 7. Empirical Study: Corporate Finance in Times of Changing Key Interest Rates ...... 101 7.1 Selection of Variables ...... 101 7.2 Composition of Sample and Control Group...... 105 7.3 Descriptive Statistics and Elimination of Outliers ...... 109 7.4 Research Design ...... 111 7.5 Result Discussion and Limitations ...... 114

7.5.1 Impact of Key Interest Rates on Investment Activity (H2A) ...... 114

7.5.2 Impact of Key Interest Rates on Return on Assets (H2B) ...... 117

7.5.3 Impact of Key Interest Rates on Cash Holdings (H2C) ...... 120

7.5.4 Impact of Key Interest Rates on the Financial Indebtedness (H2D) ...... 123 V

8. Empirical Study: Zero Yield Bias ...... 126 8.1 Experimental Design ...... 126 8.2 Sample and Data Preparation ...... 130 8.3 Descriptive Statistics ...... 133 8.4 Methodological Approach ...... 135 8.5 Result Discussion and Limitations ...... 136 9. Interrelation Discussion ...... 139 9.1 Summary of Empirical Results ...... 139 9.2 Implications for the Direct Lending Industry ...... 141 10. Conclusion and Outlook ...... 144 Appendix ...... 145 Appendix A: Long-Term Development of Key Interest Rates and Inflation ...... 146 Appendix B: Impacts of Key Interest Rate Alterations on Inflation Rates ...... 147 Appendix C: Calculation of Gross Total Wealth Allocation in Germany ...... 150 Appendix D: Calculation of Gross Money Wealth Allocation in Germany ...... 151 Appendix E: Constituents of the Test Group ...... 152 Appendix F: Constituents of the Control Group ...... 153

Appendix G: Histogram and Density Plots of Transformed H1A Variables ...... 154

Appendix H: Histogram and Density Plots of Non-Transformed H1A Variables ...... 155

Appendix I: Annual Development of the Hypothesis H1A Model Variables ...... 156

Appendix J: Determination of Panel Model for the Test of Hypothesis H1A ...... 159

Appendix K: Regression Diagnostics Hypothesis H1A ...... 160

Appendix L: Information Criterion Comparison Hypothesis H1A ...... 161

Appendix M: Graphical Analysis of Actual and Fitted Values Hypothesis H1A...... 162 Appendix N: Constituents of the Test and the Control Group ...... 163

Appendix O: Histogram and Density Plots of Transformed H2 Variables ...... 167

Appendix P: Histogram and Density Plots of Non-Transformed H2 Variables ...... 168

Appendix Q: Annual Development of the Hypothesis H2 Model Variables ...... 170

Appendix R: Determination of Panel Models for the Test of Hypotheses H2 ...... 174

Appendix S: Regression Diagnostics Hypotheses H2 ...... 176

Appendix T: Information Criterion Comparison Hypotheses H2 ...... 180

Appendix U: Graphical Analysis of Actual and Fitted Values Hypotheses H2...... 182 Appendix V: Sociodemographic Determinants of Investment Behavior ...... 186 Bibliography ...... 187

List of Abbreviations €STR Euro Short-Term Rate AIC Akaike Information Criterion ACS Accounting Standard AE Administrative Expenses AERWA Administrative Expenses over Risk-Weighted Assets ALMM Additional Liquidity Monitoring Metrics BCE Before the Christian Era BG Bank Group BIC Bayesian Information Criterion BoJ Bank of Japan BST Balance Sheet Total

BSTYOY Year-Over-Year Development of Balance Sheet Total CAGR Compound Annual Growth Rate CAPM Capital Asset Pricing Model CASH_BST Cash and Cash Equivalents over Balance Sheet Total CDLI Cliffwater Direct Lending Index CFI_BST Cash Flow from Investing Activity over Balance Sheet Total CFO_R Cash Flow from Operating Activity over Revenue CIRWA Core Income over Risk-Weighted Assets COH Country of Headquarters CoRep Common Solvency Ratio Reporting COVID-19 Corona Virus Decease 2019 CRR Capital Requirements Regulation CRD Capital Requirements Directive DLL Direct Lending Loans DLL_LCSI Direct Lending Loans over Loans to Domestic Corporates and Self- Employed Individuals DLL_NTW Direct Lending Loans over Net Total Wealth EBIT Earnings Before Interest and Taxes EBIT_BST Earnings Before Interest and Taxes over Balance Sheet Total VII

EBIT_R Earnings Before Interest and Taxes over Revenue EBITDA Earnings Before Interest, Taxes, Depreciation, and Amortization EBT Earnings Before Taxes EAT Earnings After Taxes ECB European Central Bank EMIR European Market Infrastructure Regulation EMU Economic and Monetary Union ESCB European System of Central Banks EURIBOR Euro InterBank Offered Rate FA_BST Fixed Assets over Balance Sheet Total Fed Federal Reserve System FEM Fixed Effects Model FINREP Financial Reporting Fintech Financial Technology FRTB Fundamental Review of Trading Book GAAP Generally Accepted Accounting Principles gretl Gnu Regression, Econometrics and Time-series Library GS Geographical Scope HICP Harmonized Index of Consumer Prices HQIC Hannan-Quinn Information Criterion IAS International Accounting Standards ICAAP Internal Capital Adequacy Assessment Process IFRS International Financial Reporting Standards ILAAP Internal Liquidity Adequacy Assessment Process IRBA Internal Ratings-Based Approach IRR Internal Rate of Return IS-LM Investment-Saving / Liquidity Preference-Money KIR Period-Weighted Average Key Interest Rate LCR Liquidity Coverage Ratio LCSI Loans to Domestic Corporates and Self-Employed Individuals

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LIBOR London Interbank Offered Rate LLP Loan Loss Provisions LLPRWA Loan Loss Provisions over Risk-Weighted Assets LOG_BST Logarithm-Transformed Balance Sheet Total LOG_NOE Logarithm-Transformed Number of Employees LOG_R Logarithm-Transformed Revenue LOG_RWA Logarithm-Transformed Risk-Weighted Assets MaRisk Minimal Requirements for Risk Management MiFID Markets in Financial Instruments Directive MiFIR Markets in Financial Instruments Regulation MREL Minimum Requirement for Own Funds and Eligible Liabilities N/A Not Applicable NCI Net Commission Income NII Net Interest Income NOE Number of Employees

NOEYOY Year-Over-Year Development of Number of Employees NPV Net Present Value NSFR Net Stable Funding Ratio NTW Net Total Wealth OLS Ordinary Least Squares OpRisk Operational Risk OSIERWA Other Sources of Income or Expenses on Risk-Weighted Assets PRIIP Packaged Retail and Insurance-Based Investment Products PSD Payment Services Directive

RYOY Year-Over-Year Development of Revenues REM Random Effects Model RORWA Return on Risk-Weighted Assets

RORWAYOY Year-Over-Year Percentage-Point Alteration of Return on Risk- Weighted Assets RWA Risk-Weighted Assets

RWAYOY Year-Over-Year Development of Risk-Weighted Assets SEL Stock Exchange Listing SEPA Single Euro Payments Area SIF Specialized Investment Fund SREP Supervisory Review & Evaluation Process TARGET Trans-European Automated Real-Time Gross Settlement Express Transfer System TFL_BST Total Financial Liabilities over Balance Sheet Total TL_BST Total Liabilities over Balance Sheet Total TLAC Total Loss Absorbing Capacity VIF Variance Inflation Factor WACC Weighted Average Costs of Capital

List of Figures Figure 1: Structural Organization of the Dissertation ...... 3 Figure 2: Two-Pillar Approach and Its Interrelation with the Transmission Mechanism ...... 10 Figure 3: Development of Key Interest Rates and Inflation Since 1999 ...... 14 Figure 4: Intermediary Costs Moderate the Key and Bank Interest Rate Relationship ...... 36 Figure 5: Impact of Changing Risk-Free Rate on Indifference Curve and Return ...... 37 Figure 6: Regulatory Landscape in the German Banking Industry ...... 45 Figure 7: Three-Pillar System of the German Banking Industry ...... 49 Figure 8: Development of Net Interest Income of German Banks ...... 51 Figure 9: Fintech Start-Ups and Bankruptcies in Germany ...... 53 Figure 10: Market Share of Debt Funds and Banks in the German Mid-Cap Market ...... 55 Figure 11: Comparison of Direct Lending to Classical Financial Intermediation ...... 56 Figure 12: Asset Allocation Process with Typical Elements ...... 75 Figure 13: Overview of Wealth Allocation in Germany ...... 76 Figure 14: Quarterly Development of Market Shares in Germany ...... 98 Figure 15: Annual Distribution of Complete Data Sets ...... 108 Figure 16: Survey Question for Normal Interest Rate Environment ...... 128 Figure 17: Survey Question for Zero Interest Rate Environment ...... 129 Figure 18: Survey Question about Motives for Wealth Allocation ...... 130 Figure 19: Changes in Average Investment Behavior ...... 134 Figure 20: Primary and Secondary Motives for Asset Allocation ...... 134 Figure 21: Investment Motive Consistency ...... 135 Figure 22: Empirically Implied Investment Behavior along Capital Allocation Line ...... 138 Figure 23: Possible Causes of the Growing Direct Lending Industry in Germany ...... 143

List of Tables Table 1: Overview of Objective Systems of the Top 5 Central Banks by Total Assets ...... 9 Table 2: Monetary Policy Instruments of the ECB ...... 11 Table 3: Synopsis of Pre-Classical Interest Rate Assessment ...... 17 Table 4: Synopsis of Classical Interest Rate Assessment ...... 19 Table 5: Synopsis of Neoclassical Interest Rate Assessment ...... 24 Table 6: Synopsis of Heterodox Interest Rate Assessment ...... 29 Table 7: Efficient Coordination Mechanisms According to Transaction Cost Theory ...... 32 Table 8: Instruments of Behavioral Governance in the Principal Agent Theory ...... 34 Table 9: Hypotheses Conceptualization ...... 42 Table 10: Transformational and Transactional Functions of a Bank ...... 44 Table 11: Top 5 Fintech Funding Deals in Germany in 2018 ...... 54 Table 12: Refinement of Bank-Related Hypotheses ...... 59 Table 13: Volumes and Terms of the Syndicated Loan Market Germany 2001–2017 ...... 66 Table 14: Key Interest Rate Impacts on Assets in IFRS Accounting ...... 68 Table 15: Key Interest Rate Impacts on Liabilities in IFRS Accounting ...... 69 Table 16: IFRS Variables for Further Utilization in Empirical Research ...... 71 Table 17: Refinement of Corporation-Related Hypotheses ...... 73 Table 18: Refinement of Investor-Related Hypotheses ...... 78

Table 19: Definition and Description of Variables for the Test of H1A ...... 83 Table 20: Composition of the Test Group ...... 84 Table 21: Composition of the Control Group by Country ...... 87

Table 22: Descriptive Statistics of Numerical Variables for the Test of H1A ...... 89

Table 23: Descriptive Statistics of Categorical Variables for the Test of H1A ...... 89 Table 24: Results of Regression Analyzing Determinants of Bank Profitability Growth ...... 92

Table 25: Descriptive Statistics of Variables for the Test of H1B ...... 98

Table 26: Results of One-Sample 푡-Tests Analyzing Market Shares (H1B) ...... 99

Table 27: Definition and Description of Variables for the Test of H2 ...... 105 Table 28: Composition of the Sample Subjects ...... 107

Table 29: Descriptive Statistics of Numerical Variables for the Test of H2 ...... 110

Table 30: Results of Regression Analyzing Determinants of Investment Activity (H2A) ...... 114

Table 31: Results of Regression Analyzing Determinants of the Return on Assets (H2B) ..... 117

Table 32: Results of Regression Analyzing Determinants of Cash Holdings (H2C) ...... 120

Table 33: Results of Regression Analyzing Determinants of Financial Indebtedness (H2D) . 123

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Table 34: Utilized Average Returns per Year for the Defined Asset Classes ...... 127 Table 35: Overview of Survey Completion Ratios ...... 131 Table 36: Descriptive Statistics of Categorically Scaled Sociodemographic Variables ...... 131 Table 37: Descriptive Statistics of Metrically Scaled Sociodemographic Variables ...... 133

Table 38: Results of Paired-Sample 푡-Tests Analyzing Investment Behavior (H3A, H3B) ..... 136 Table 39: Summary of Empirical Results ...... 139

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List of Symbols 훼푒 Probability for an alpha error in statistical testing ̅̅̅̅ 푎̂푚 Mean of subject-specific intercepts 훽 Coefficient of idiosyncratic correlation with market portfolio

훽0 Regression intercept

훽푒 Probability for a beta error in statistical testing

훽푘 Regression coefficient 푘

푐푎푖푗 Allocated asset costs 푐푎 in bank 푖 and business area 푗

푐푓푖푗 Allocated costs of deposit protection 푐푓 in bank 푖 and business area 푗

푐푗 Costs of regulation of business area 푗

푐표푖푗 Allocated other costs 푐표 in bank 푖 and business area 푗

푐푝푖푗 Allocated payroll costs 푐푝 in bank 푖 and business area 푗

푐푟푖푗 Allocated risk costs 푐푟 in bank 푖 and business area 푗

퐶퐼95% Confidence interval 95%

∗ 퐶퐹푡∗ Cash flow in period 푡

퐷푀푉 Market value debt 푑푓 Degrees of freedom

퐸푀푉 Market value equity

퐸푛표푚 Nominal liable equity capital

퐸푟푎푡 Respective equity capital ratio in relation to risk-weighted assets 퐹 Test statistic based on Fisher distribution

푓2 Effect size measure by Cohen

퐻 Hausman’s specification test statistic

푖 Index of individual banks (푖 = 1, … , 퐼)

∗ 퐼0 Initial investment in 푡 = 0 푗 Index of specific business area (푗 = 1, … , 퐽)

푘 Index of determinants (푘 = 1, . . . , 퐾)

푙 Index of observations (푙 = 1, . . . , 퐿) 퐿푆퐷푉 푅² Least square dummy variable coefficient of determination

XIV

푚 Index of subjects (푚 = 1, … , 푀) 푀푎푥 Maximum value

푀푖푛 Minimum value

푛 Number of sample subjects

푛푃𝐴 Number of sample subjects from precise allocation subpopulation 휇 Mean value 푝 Probabilitas value

푃 Prices (푃 ≫ 0) 푟 Bravais-Pearson correlation coefficient

푟퐷 Costs of debt capital

푟퐸 Costs of equity capital

푟푓 Expected return of risk-free asset

푟푚 Expected return of the market 푅² Coefficient of determination

푅²푤푖푡ℎ푖푛 Within coefficient of determination 푠 Tax rate

휎 Standard deviation

푡 Test statistic based on Gosset’s 푡-distribution 푡∗ Index of periods (푡∗ = 1, … , 푇∗)

푇 Test value for test statistic based on Gosset’s 푡-distribution

푇퐶푀푉 Market value total capital

휃푖푗 Binary multiplier for (non-)existence of business area 푗 in bank 푖 푢(푋) Utility function

푢푙 Residual value of observation 푙

∗ 푢푚푡∗ Residual value of subject 푚 in period 푡 푊 Wealth level (푊 > 0)

푥푘푙 Observed value 푙 of determinant 푥푘

푥̅푘푚 Mean value of observed determinant 푥푘 of subject 푚

∗ 푥푘푚푡∗ Observed determinant 푥푘 of subject 푚 in period 푡

∗ 푥̃푘푚푡∗ Mean-corrected observed determinant 푥푘 of subject 푚 in period 푡 푋 Commodities

푦푙 Observed value 푙 of criterion 푦

푦̅푚 Mean value of observed criterion 푦 of subject 푚

∗ 푦푚푡∗ Observed criterion 푦 of subject 푚 in period 푡

∗ 푦̃푚푡∗ Mean-corrected observed criterion 푦 of subject 푚 in period 푡 푧 Test statistic based on Gaussian 푧-distribution

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List of Formulas Formula (1): Utility Maximization Problem ...... 47 Formula (2): Price Determination of Financial Services Provided by a Bank ...... 48 Formula (3): Net Present Value ...... 60 Formula (4): Weighted Average Costs of Capital ...... 61 Formula (5): Expected Return on Equity Capital (CAPM) ...... 62 Formula (6): Reverse Calculation of the Nominal Risk-Weighted Assets ...... 85

Formula (7): Model Equation Hypothesis Test H1A ...... 90

Formula (8): Model Equation Hypothesis Test H2 ...... 112

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1. Introduction 1.1 Key Interest Rates in the Euro Area as the Starting Point of a Problem Since its foundation in 1998, the European Central Bank (ECB) has almost continuously remained in a crisis-management mode. The burst of the dot-com bubble between 2000 and 2002, the Lehman Brothers default in 2008, and finally, the European debt crisis beginning in 2010 and still partially ongoing today all represented major incidents affecting ECB monetary policy (cf. e.g. Hartmann & Smets, 2018, 8). After executing different intervention measures, in March 2016, the ECB finally lowered the fixed tender rate to 0.0% and proclaimed today’s zero interest rate environment in Europe (European Central Bank, 2016). The concept of lowering key interest rates to stabilize an industrialized market—or, in this specific case, the struggling financial industry—is not new. As early as the mid-90s, the Bank of Japan (BoJ) intervened with this measure to guide the economy out of a prolonged recession following the burst of the asset-price bubble. However, rather than stopping the recession, this monetary policy proved detrimental, and the zero interest rate environment led to a liquidity trap (cf. e.g. Murota & Ono, 2012, 353-354; Okina, Shirakawa, & Shiratsuka, 2001, 408-414).

The environmental complexity impedes simply applying lessons learned from Japan to the European economy. Beyond the interdependencies between the 19 states utilizing the common currency, developments in globalization, digitalization, and knowledge over the last quarter century have created a different framework, and thus the possibility of a completely different effect of the zero interest rate environment. Either way, the first step toward an impact assessment would be to analyze which and how market participants are affected by the ECB’s monetary policy. Exploring recent medial and scientific discussions, the typical protagonists occurring in connection with the zero interest rate environment comprise banks, companies, and investors. Banks are being forced to manage the conflict between decreasing margins due to key interest rates alterations, on one hand, and the regulatory framework, new market participants, and changing demands on the other (cf. e.g. Waschbusch, Reinstädtler, & Ruffing, 2018, 916). Companies mostly benefit from decreasing interest rates, and therefore cheaper debt capital, but at the same time, they are also facing deposit charges for cash positions and increasing costs for pension provisions due to changing discount rates (cf. e.g. Dostert & Freiberger, 2017, 26). However, investors in particular are experiencing a new challenge. With the decrease of key interest rates, the allegedly risk-free investment opportunities (e.g. government bonds) also became yield-free. As such, a discussion regarding whether adjusting the asset allocation policy is necessary, and in which asset class the available funds should be allocated, became inevitable (cf. e.g. Lübbe, 2018, 445). 1

As demonstrated by those simple examples, each of those groups is affected differently by the monetary policy or rather the key interest rates. The interesting part, however, is their interrelation, which could have laid the foundation for a now rapidly growing direct lending market throughout Europe. The combination of yield-searching investors, banks that are unable to provide the necessary funds due to regulatory measures, and companies to whom the lender’s characteristics are irrelevant as long as there remains some pricing or structural advantage, subsequently led to increasing acceptance of direct lending products (Ares Management, 2018, 10; Deloitte, 2018, 29-38). This development applies not only to institutional investors and companies who interact through private debt funds, but also to private households who utilize new platform solutions to invest directly in private lenders or enterprises (e.g. auxmoney, Mintos). Shaking up the intermediary role of classical banks, this development could influence the entire economy.

Summarizing this introductory analysis, the necessity of academic research is evident. However, this raises the question of how to approach a topic that cannot be explored through history books or theoretical frameworks. This dissertation project provides an exploratory examination of bank, corporation, and investor behavior in a zero interest rate environment, with a focus on deriving possible explanations for the development of Germany’s direct lending industry.

1.2 Objectives of the Research Project Essentially, this dissertation project is subdivided into four objectives: First, current developments in Germany’s banking industry are analyzed to assess how intermediation costs affect bank business models in general and the significance of institutional direct lenders (i.e. private debt funds in leveraged transactions) as intermediation-light suppliers in particular. Second, this thesis investigates how key interest rate alterations influence corporate investment and funding strategies of listed companies in Germany and other industrialized markets. Third, investor behavior is examined in different interest rate environments to determine whether German investors are exposed to the zero yield bias, and thus seek out riskier asset classes to generate additional returns. Finally, the last sub-objective is to discuss the interrelations between the results and to propose possible explanations for Germany’s strongly growing direct lending sector.

The following three questions guide the first conceptualization of research hypotheses in Chapter 2. These conceptualized hypotheses are then refined in Chapters 3 through 5 and subsequently used to achieve the defined sub-objectives:

1. Are low intermediation costs in times of zero key interest rates beneficial for the business development of financial intermediaries in Germany? 2. How does the zero key interest rate environment influence financing and investment decisions of stock-listed companies in Germany? 3. Do investors change their behavior, and especially their risk tolerance, based on the current key interest rate level?

1.3 Course of Work To achieve the defined sub-objectives, this work is subdivided into 10 chapters, starting with a general theoretical analysis of the relevant topics and continuing into the necessary empirical studies to test the defined hypotheses. Figure 1 presents this work’s structural organization, including the respective chapters.

Figure 1: Structural Organization of the Dissertation

Source: Own representation.

The second chapter emphasizes the causes and implications of today’s zero interest rate environment. First, the ECB’s establishment history, as well as its role in the European economy throughout the last decades, is described. Second, focus is placed on the available instruments that can be utilized by a central bank in case of restrictive or expansive monetary policy. This subchapter also provides an overview regarding different monetary policies, their execution, and their success in reaching the monetary objectives. In the third subchapter, the lowering of key interest rates to zero is assessed from a theoretical perspective. The discussion aims at developing an understanding concerning how zero key interest rates should theoretically 3 influence banks, corporations, and investors. To establish a holistic overview, major paradigms starting in the pre-classical and leading into the neoclassical era are taken into account. The final subchapter presents a first conceptualization of the research hypotheses.

The third chapter focuses on refining the conceptualized bank-related hypotheses. First, the composition of intermediation costs and their mathematical implications for utilizing a financial service are discussed in detail. Based on the assumptions made, the third subchapter analyzes the competitive environment in the German banking industry. Identifying the rise of corporate direct lending in leveraged transactions as one of the major developments related to the intermediation-light trend, the fourth subchapter focuses its definition and relevance in a scientific and a practical sense. The fifth subchapter refines the conceptualized hypotheses into a researchable state.

The fourth chapter emphasizes the discussion of corporation-related hypotheses. The first subchapter presents an overview of financial investment decisions facing uncertain circumstances. Based on the following discussion of capital structure theory, debt is reviewed as a funding source in leveraged transactions. The third subchapter analyzes implications of the key interest rate alteration for accounting principles to mitigate a biased research set-up. According to the results, the fourth subchapter presents a selection of variables for further research, after which the research hypotheses are refined and supplemented in the fifth subchapter.

The fifth chapter connects the topics of asset allocation, portfolio theory, and investor behavior. After differentiating strategical and tactical asset allocation, the first subchapter presents an overview of primarily utilized asset classes in Germany. Based on the introduced concepts, the second subchapter intensifies the discussion of a possible investor-behavior bias. This discussion includes first empirical results on individual investment decisions as well as evidence from institutional investment behavior. Finally, the investor-related research hypotheses are supplemented by definitions in the third subchapter.

The sixth chapter comprises two empirical studies for testing the bank-related hypotheses. The first subchapter presents the conceptualization, implementation, and results of the study, which examines the determinants of bank profitability growth in times of zero key interest rates. The second subchapter examines the question of whether the direct lending industry already possesses significant market shares related to the investment and corporate credit markets in Germany.

The seventh chapter presents the results of the empirical study examining how changes in key interest rates influence financing and investment decisions of stock-listed companies in Germany and other industrialized markets. After deriving the methodological approach in the first four subchapters, the results are presented individually in relation to the respective research hypothesis.

The eighth chapter presents the fourth and concluding empirical study, which examines whether different key interest rate environments significantly influence investor behavior and whether investors tend toward riskier investments when key interest rates are low. The first three subchapters introduce the questionnaire design and the survey’s collected data. The following two subchapters present the inferential statistical approach and the results calculated herewith.

The ninth chapter focuses on the final sub-objective of this dissertation and presents an answer as to whether some of the empirical findings can be considered an explanation for Germany’s quickly growing direct lending sector. Initially, the first subchapter summarizes all empirical findings, after which these findings are assessed in relation to Germany’s direct lending industry in the second subchapter.

2. Theoretical Framework of a Zero Key Interest Rate This chapter is dedicated to deepening the theoretical understanding of today’s zero interest rate environment in the Eurozone. Following an overview of the ECB’s role in the European economy, the instruments that can be employed in case of expansive or restrictive monetary policy are analyzed in connection with recent European crises. The third subchapter focuses on theoretical consequences of decreasing key interest rates to zero, continuing into a conceptualization of research hypotheses.

2.1 Role of the European Central Bank in the European Economy To approach the concept of key interest rates, the first step involves analyzing the institution that possesses the ability to exercise their modification.

The ECB’s current institutional framework comprises the result of long-term development. Putting aside the discussions regarding the European economic community, which began shortly after the Second World War (e.g. European Coal and Steel Community), the first debates concerning a common European currency can be traced back to 1962 (European Economic Community Commission, 1962, 63–67). The first European institution focusing on improved cooperation between central banks and harmonizing monetary policies of member countries was the Committee of Governors, founded in 1964 based on the treaty establishing the European Economic Community (European Economic Community, 1957, §105; 1964, 1; Holtrop, 1964, 1). These cornerstones provided the foundation for the following attempts to implement a currency union in Europe. However, neither the Werner Report, which proposed a three-step plan to achieve an economic and monetary union within a decade (Committee of Experts, 1970, 26), nor the Snake-in-the-Tunnel mechanism, which aimed at limiting fluctuations between European currencies and creating a single currency band in the long term (Council of the European Communities, 1972, 65–66), were successful. Their dependence on the U.S. dollar and its convertibility into gold revealed impassable system weaknesses (Delivorias, 2015, 2–3). Learning from those attempts, in 1979, the European Monetary System was established, referencing different European currencies against a calculation-based European Currency Unit (European Council, 1978, 1). The signing of the Single European Act, which extended authorities for implementing a common currency (European Economic Community, 1987, 8–9), as well as the Delors Report, which concluded that a monetary union could be realized in three stages (Committee for the Study of Economic and Monetary Union, 1989, 34–40), supported the following implementation of the Economic and Monetary Union (EMU) in Europe.

Over the course of executing the three stages, the Committee of Governors’ role changed extensively. In the first stage, the committee received additional responsibilities, such as consulting on member states’ monetary policies or promoting their coordination, seeking to achieve monetary stability (Council of the European Communities, 1990, 25–26). Based on the signing of the Maastricht Treaty in 1992, the committee also had to prepare its supersedure by the European Monetary Institute (Committee of Governors of the Central Banks of the European Economic Community, 1993, 56–57). The institute’s foundation in January 1994 marked the beginning of the second stage of the EMU and comprised a preparatory step for establishing the European System of Central Banks (ESCB) (European Monetary Institute, 1995, 63). Although national central banks had to achieve political independence and were ordered not to grant credit facilities to governmental borrowers, they remained able to autonomously decide on monetary policies in stage two (Council and Commission of the European Communities, 1992, §104, §107, §109f). On 1 June 1998, the European Monetary Institute was dissolved and superseded by the ESCB and the ECB. With the start of the third stage, and the introduction of the euro to the 11 initial member states on 1 January 1999, the ECB also took over responsibility for their monetary policy (Scheller, 2006, 23–30).

The Maastricht Treaty stipulated that the new supranational institution, which today is responsible for the monetary policy of 19 European states in total, should implement two decision-making bodies (Council - Commission of the European Communities, 1992, §109a). The Executive Board consists of the ECB’s president, vice-president, and four other members. It is responsible for implementing the monetary policy decisions made by the Governing Council and the ECB’s day-to-day business. The Governing Council itself consists of the six members of the Executive Board and the governors of the 19 national central banks. It possesses the power to formulate the Community’s monetary policy, including objectives, key interest rates, and the supply of reserves, thus comprising the main decision-making body. Decisions regarding monetary policy are made with a simple majority. The voting rights are held by the six members of the Executive Board and 15 governors who are selected by a rotation and group- affiliation mechanism. This mechanism differentiates two groups determined by the size of the economy and the financial sector. While the first five economies in the ranking share four voting rights, the other 14 share the remaining 11 (European Central Bank, 2011a, 10–13). As an additional committee of the ECB, the General Council pursues transitional and advisory tasks involving the remaining nine non-Eurozone countries of the European Union (European Central Bank, 2011a, 25–26).

An important cornerstone of the ECB’s decision-making process consists of the five principles of independence. Institutional independence states that the decision-making bodies should, in general, not be influenced by any governmental or other organization (European Central Bank, 2011a, 9–10). Furthermore, the Executive Board members are appointed for a non-renewable term of eight years and can only be suspended in case of serious misbehavior so as to ensure personal independence and objective decisions. Functional independence guarantees that the ECB possesses all necessary and exclusive competencies to achieve its primary objective. The fourth principle is financial independence. The ECB and the national central banks are allowed to possess their own financial resources and income to autonomously pursue their tasks (Görgens, Ruckriegel, & Seitz, 2014, 94). Finally, the fifth principle concerns legal independence, which is linked to the ECB’s own legal entity and the therefrom derived ability to enforce its rights before the European Court of Justice (Weber & Forschner, 2014, 48).

Two findings of this analysis in particular should be pointed out. First, the ECB bears full responsibility for the monetary policy of the Eurozone states, and is therefore an institution that substantially influences economic developments. Second, the ECB operates independent of any political influence, which—in combination with the euro as common currency—is a result of long-term structural, political, and economical changes in Europe.

2.2 Objective Systems and Options for Action of Central Banks Altering key interest rates comprises an operative measure for achieving defined monetary policy objectives. Analyzing those objectives in detail should offer insight into the usability of different instruments in specific economic environments, as well as their efficiency.

According to the Treaty on the Functioning of the European Union, the ESCB’s primary goal involves maintaining price stability (European Union, 2016b, 102). The detailed specification states that price stability is sustained if the Harmonized Index of Consumer Prices (HICP) increases on a year-on-year basis below, but close to, 2% (European Central Bank, 2003). With its differently weighted prices for goods and services, the HICP provides a close approximation to the inflation rate in the Eurozone (Eurostat, 2018, 16–20). Thus, the ESCB’s goal definition proposes moderate price growth and rules out deflationary developments. The secondary goal concerns supporting the achievement of the European Union’s general economic objectives, as laid down in the Treaty of the European Union (European Union, 2016a, 17). However, the secondary goal should not interfere with realizing price stability. Table 1 below illustrates the ESCB’s objectives compared to the other top five central banks worldwide, according to their total assets (Financial Stability Board, 2019).

Table 1: Overview of Objective Systems of the Top 5 Central Banks by Total Assets Geographical Primary Area Secondary Objectives Source Objective (Central Bank) Achieving inflation Maintaining and improving national Brazil targets set by the financial systems, legal frameworks, Banco Central do (Banco Central do National Monetary compliance, governance, and the Brasil (2019) Brazil) Council relations of the central bank. Maintaining a stable level of liquidity, Monetary Policy China improving the two-pillar regulatory Analysis Group of Sustainable (The People’s framework and the financial sector’s The People’s Bank economic growth Bank of China) stability, and introducing market-based of China (2019, 81– interest and exchange rates. 89) Euro Area Supporting economic objectives of the European Union, (European Central Price stability European Union (e.g. growth, full (2016b, 102–103) Bank) employment, financial system stability). Japan Ministry of Justice Price stability Financial system stability. (Bank of Japan) (2009, §§ 1–2) Achieving Stabilization, supervision, and maximum Board of Governors United States improvement of the financial system, employment, stable of the Federal (Federal Reserve efficiency improvement of payment prices, and Reserve System System) systems, and promotion of consumer moderate long-term (2016, 23–26) protection and community development. interest rates Source: Own representation.

The detailed analysis of the primary objectives demonstrates that the ECB, the BoJ, and the Federal Reserve System (Fed) published comparable quantifications of price stability, aiming at an inflation of approximately 2% per year (Bank of Japan, 2013, 1; Board of Governors of the Federal Reserve System, 2016, 24; European Central Bank, 2003). Although the particular wordings differ, the objective systems of those three central banks are also generally comparable, focusing on economic growth, financial system stability, and maximum employment. The Brazilian central bank, on the contrary, features a different set-up and primarily responds to varying inflation targets set by the National Monetary Council. The Chinese central banks’ primary objective concerns sustainable economic growth. Although it seems to feature a different approach, the ECB’s monetary policy, with its focus on price stability, does not ignore real economic developments. Rather, the policy specification aims at a different level of the transmission mechanism, taking both monetary and economic development into account.

Figure 2 below offers an overview regarding the transmission process and its interrelation with the ECB’s monetary policy decisions. The ECB’s Governing Council’s decision-making process is based on a two-pillar risk analysis approach. The first pillar aims at assessing short- to mid-term real economic developments that could influence prices. Variables for this

9 assessment could include, for example, capital and labor market conditions, exchange rate developments, overall output, aggregate demand, or global economy developments (European Central Bank, 2011b, 69–71). The second pillar focuses on mid- to long-term analysis of the link between monetary growth and inflation. An additional component for this assessment concerns, for example, analyzing bank balance sheet developments (European Central Bank, 2011b, 80–81). Based on the two-pillar assessment and cross-checking between the results, the Governing Council can execute its monetary policy decisions that trigger the transmission mechanism. Put simply, any operationalization of a monetary policy decision should influence market expectations and money market interest rates. Their alterations in turn affect exchange rates, asset prices, bank interest rates, and credit and money volumes, leading to changes in supply and demand of goods and labor markets. Finally, those changes should impact wages and domestic prices, which, combined with the import price development, lead to the overall market price development—inflation. However, due to the complexity of economic interactions, exogenous shocks that can influence these interactions, and the time lag between a decision and the actual impact on prices, the ECB’s monetary policy implementation is confronted with a substantial level of uncertainty (European Central Bank, 2011b, 58–62).

Figure 2: Two-Pillar Approach and Its Interrelation with the Transmission Mechanism

Source: Own representation based on European Central Bank (2011b, 59, 83).

2.2.1 Instruments of an Expansive or Restrictive Monetary Policy To execute its supranational monetary policy and trigger the transmission mechanism, the ECB requires effective operative measures. Table 2 below presents an overview of the available instruments that can be used to implement an expansive or restrictive monetary environment in the Eurozone.

Table 2: Monetary Policy Instruments of the ECB Monetary Policy Description Maturity Frequency Procedure Operation Open Market Operations Main Liquidity-providing reverse Standard refinancing One week Weekly transactions tenders operations

Generally three Longer-term months, but also Liquidity-providing reverse Standard refinancing up to 48 months Monthly transactions tenders operations in non-regular transactions

Liquidity-providing and liquidity- Quick absorbing transactions to smooth Fine-tuning Non- tenders / liquidity fluctuations (i.e. reverse Non-regular operations standardized bilateral transactions, foreign exchange swaps, procedures collection of fixed-term deposits) Liquidity-providing and liquidity- absorbing transactions to adjust the Standard Standardized / Structural structural position of the financial Regular / tenders / non- operations sector (i.e. reverse transactions, non-regular bilateral standardized outright transactions, issuance of debt procedures certificates) Standing Facilities Marginal Permanent liquidity-providing facility Overnight lending facility Access at the discretion of counterparties Deposit facility Permanent liquidity-absorbing facility Overnight Minimum Reserves Calculation of necessary Credit institutions are obliged to hold deposits on an average daily Minimum deposits on accounts of their national Perpetual reserve holdings basis over a reserves central bank maintenance period of about one month Non-Standard Monetary Policy Measures Secondary market purchases of Since December 2018, the program is in its covered bonds, asset-backed securities, Asset purchase refinancing phase that allows full reinvestments and public and corporate sector bonds program of maturing securities purchased under the asset to achieve the primary objective of purchase program price stability Source: Own representation based on European Central Bank (2019a, 2019d).

The open market operations and the standing facilities can be utilized by any counterparty who is obliged to the mechanism of minimum reserves (European Central Bank, 2011b, 96). The 11 partially tender-based liquidity-providing and liquidity-absorbing instruments offer different maturities to meet market participants’ liquidity requirements. Through regulating those instruments’ corresponding interest rates, the ECB can trigger the transmission mechanism. In September 2019, the Governing Council decided to keep the interest rate at 0.00% for the main refinancing operation, 0.25% for the marginal lending facility, and to decrease the interest rate to –0.50% for the deposit facility, representing an aggravation of the ECB’s zero interest rate policy (European Central Bank, 2019c). As an additional instrument, the minimum reserves present the opportunity to stabilize money market interest rates (European Central Bank, 2011b, 101). Furthermore, in 2009, through the asset purchase program, the ECB introduced a non- standard monetary policy measure to stabilize markets (European Central Bank, 2009, 18–19). Ten years later, in 2019, four facilities of this program had entered a reinvestment phase, three facilities were terminated and await the maturity of their holdings, and one facility newly began its purchasing phase in November (European Central Bank, 2019a, 2019c).

A simple example can be used to demonstrate the link between the presented instruments and the transmission mechanism from the previous subchapter. Specifically, if the ECB decides to lower the key interest rates for the described instruments, the money market interest rates should also decline. This decision could also influence long-term interest rates if the market expectations of the economic development generally change based on the ECB’s alterations. Lower market interest rates should incentivize consumption and investment decisions of private households and companies, since loans to finance those expenses become cheaper and savings less attractive. Increasing demand should, ceteris paribus, produce an upward impact on market prices. Thus, lowering key interest rates should lead to increasing the inflation rate, and vice versa (for a higher degree of detail cf. European Central Bank, 2011b, 58–62).

The European Monetary Transmission Network, which included researchers from the ECB, the national central banks, and different universities, performed numerous empirical studies examining how monetary policy instruments influence the transmission mechanism in the Eurozone (Angeloni, Kashyap, & Mojon, 2003, 1–11). Summarizing their conclusion, monetary policy’s interest rate channel holds a prominent and, in some countries, nearly exclusive role for the transmission mechanism. It was also stated that the financial sector’s credit supply to private households and firms is important, but that the banks who provide those means remain, to some extent, subordinated to the process itself (Angeloni, Kashyap, Mojon, & Terlizzese, 2003, 410–412). However, since the utilized data originated from the time before the common currency’s implementation, validating those results proved mandatory. As the data

12 set grew over time, more researchers were able to review those findings (cf. e.g. Alves, Brandão de Brito, Gomes, & Sousa, 2011, 922; Angeloni & Ehrmann, 2003, 491–492; Ciccarelli, Maddaloni, & Peydró, 2013, 29) and even assess the impact of the newly introduced asset purchase programs (cf. e.g. Gern, Jannsen, Kooths, & Wolters, 2015, 212; Salachas, Laopodis, & Kouretas, 2018, 6496–6498). The results demonstrated that the supranational execution of monetary policy led to a more homogeneous transmission development in the respective economies (Angeloni & Ehrmann, 2003, 492). The unconventional instruments contributed to the financial markets’ stability and helped mitigate a recession scenario. Although their impact on the transmission mechanism was smaller than expected, most authors agreed that their implementation following the financial crisis was necessary for market stabilization (cf. e.g. Salachas et al., 2018, 6498).

The empirical research and the respective operationalization of studies to this topic have been coined by an extensive granularity (Görgens et al., 2014, 310). The important conclusion for further proceeding concerns how altering key interest rates represents the ECB’s most important measure for reaching its goal of price stability (Görgens et al., 2014, 346). However, no direct observable relationship was found between key interest rates and inflation, as presented in the following subchapter; rather, numerous interrelated variables were found to influence the transmission mechanism.

2.2.2 Crisis Management by the European Central Bank Different European and global crises forced the ECB to implement countermeasures that subsequently led to the zero interest rate environment in the euro area. By linking the key interest rates to inflation, it is possible to superficially approach the motives behind the ECB’s measures. Figure 3 below presents the key interest rate as well as the inflation rate development in Europe since 1 January 1999. It also includes the Japanese and US-American developments due to their extensive similarities in central bank policy (for more details cf. Chapter 2.2) and the comparable crises responses in the recent decades.

Following the burst of the dot-com bubble and the financial crisis, the ECB and the Fed significantly lowered their key interest rates to stabilize markets. The BoJ executed comparable measures, but due to the already low interest rate environment in Japan, the key interest rate reduction remained rather moderate. The ECB also reduced key interest rates during the European sovereign debt crisis and implemented the asset purchase program in this context (cf. e.g. Hartmann & Smets, 2018, 76). In contrast to the ECB and the BoJ, the Fed managed to leave the low interest rate environment in 2016.

Figure 3: Development of Key Interest Rates and Inflation Since 1999

Source: Own representation based on data retrieved from Thomson Reuters (Key Interest Rate Eurozone = Main Refinancing Facility Rate; Key Interest Rate Japan = Basic Discount & Loan Rate; Key Interest Rate USA = Federal Funds Target Rate; Inflation Eurozone = HICP All Items; Inflation Japan = Consumer Price Index All Items; Inflation USA = Consumer Price Index All Items). However, the link between key interest rates and inflation is not as obvious as implied in the transmission mechanism specification. The figure demonstrates that a key interest rate increase does not necessarily lead to a decrease in inflation (e.g. ECB between 2005 and 2008), and vice versa (e.g. ECB between 2001 and 2003). As presented in Appendix A, it is also challenging to identify such a relationship or correlation between those variables when examining a longer period of history. Even calculating the impact of key interest changes on a 1-, 6-, or 12-month inflation rate alteration does not provide clear results. As illustrated in Appendix B, both the sample since 1971 and the sample since 1999 present trend lines with a positive slope, which would indicate that increasing key interest rates leads to higher inflation compared to a decrease scenario. However, this development is—in a stand-alone consideration—contradictory to the intended impact on prices.

This exemplification emphasizes that the transmission mechanism is a complex interrelated system. Focusing solely on how key interest rates affect inflation would neglect not only the numerous phases in between, but also the exogenous circumstances that could be significant determinants of price developments by themselves (cf. e.g. Bindseil, 2014, 294–298). Taking

14 these interrelations into account, this work focuses on how the zero interest rate environment affects a specific financial topic so as to ensure deep empirical insight rather than the described bird’s-eye perspective. Nevertheless, such a top-down approach is at least necessary to discuss which impact the zero key interest rates should have from a theoretical point of view.

2.3 Zero Interest Rates from a Theoretical Point of View The concept of the transmission mechanism and its interrelation with key interest rates is one thing, while the key interest rates actually reaching zero is another. This environment presents a novelty for the European community. Accordingly, there are neither history books nor a comprehensive empirical database to resort to when specific cause-and-effect derivations are required. The preceding analysis of economic paradigms and their assessment of the impact of such an interest rate environment is thus essential for the process of developing research hypotheses. Due to the granularity of economic schools and approaches, however, the following analysis cannot claim to be exhaustive, nor can it cover whether the theories are right or wrong. Rather, it presents major economic thoughts throughout different eras, focusing particularly on separating implications for banks, corporations, and investors. The analysis results provide the framework for the research project’s structure.

2.3.1 Pre-Classical Schools of Economic Thought The exact origins of interest-bearing debt, as well as the cause of specific interest rate levels, are buried in ancient history of mankind. Historical documents prove that interest payments on loans go back to at least the Mesopotamian empire, approximately 2500 BCE (Hudson, 2000, 132–135). The Code of Hammurabi is included in Table 3 below as an example from the Babylonian empire. Even though its identification as an ancient economic school has its limitations, the document presents one of the first codifications of a maximum interest rate level at 33.3% (Hammurabi, trans. 1915, 13, 15; Hudson, 2000, 158). Several centuries later, Aristotle (trans. 1853, Book I, Chapters IX–X) discussed money’s primary functions and differentiated between natural and unnatural applications. Its use for barter and to satisfy essential needs was considered natural, while using money to create more money and wealth through interest payments was contrary to nature. As the zero interest rate environment does not abolish interest payments on loans, Aristotle’s desired concept of natural wealth provides only limited possibilities for application in today’s world.

In the Middle Ages, Scholasticism represented one of the most important schools of economic thought. Taking into account the church’s societal importance in these ages, economic theories and thoughts were, in general, affected by theology (Söllner, 2015, 5–6). For example, Aquinas

(trans. 1947, Question 78) recognized interest rates in his Summa Theologica as a subject of sin that arises from the usage of money instead of its consumption. This approach is comparable to Aristotle’s unnatural application, but also presents several exceptions for the lender (e.g. capital contribution into a business undertaking). Although discussing the rightfulness of prices and the necessity of monetary value’s stability, Aquinas (trans. 1947, Question 77) did not offer thoughts on causes and consequences of their changes. This topic was addressed by Oresme (trans. 1956, Chapters VIII–XVII), who not only presented one of the first dedicated works about money, but also introduced and criticized the concept of weight-induced inflation. Despite his acceptance of a moderate seignorage gain for minting and other services provided by the government, other gains from money (e.g. interests) remained at least contemptible (Oresme, trans. 1956, Chapters XVII & XXIII). Summing up, the Scholastics contributed important elements for developing economic theory, but based on these thoughts, it remains impossible to derive implications for corporate finance or investments given the current interest rate environment.

Between approximately the 16th and 18th centuries, Mercantilism was the dominant school of economic thought. This was characterized by a broad variety of theories coming from different authors and being influenced by the challenges in their respective countries (Söllner, 2015, 9– 15). However, those theories also shared some common features, primarily focusing on a surplus of the balance of payments, as well as the increase of the stock of precious metals to ensure a country’s wealth (cf. e.g. Mun, 1664, 5, 21). In this context, demonizing the pursuit of profit and trade was abolished. These topics not only became honorable objectives, but mercantilists demanded protectionist measures by the government to support their achievement, even if it meant military intervention (cf. e.g. Viner, 1948, 2–8). One of the proposed measures to support economic activity concerned managing interest rates (cf. e.g. Grampp, 1952, 473). Law (1720, 17), for example, explained the relationship between low interest rates and high economic activity with the possibility to obtain cheaper loans and thus to increase stocks for trade. However, the topic of whether interest rate determined quantity of money through increasing wealth or if quantity of money determined interest rate through a higher supply could not be resolved in consensus (Grampp, 1952, 478–480). Cantillon (trans. 2010, 177–181) even argued that no obvious relationship exists between those variables and that they are heavily influenced by the supply and demand of loanable funds. He also discussed the topic of interest rate regulation by the state and proposed that restricting interest rates only makes sense in the highest market level or above so as to ensure frictionless continuance of lending and borrowing in all interest rate levels below (Cantillon, trans. 2010, 180). 16

In summary, Mercantilism offers two important ideas for assessing the zero interest rate environment. First, it provides the view that lower interest rates benefit corporate lending, corporate investment, and subsequently company profits. Second, it offers an idea that can be connected with the transmission mechanism’s complexity, stating that no direct relationship exists between market interest rates and entrepreneurial actions. Rather, these actions are influenced by a variety of factors, such as political stability, expenditures, or trade balance. Table 3 below presents an overview regarding interest rate assessment by pre-classical schools of economic thought.

Although it would be possible to provide more insight from other pre-classical schools of economic thought, the added value would be limited. For example, Physiocracy aimed at regulating interest rates to strengthen the agricultural sector (cf. e.g. Samuels, 1961, 103). As one of its representatives, Turgot (trans. 1859, 292–294) discussed this topic and introduced the time preference of money as a justification of interest taking in general. However, no theory provides specific implications for financing or investment strategies that go further than the mercantilist approach. Therefore, the next analysis focuses on classical economic theory.

Table 3: Synopsis of Pre-Classical Interest Rate Assessment Scholar (Zero) Interest Rate Assessment Source Ancient Schools  No cause-and-effect derivation possible. Hammurabi (trans. Hammurabi  First codification of a maximum interest rate level. 1915, 13, 15)  No cause-and-effect derivation possible. Aristotle (trans. 1853, Aristotle  Interest gains are recognized as money created from money, which is Book I, Chapter X) contrary to nature and should be rejected. Scholasticism  No cause-and-effect derivation possible. Thomas  Money is recognized as a good for consumption and not for Aquinas (trans. 1947, Aquinas continuous usage. Thus, interest gains are—with several exceptions— Question 78) seen as sins before God.  No cause-and-effect derivation possible. Nicole Oresme (trans. 1956,  Profits from money are at least contemptible and only acceptable in Oresme Chapter XVII) specific occasions (i.e. as a compensation for services). Mercantilism  Cause-and-effect: Lower interest rates lead to increasing liabilities, investments, and thus higher profits. John Law Law (1720, 17)  Higher economic activity is possible through cheaper loans and accordingly increasing stocks for trade.  Cause-and-effect: Lower interest rates lead to price inflation. Due to the variety of determinants, lower interest rates exert no obvious Richard Cantillon (trans. 2010 influence on corporate investment. Cantillon 177-181)  Supply and demand of loanable funds determine the interest rate.  Lower interest rates should, in most cases, lead to price increases. Source: Own representation.

2.3.2 Classical Schools of Economic Thought In 1776, Adam Smith marked the foundation of the Classical School by publishing his five books inquiring about the Wealth of Nations. Especially with David Ricardo’s later contributions, classical theory succeeded in dominating economic thinking for roughly a century afterwards (Söllner, 2015, 21). Smith (1776a, 1776b) discussed a variety of topics, such as the division of labor, the benefits of free trade, or the principle of the invisible hand. Furthermore, despite occasionally being criticized in later centuries for their lack of new principles (cf. e.g. Schumpeter, 1954, 179), they remain one of the most-cited works in economic theory today. Focusing on interest rates, several aspects can be projected on the zero interest rate environment.

First, Smith (1776a, 434–436) opposed government-induced lowering of interest rates. From his perspective, such a measure would intensify the search for alternative investment opportunities to compensate for the missing capital revenues. His example focused on rising land prices as a consequence of low interest rates, which can be identified as current problem in today’s Germany (cf. e.g. Haimann, 2019, 26).

Second, Smith (1776a, 426) described loans as an opportunity to invest more in capital, which then provides the necessary loan repayments as well as an additional profit. Thus, lowering interest rates should lead to increased investment activity, since the hurdle rates for successful investment also decrease, and the incentive for saving money becomes less attractive.

Another interesting aspect of Smith’s theory concerns the topic of hoarding money. For Smith (1776a, 410–411), hoarding was not an actual issue of an economy, since he equaled savings with investments of other market participants. However, examining today’s regulatory framework for banks and circumstances where people earn money without spending it, this hypothesis becomes highly questionable, and is thus discussed further in Chapter 2.3.4.

Having discussed Smith’s assessment of the interest rate, Ricardo (1817, 511–515) contributed only small additional input on this topic. From his perspective, interest rates can and should differ only in the short-term from the profit rate of investment, and they should return to a natural rate in the long-term. As such, he also rejected a government-induced interest rate policy for the benefit of market flexibility. Following his ideas, the cause-and-effect relationships for financing and investment decisions are generally comparable to Smith’s explanations.

Although Marxism has generally been classified as a rivalling economic school during the classical era (Söllner, 2015, 217), the approach to explaining interest rate is not completely

18 contrary to other, already discussed concepts. Marx (1894, 350–403) considered interest rate as an element of an enterprise’s profit rate, which is subject to the distribution process between money and functioning (i.e. entrepreneurial) capitalists. Therefore, lower interest rates would, ceteris paribus, increase a functioning capitalist’s return and thereby additionally incentivize an increase in investment activity. The interest rate itself comprises an exogenous variable determined by political, organizational, and historical circumstances and which possesses a long-term upper limit based on the investment’s maximum profit rate. Marx (1894, 378–379) also indirectly argued that decreasing interest rates should benefit real investments, since money or functioning capitalists switch their investment sphere to compensate for missing profits in their genuine asset class if they are able to generate greater profits in the other. This cause-and- effect relationship corresponds with Smith’s example of increasing investments in land when interest rates are decreasing.

The classical era was not exempt from a granularity of contributions by different authors and schools. Regarding the topic of interest rates, however, only certain ideas differ from the above- mentioned schools and are worth pointing out separately (von Böhm-Bawerk, 1921a, 70). Senior (1836, 184), for instance, introduced the idea that the interest on capital represents a compensation for the abstinence of its consumption. Projecting this thought on the zero interest rate environment, consumption should, in general, increase, since the compensation for abstinence remains at a low level. However, since the conclusions from those contributions still lead in the same direction and do not focus the topic of concern, their detailed discussion is omitted. Table 4 below summarizes the interest rate assessment by the two major classical schools of economic thought.

Table 4: Synopsis of Classical Interest Rate Assessment Scholar (Zero) Interest Rate Assessment Source Classical School  Cause-and-effect: Lower interest rates lead to an intensified search for yield. Adam  Cause-and-effect: Lower interest rates lead to higher investment Smith (1776a, 410– Smith activity. 411, 426, 434–436)  There is no hoarding in an economy; instead, savings lead to investments of other market participants. Marxism  Cause-and-effect: Lower interest rates lead to higher profits of functioning capitalists, and thus should benefit additional investment Karl Marx activity. Marx (1894, 350–403)  Indirect cause-and-effect: Lower interest rates could result in switching monetary into real investments. Source: Own representation.

2.3.3 Neoclassical Schools of Economic Thought Today, the Neoclassical School represents the mainstream of economic thought, which emerged at the end of the 19th century (Söllner, 2015, 41–45). Gossen’s (1854, 4–12) laws of diminishing marginal utility and expenditure allocation based on marginal utility of goods were, although not immediately recognized as such, important cornerstones of neoclassical economics. Today, after countless important contributions by other scientists, the theoretical principles of the Neoclassical School are based on three major pillars: The first comprises the principle of function optimization under certain conditions, including marginal value analysis and the assumption of rational behavior. Second, the principle of an equilibrium that can be achieved on micro- and macroeconomic levels. Finally, the principle of a methodological individualism that serves to explain social phenomena (Söllner, 2015, 43–45). However, despite the consensus on numerous topics, interest rate assessment remains characterized by a variety of approaches; the primary approaches are analyzed hereafter.

Walras (trans. 2014, 288–289, 366), despite providing a completely different theoretical framework, shared a few of Smith’s and Marx’s ideas regarding interest rate and the capital profit trade-off situation. From his perspective, rational market participants would shift their capital allocation between savings and capital investments based on which asset class provides the higher returns. Projecting this idea on the zero interest rate environment, market participants should increase their investment activity into real assets instead of saving money if the expected return from investment is greater than the achievable interest rate on savings. Walras (trans. 2014, 382–384) also described determining interest rates through an equilibrium mechanism. Herein, the interest rate is dependent on the market’s monetary supply and demand, and thus represents its equilibrium price. With the interest rate being a dependent rather than determining variable, assessing influences on financing and investment decisions is not feasible, since the ECB not only increased the monetary supply to decrease market interest rates, but also actively changed key interest rates. One possible conclusion regarding the attractiveness of the ECB- implied equilibrium interest rates would still lead to the first assessment that market participants have to choose between investments and savings based on the new return situation.

One of the first systematic developments of an actual interest rate theory was provided by Eugen von Böhm-Bawerk. Böhm-Bawerk’s (1921a) first treatise focused on the detailed confrontation and on, more or less, the dismantling of existing theoretical assessments of capital and interest rates. This work provided the necessary foundation for presenting his own theory in his second treatise. Herein, Böhm-Bawerk (1921b) derived the detailed relationship between interest rates and the real economy. 20

First, he described the value difference between present and future goods, stating that the value of future goods should, in most cases, be lower due to intertemporal uncertainty and the accordingly necessary risk premium. Furthermore, future needs are usually underestimated compared to today’s needs, which also negatively influences the future value estimation of goods (von Böhm-Bawerk, 1921b, 324, 327, 332). Taking these considerations into account, loans should, from his perspective, undeniably provide interests for the creditor to compensate for the lower future value estimation of the repayment (von Böhm-Bawerk, 1921b, 363). Böhm- Bawerk (1921b, 413, 423) also discussed the present value of future surpluses and the terminal value of an investment, which today form the basic concepts for the discounted cash flow method in corporate evaluation (cf. e.g. Damodaran, 2012, 11, 304). Finally, he presented a total of seven determinants influencing the interest rate. The major determinants include the size of the national fund reserves, the number of producers who should be supplied from those reserves, and the structure of returns from investments (von Böhm-Bawerk, 1921b, 472).

Since the explanation of the interest rate level is based on market participants’ behavior, governmental-induced key interest rate alterations to zero are nearly incompatible with Böhm- Bawerk’s theory. This incompatibility, however, is not derived from missing interpretation opportunities. Instead, those interpretations contradict other statements in Böhm-Bawerk’s complex theory. For example, a zero interest rate could lead to excess demand for borrowing to satisfy present needs. Simultaneously, capitalists should cease lending money on the market, since the compensation for a lower future value would be equal to zero. Ceteris paribus, both situations should lead to increasing market interest rate equilibrium, which, however, is influenced by the central bank in reality. Due to these contradictions, and despite the scope of Böhm-Bawerk’s theory, no cause-and-effect relationship can be clearly stated for the zero interest rate environment.

Böhm-Bawerk’s second treatise was controversially discussed in scientific literature afterwards. Clark (1895, 277–278), for example, strictly rejected the idea of time preferences as an explanation for interest rates. Instead, he postulated capital itself as the sole source of returns and that a perpetual social fund of capital permanently creates the returns, which are subsequently distributed between different stakeholders, including lenders. The increase of specific capital in the perpetual fund should, from his point of view, lead to a decreasing rate of returns, and vice versa. As soon as the returns provide no more marginal utility, such as would be the case when the loan interest rates exceed the rate of returns from capital, the entrepreneur would change the investment behavior (Clark, 1908, 125, 185–187). Although the

21 level of detail and clarity of terminology can hardly compete with Böhm-Bawerk’s treatise, Clark’s theory at least provides a clear implication for the zero interest rate environment. Following his theory, lower market interest rates would enable more investments with a low scale of productivity, leading to a decreasing capital return ratio (Clark, 1908, 186).

Marshall (1890, 614, 620–622), although rephrased to some extent, acknowledged Böhm- Bawerk’s time-focused explanation of the interest rate. He described it as a compensation payment for waiting, which composes the earnings for managing a business and an insurance premium for specific risks. In his theory, the interest rate represents an equilibrium price for supply and demand of capital. If, for example, the power and will to save money increases, the interest rate should, ceteris paribus, decrease due to an increasing market supply (Marshall, 1890, 624–625). Marshall (1890, 616) also discussed the, from his perspective, hypothetical, but today quite real, situation in which the interest rate equals zero and where the investor even has to pay a premium for storing his capital. In this scenario, the missing savings incentive would lead to a decrease of available capital, which should automatically lead to an increasing interest rate. Based on this explicit assessment, and comparable to Böhm-Bawerk, there remains only a limited possibility to derive statements for the zero interest rate environment without contradicting other parts of Marshall’s theory.

In his Theory of Interest, Fisher (1930) developed another important contribution in the neoclassical era. Herein, the determination of interest rates is primarily based on three pillars. The first pillar describes the impatience principle, according to which any market participant strives to maximize his current and future income stream. This especially includes the valuation of present versus future incomes (dependent on their size, distribution in time, composition, probability, and personal factors) and their modification by borrowing and investing money. The second pillar describes the investment opportunities and their present value, which represents the decision criterion for a market participant’s income optimization. The third pillar outlines the market principle, which determines the market equilibrium interest rate. According to this principle, the equilibrium interest rate enables market clearance, matching the monetary supply and demand (Fisher, 1930, 61–227).

The essential aspects of Fisher’s theory are based on time preferences and utility optimization, which is recognized by Böhm-Bawerk (1921b, 458) as a related—or rather, comparable— approach to his own. According to that, the market participant could borrow money as long as the return from its exploitation exceeds the interest rate for the loan (Fisher, 1930, 104–112). Thus, market interest rates and investments are negatively related. Applying this concept to the 22 zero interest rate environment, the borrowings should increase significantly to finance income modifications, which, in the case of corporate finance, could include entrepreneurial investments with a lower rate of return. However, the above-discussed limitations also apply to Fisher’s theory. In his book, he explicitly and repeatedly rejected the possibility of a zero or negative market interest rate (cf. e.g. Fisher, 1930, 40, 68, 192). Regarding the price equilibrium mechanism and the herein required incentive for lenders to provide capital to the market, Fisher’s rejection is comprehensible. At the same time, it illustrates that his theory did not cover governmental interventions for determining the equilibrium price, and thus is not used for cause-and-effect derivations.

From this point, the detailed discussion of further works from the late 19th to early 20th century would result in redundancies. As such, the following influential contributions on the topic of interest rate theory are only briefly outlined. Wicksell (1898, 82, 85, 97–102), for instance, also described the interest rate as an equilibrium price between monetary supply and demand. From his perspective, interest rate also influences the market prices of goods. Accordingly, decreasing interest rates enable market participants to invest more money into capital or consumption goods, consequently leading to rising prices. These considerations could be perceived as a vanguard of the famous Fisher Equation, which represents a major element of the quantity theory of money (Fisher, 1922, 26). However, this theory is not explored further here due to its limited focus on the topic of interest rates.

Hayek (1933, 212–218) proposed in his theory that the interest rate primarily features an allocation function between savings and investments. In the long term, this mechanism also influences the prices of goods, since decreasing interest rates trigger a decrease in savings and an increase in investments, which in most cases should result in rising prices of goods. However, despite being a further development of Wicksell’s approach, his theory provided only limited additional insight regarding the topic of interest rate theory.

Schumpeter’s (1987, 283–288, 291) interest rate theory was strongly influenced by the treatise of Böhm-Bawerk, who had been one of his teachers during his studies. He also followed the approach to describing the equilibrium interest rate as a result of market supply and demand. Herein, the interest rate was regarded as compensation for time and provided a decision basis for further investments that should be pursued as long as the variable entrepreneurial return exceeded the necessary interest payment on the investment loan. Furthermore, Schumpeter (1987, 296–299) also rejected the possibility of a negative interest rate due to the described market mechanism and the missing incentive for the supply side. 23

Gesell (2003, 264–265) introduced a different perspective on the topic of negative interest rates. Assuming storage costs for money and an oversupply on the market, savers could, from his perspective, possibly pay the borrowers for the storage of their capital. Although Gesell’s theory did not achieve the same international prominence and recognition compared to the other cited authors, it seems in this point alone to hold true regarding today’s deposit charges on savings. As previously discussed, the majority of scientific contributions rejected the idea of zero or negative interest rates. Therefore, the equilibrium price for loans should usually be positive, which held true for most economies until central banks began to control inflation through interest rate alterations to or below zero.

Table 5 below presents a summary of the extensively discussed theories from the early neoclassical era.

Table 5: Synopsis of Neoclassical Interest Rate Assessment Scholar (Zero) Interest Rate Assessment Source Lausanne School  Cause-and-effect: Lower interest rates lead to a shift between savings and capital investments to reach a new capital allocation equilibrium. Walras (trans. 2014, Léon Therefore, market participants will search for yield and increase 288–289, 366, 382– Walras investments. 384)  Interest rates are being determined by supply and demand for money, and are thus a dependent rather than determinant variable. Austrian School  No cause-and-effect derivation possible. Eugen von von Böhm-Bawerk  Interest rates represent an intertemporal uncertainty compensation. Böhm- (1921b, 324, 327, 332, Bawerk  Interest rates are being determined by supply and demand for money, 363, 472) and are thus a dependent rather than determinant variable. American Marginalists  Cause-and-effect: Lower interest rates enable investments with a lower scale of productivity. Therefore, an increase in investment activity should be evident. John Bates Clark (1908, 125, 185–  Cause-and-effect: The lower scale of productivity leads to a Clark 187) decreasing capital return ratio.  Interest rates are a result of the productivity of a perpetual social capital funds.  No cause-and-effect derivation possible.  Market participants strive to optimize their income streams though Irving borrowing and investing money, which is pursued as long as the Fisher (1930, 68, 104– Fisher additional return is superior to the market interest rate. 112, 192, 222–226)  Market interest rates represent an equilibrium price at which a market clearance can be realized through equal supply and demand. Cambridge Neoclassicals  No cause-and-effect derivation possible. Marshall (1890, 614, Alfred  Interest rates represent a premium for waiting. 616, 620–622, 624– Marshall  Interest rates are being determined by supply and demand for money, 625) and are thus a dependent rather than determinant variable. Source: Own representation. Summarizing the results, two major findings can be derived. First, the interest rate theory underwent an evolutionary step, growing from primarily a side note position into the spotlight 24 of economic research. In particular, Böhm-Bawerk’s (1921b) treatise, with its time-focused equilibrium approach, represented a milestone in interest rate theory that induced far-reaching scientific discussion. Second, despite the new scope of theoretical foundation, the current zero interest rate environment and its impacts can still barely be explained in compliance with these theories. Several authors have explicitly ruled out zero or negative interest rates as a hypothetical or impossible scenario to strengthen their equilibrium derivation. Herein, the equilibrium state is based on monetary supply and demand or the equivalence between savings and investments. With an interest rate that provides no inducement to a money supplier, the self-regulating process of a new equilibrium should rule out today’s reality by increasing interest rates. However, this is not happening, and since Gesell’s (2003, 264–265) approach was not assimilated into the mainstream, valid explanations remain scarce commodities.

To expand the analysis scope in search of valid cause-and-effect relationships, the following subchapters cover alternative economic approaches during the neoclassical era, thematic influenced economic theories, and modern schools of the late 20th century. The analysis, however, is comparable to the previous subchapters, focusing on mainstream theories and thematically relevant topics.

2.3.4 Heterodox Economic Approaches The first, and maybe most important, heterodox approach to be pointed out was presented by John Maynard Keynes (2018) in his General Theory of Employment, Interest, and Money, originally published in 1936. In general, Keynes shared several concepts of neoclassical economists regarding interest rate. His definition of marginal efficiency of money, for example, stated that, as long the rate of return exceeds the interest rate, new investments will be pursued by market participants (Keynes, 2018, 124). Furthermore, he agreed on the equilibrium mechanism resulting from the interest rate, with a modified definition. From his perspective, the equilibrium interest rate describes the price in which the desire of holding cash equals the available quantity of cash on the supply side (Keynes, 2018, 146–147). With this definition, he introduced the concept of liquidity preference. According to that, every market participant prefers to hold cash and maintain liquidity for different purposes. The four specific purposes include the income motive, business motive, precautionary motive, and speculative motive (Keynes, 2018, 172–173). According to the income motive, cash bridges the time between expenditures and income. Following the business motive, cash bridges the time between disbursement of business costs and realization of revenues. The precautionary motive describes the situation in which unforeseen expenditures and sudden purchases can be covered with a cash reserve. Finally, and most importantly for the present discussion, the speculative motive 25 describes behavior in which a market participant holds cash based on his expectations of the future market development.

Keynes (2018, 146, 152) strictly rejected the idea that an interest payment represents compensation for waiting, since this would imply that hoarding cash would also automatically lead to an interest payment. Instead, he described interest payments as rewards for not hoarding money. Since the speculative reserve is based on expectations, decreasing interest rates should, ceteris paribus, lead to its increase. Market participants are less willing to surrender liquidity for the lower market interest rate, and thus hold more cash for speculative reasons. If the decrease in interest rates is induced by monetary policy, then the impact could cover not only short-term, but also long-term beliefs, leading to an even greater increase in speculative reserve, with cash deprived from the lending market (Keynes, 2018, 177–180).

Keynes (2018, 151) also discussed other possible monetary policy failures. For example, an increase in monetary quantity does not necessarily lead to a decreased interest rate, since the speculative or other reserves could absorb the additional liquidity. On the other hand, a decreasing interest rate does not necessarily lead to increased investment activity, since the rate of return could decline more rapidly, eliminating any given inducement.

By establishing a relationship between monetary policy measures and market expectations for short-term and long-term interest rates, Keynes provided important preparatory work for today’s understanding of the transmission mechanism, as described in Chapter 2.2. This relationship, combined with his understanding of liquidity traps resulting from increasing speculative reserves, provided useful predictions of employment and inflation developments during the financial crisis, although his theory was discussed controversially beforehand (Krugman, 2018, XXXVIII–XLIII). Accordingly, if the interest rates decrease to a certain level, the monetary authority could lose control over the effective interest rate due to the favored speculative reserve in cash. Public authorities would also be able to borrow money at nominal rates through the banking system, since the monetary supply would effectively remain unused (Keynes, 2018, 182).

Another important point stated by Keynes (2018, 148) concerned the minimum level of interest rates. In his model of liquidity preference, he assumed that the rate of interest cannot be negative on the savings side, thus inducing market participants to give up hoarding. Here, the theory does not correspond with reality, at least for the German market, as deposit charges were introduced by banks as compensation for the ECB’s negative rates in the overnight deposit

26 facility. For the lending side, he described the intermediary costs produced by a bank in the process of providing loans as the main reason for slower adjustment of interest rates to the market and for the institutional impossibility of zero or negative interest rates (Keynes, 2018, 183, 192). The composition of these intermediary costs is analyzed in detail in Chapter 3.

Summarizing the analysis results, Keynes presented a theory that was considered heterodox at the time, but which later proved to be close to reality during the financial crisis and its aftermath. The theory also provides specific cause-and-effect relationship considerations for the zero interest rate environment. First, decreasing interest rates should, in most cases, lead to increased investment activity. Second, if the interest rate decreases to a certain level, investments could be mitigated, and the available cash would instead be hoarded as a speculative reserve. With the interest rates being zero or even negative on the savings side, increasing cash positions indicate missing investment opportunities that would provide an adequate return on investment, including a premium for taking the illiquidity risk. This relationship can also be projected on the lending side, since interest rates close to zero would induce extensive lending if sufficient investment opportunities were at hand.

Hicks (1937) took up Keynes’ idea and merged it in his famous investment-saving and liquidity preference-money (IS-LM) model with the neoclassical mainstream. Although this model solved several ambiguities and provided a simultaneous equilibrium framework for the money and goods market, it provided only minor additions to the discussion regarding determinants and effects of interest rate adjustments (Söllner, 2015, 160–161). A comparable conclusion can be drawn for the contributions of Baumol (1952), Whalen (1966), and Tsiang (1969).

Baumol (1952, 545–549) provided an explanation of the cash holdings based on the income and business motives from a logistics sector perspective. Herein, the market participant has to compare opportunity costs from missing interest income, based on warehousing of cash, against exchange costs for trading illiquid into liquid assets. Whalen (1966, 315) followed the same approach for explaining the cash holdings resulting from the precautionary motive. He proposed costs from possible illiquidity, opportunity costs from missing interest income, and the amount and variability of income as determinants of the precautionary reserve. Tsiang (1969, 106–111) connected the transaction-based and the precautionary reserve, stating that the smaller the first becomes, the stronger the latter will grow.

These three economists solved specific ambiguities, and thus advanced Keynesian ideas. However, their contributions aimed at developing the mainstream, and not on developing a

27 completely new approach. Furthermore, exploring other heterodox schools of economic thought not yet discussed, the theoretical foundation, and thus the additional insight into the topic of interest rates, falls short compared to the impact of Keynes’ treatise. To name some examples, the interest rate remains mostly a peripheral phenomenon or features at least subordinate importance in the Historical Schools, the Green Economic Schools, and Evolutionary Economic Schools.

One of the three exceptions pointed out in the following concerns the Monetarism School. Beside their strong rejection of the Keynesian perception regarding the relation between inflation and unemployment (i.e. Phillips Curve), monetarists also pursued a different approach for effective monetary policy (Friedman, 1968). As one of its major scholars, Friedman (1992, 30–50) proposed abandoning the interest rate as a monetary policy instrument and to instead utilize the quantity of money as the sole control tool for inflation—or rather, the unemployment rate in an economy. With this proposal, monetary policy should directly influence the total demand through monetary quantity. In contrast, the Keynesian approach describes that the monetary policy influences interest rates, which then indirectly affect the total demand (Söllner, 2015, 176). However, the disagreement is based on a different perception of the transmission mechanism rather than on major theoretical differences (Modigliani, 1977, 1). Regardless, a derivation of cause-and-effect relationships remains challenging. With the interest rate being a dependent variable in the interrelated system, implications for financing and investment decisions should be formulated using monetary quantity as the determinant. This would lead to comparable relationships, as in the neoclassical and Keynesian frameworks. However, this does not represent the focus of the present analysis, and is thus not explored further.

The second spotlight is devoted to Pigou’s (1943, 349–350) understanding of the IS-LM model. Focusing on a different perspective, he proposed that, in a deflationary state, the interest rate should automatically adjust from zero to a positive value. This would result from rising unemployment, followed by decreasing prices, and then the increasing real value of money stock, which should subsequently stimulate consumptive expenses and increase interest rates (i.e. Pigou Effect). Beyond this explanation, Pigou also explicitly ruled out negative interest rates in his theory by using the store-of-value function of money (Pigou, 1943, 346). The problem of a speculative reserve that leads to a liquidity trap due to low interest rates would be resolved in Pigou’s model. The long-term interest rate environment in Japan, however, provides empirical evidence against his assumption (cf. Appendix A). The derivation of cause-and-effect relationships would be redundant to other neoclassical scholars, and is thus not explored further.

The third contribution to be pointed out concerns Tobin’s (1958) work on the linkage between risk and liquidity preference. Herein, the speculative reserve is explained by the market participant’s expectations of capital gains or losses resulting from holding interest-bearing assets rather than cash. In the general case of a risk-averse individual, the interest rate should provide an adequate inducement for leaving the all-cash position and converting to a mixed or even all-bond portfolio (Tobin, 1958, 71–74, 77–80). Low interest rates combined with a specific expectation regarding capital losses would result in an all-cash position due to a lack of shifting inducement, confirming Keynes’ speculative reserve assumption. On the other hand, high interest rates should bear sufficient compensation that an all-bond position could also be pursued (Tobin, 1958, 85–86). Tobin’s approach is consistent to both the empirically evident diversification of cash assets and Keynes’ liquidity preference. Therefore, it provides an important milestone for further developing the Keynesian School, as well as additional insight into the zero interest rate environment. Accordingly, decreasing interest rates should be accompanied by a decrease in the non-cash position until a certain level is reached and the market participant has shifted his portfolio completely into a cash reserve. In his analysis, Tobin (1958, 78) a assumed a constant risk estimation by the market participant. This topic is analyzed later regarding its connection to portfolio theory implications. Table 6 below summarizes the primarily discussed schools in this topic.

Table 6: Synopsis of Heterodox Interest Rate Assessment Scholar (Zero) Interest Rate Assessment Source Keynesian School  Cause-and-effect: Lower interest rates lead to increased investment.  Cause-and-effect: Interest rates below a certain level lead to increased speculative reserve, since the inducement of surrendering liquidity is Keynes (2018, 124, John insufficient. 146–148, 151–152, Maynard  Market participants have a liquidity preference, and thus hold or 172–173, 177–183, Keynes rather hoard cash for specific purposes. 192)  Interest rates represent the equilibrium price in which the desire of holding cash equals the available quantity of cash on the supply side.  Cause-and-effect: Lower interest rates lead to a shift from an all-bond James or diversified to an all-cash portfolio. Tobin (1958, 85–86) Tobin  Considering income allocations, market participants take not only the return of an investment, but also its risk into account. Monetarism  No cause-and-effect derivation possible. Milton  Interest rates should result from quantitative monetary policy Friedman (1968, 14– Friedman measures, and thus represent a dependent rather than determinant 17) variable. Source: Own representation.

Briefly summarizing the results, Keynes’ liquidity preference framework provided the most influential heterodox theory on interest rates in the 21st century. The development of key

29 interest rates in turn provides the necessary basis for hypothesis tests regarding market participants’ liquidity preferences. The following subchapter shifts the focus from mainstream macroeconomic perspectives to emphasize theories thematically related to the topic of interest rates in order to generate additional insights.

2.3.5 Modern Theories and Microeconomic Approaches As stated in the previous subchapter, modern schools of economic thought, apart from the mainstream and major heterodox approaches, cover the interest rate topic either on a highly limited scale or not at all. Before focusing on the theories that actually provide additional insights for the analysis, several modern schools and microeconomic approaches are briefly alluded. This forerunning analysis, however, is exposed to the granularity of current research, including its partially still pending mainstream acknowledgement, and is thus by no means exhaustive.

Evolutionary economic theory utilizes evolutionary methodology to describe transformation processes in economies. The analysis of transformation covers, for example, technology and routines (cf. Nelson & Winter, 1982) or the competition process in markets (cf. Dopfer, Foster, & Potts, 2004). An evolutionary approach for explaining interest rates has yet to be provided.

Ecological economic theory concerns research into the interdependence between economies and natural ecosystems, stating that natural resources influence the circular flow of income and should thus be included in the model (cf. e.g. Martinez-Alier & Muradian, 2015). Attempts to estimate the value of natural resources have also been pursued by leading scientists of this school (cf. e.g. Costanza et al., 1998). Ecological economic theory should not be confused with environmental economic theory, which focuses sources and possible solutions to environmental problems, considering the scarcity of natural resources (cf. e.g. Phaneuf & Requate, 2017). However, except for the discussion about declining interest rates in project valuation due to rising uncertainty over time (cf. e.g. Groom, Hepburn, Koundouri, & Pearce, 2005), those schools’ theoretical contributions to the topic at hand remain rather rare.

Game theory provided an important framework for the outcome analysis of a rational decision- making process. On one hand, its mathematical foundation allowed utilization in different scientific schools, such as economics (cf. e.g. von Neumann & Morgenstern, 1953) or biology (cf. e.g. Smith, 1982). On the other hand, this broadly applicable approach does not provide specifics regarding topics such as interest rate. This statement is also partially applicable to several inductive disciplines, which contribute to the mainstream theory by conducting empirical research or bottom-up analyses. Based on the research granularity, within those 30 schools of thought, few results have managed to become scientific paradigms. Experimental economics, behavioral economics, and positive economics in general could be identified as such disciplines.

In experimental economics, scientists study economic questions by applying experimental or quasi-experimental test designs (cf. e.g. Davis & Holt, 1993). Behavioral economics studies decision-making processes, particularly the limits of market participants’ rationality in order to integrate psychological and mostly microeconomic aspects (cf. e.g. Thaler, 2005; Tversky & Kahneman, 1992). Positive economics comprises a general term for descriptive and non- normative research (cf. e.g. Friedman, 1953). All three disciplines include important empirical results, the discussion of which is postponed to the respective chapter. Focusing on microeconomic topics, their overall contribution to the neoclassical equilibrium paradigm of the market interest rate, however, remains limited.

Other recent approaches, such as the new economic geography (cf. Krugman, 1995), new trade economics (cf. Krugman, 1981), or feminist economics (cf. Waring, 1999) are not explored further here due to their limited relevance.

The two theoretical approaches subsequently discussed in detail include the theory of the bank and the portfolio theory. Starting with the theory of the bank, two frameworks must be introduced first for a full understanding of possible implications. The first framework concerns the transaction costs theory. In the neoclassical model, the market functions as a cost- and friction-free exchange coordinator. If this model reflected reality, the existence of intermediaries or firms in general would be obsolete (Coase, 1937, 386–389). The transaction costs theory, by contrast, is based on the assumption of uncertainty that manifests itself in market participants’ irrational behavior. Furthermore, it assumes asymmetric distribution of information, resulting in opportunistic behavior (Williamson, 1985, 44). In such a market, participants are forced to execute actions in relation to information, coordination, and communication to determine, transfer, or enforce their property rights on a resource (Erlei, Leschke, & Sauerland, 2007, 199–201). These multiple actions create positive ex ante (e.g. costs for search, information, negotiation, documentation, and agreement) and ex post (e.g. costs for control, enforcement, and adjustment) transaction costs (Schmid, 2013, 59–60). The amount of those costs is, from a theoretical perspective, the reason for the existence of firms. Firms are able to cumulate several transactions and reduce overall transaction costs for the market participant through internal coordination (Coase, 1937, 390–398).

Asset specificity, uncertainty, and transaction frequency represent the most important determinants of transaction costs (Williamson, 1985, 52–63). Accordingly, transaction cost theory defines four coordination mechanisms that should reduce transaction costs in the respective parameters. The market coordination comprises the most effective mechanism for nonspecific assets and recurrent transactions. The ability to change the counterparty at any time limits opportunistic behavior and reduces the transaction cost. The second coordination mechanism emphasizes contracts with a mixed or idiosyncratic asset specificity and occasional or one-time executions. The theory proposes trilateral governance (neoclassical contracting) for such settings. By integrating an independent intermediary to mediate between the counterparties, the cost of potential disputes due to asymmetric information distribution can be efficiently reduced. In contrast, bilateral governance (e.g. cooperation) is proposed as an effective mechanism for recurrent transactions with a mixed asset specificity. The costs associated with setting up the bilateral governance should induce both counterparties to maintain the contractual relationship and avoid exploiting the respective counterparty. The fourth coordination mechanism considers recurring transactions with a high asset specificity and proposes internal coordination within the firm. The repeatedly required investments to secure this type of contract can be substituted by the one-time construction costs of an internal solution. The increasing knowhow resulting from unified governance of input factors can significantly reduce transaction costs (Williamson, 1985, 72–78). Table 7 below summarizes the governance mechanisms regarding the transaction conditions.

Table 7: Efficient Coordination Mechanisms According to Transaction Cost Theory Asset Specificity Nonspecific Mixed Idiosyncratic

Frequency

Trilateral governance Occasional (neoclassical contracting)

overnance

g Bilateral Unified Recurrent governance governance

Market Market (classical contracting) (classical (relational contracting) Source: Adapted from Williamson (1985, 79).

The second framework covers the principal agent theory. The previously described minimization of transaction costs requires efficient coordination mechanisms, including

32 contractual division of labor and delegation of property rights from a principal to an agent. These contractual agreements enable the agent to make decisions that can affect the welfare of both the principal and himself. Information asymmetries in imperfect markets result in opportunistic behavior as both counterparties attempt to maximize their personal benefit (Jensen & Meckling, 1976, 308–310). Based on this conflict of interest and without intervention measures, the better-informed agent would systematically exploit his knowledge advantage (i.e. agency problem) (Eisenhardt, 1989, 58–59). The principal agent theory focuses on defining appropriate information, inducement, and control mechanisms to solve the agency problem under the constraint of minimizing agency costs incurred in the development, execution, and enforcement of said mechanisms (Schmid, 2013, 79).

Using hidden characteristics, hidden information, and hidden actions, scientific literature typically differentiates three problem types of asymmetric information distribution (Alparslan, 2006, 21–24). Hidden characteristics include agent properties that are unknown to the principal prior to the conclusion of the contract (e.g. preferences, capacity, and intention to perform). The principal is thus exposed to the systematic risk of adverse selection by entering into contractual relationships with agents possessing inadequate characteristics (cf. lemons example by Akerlof, 1970, 489–490). After concluding the contract, the risk of hidden actions manifests itself. This risk describes situations in which the principal is unable to observe the agent’s actions and monitor whether or not he is acting with the necessary level of effort and diligence in terms of the contractual relationship (Schmid, 2013, 82). Hidden information delineates the state in which the principal’s observation of the agent is possible, but does not allow assessing his work effort in the contractual sense. On the one hand, this may be due to the lack of specialist knowledge of the principal, while on the other hand, information on exogenous factors can also be selectively forwarded or manipulated by the agent (Wolf, 2011, 364). The risk of opportunistic exploitation of information asymmetries is called a moral hazard. The agent selects an alternative course of action corresponding with his maximum personal benefit, but which is not fully in the principal’s interest (Alparslan, 2006, 27–28).

Based on the described problem types of information asymmetries, the principal-agent theory proposes information, inducement, and control mechanisms for behavioral governance. The information mechanisms include tools for screening by the principal (e.g. application tests, explorations), signaling by the agent (e.g. certificates, warranties), and reputation of the agent (e.g. from previous contractual relationships). These are suggested primarily for solving the problem of adverse selection (Scholtis, 1998, 85–87, 111–113). Control mechanisms focus on

33 reducing information asymmetries after a contract’s conclusion and include monitoring and reporting. During monitoring, an internal or external instance monitors whether the agent is compliant with the principal’s contractual specifications. Reporting, on the other hand, describes the agent’s information delivery to document flawless compliance. Finally, the third behavioral control mechanism includes positive and negative incentive systems that can be implemented both before and after contract signing. Prior to the signing, commitment propositions by the agent, bonding, and self-selection can be employed as tools to identify intent. After the contract’s conclusion, the principal primarily uses incentive systems in which the agent mutually benefits from an improved principal welfare (e.g. remuneration in company shares). Simultaneously, these systems typically feature sanctioning mechanisms punishing the agent for opportunistic behavior (e.g. contractual penalties) (Schmid, 2013, 86–88).

Optimizing agency costs represents the focus of applying the described mechanisms. Success, however, results not only from utilizing individual measures, but also from combining them in order to achieve a (hypothetical) Pareto efficiency (Ross, 1973, 138). Summarizing, the principal agent theory suggests that, when delegating property rights, transaction or rather agency costs can efficiently be reduced via using suitable instruments. Accordingly, the framework of the transaction cost theory is implemented in the principal agent theory. These theoretical approaches represent two of the three important cornerstones of new institutional economics (Wolf, 2011, 338). Table 8 below summarizes the problem types with their characteristics and possible control mechanisms.

Table 8: Instruments of Behavioral Governance in the Principal Agent Theory Problem Type Hidden Hidden Hidden Action Characteristic Information Property Ex ante unobservable Unobservable level Unobservable Cause of Origin characteristics of the of effort of the information level of agent agent the agent Resulting Risk Adverse selection Moral hazard Moral hazard Signaling, Information screening,

reputation Monitoring, Monitoring, Control reporting reporting

overnance

Behavioral

G Self-selection, Incentive or penalty Incentive or penalty Inducement commitment, systems systems bonding Source: Own representation based on synopsis by Schmid (2013, 83–84). 34

The question at hand concerns why the two frameworks from new institutional economics are so important for describing the theory of the bank. The theory of the bank is a theory about intermediation (Bessler, 1989, 18). Herein, the bank’s raison d’être concerns minimizing transaction or agency costs. In an efficient neoclassical model with rational behavior and perfect information, a lender and a borrower should equilibrate their demands in the market. Neither uncertainties regarding loan repayment nor any frictions regarding demand divergence should arise in such a model. Accordingly, any kind of agent, such as a bank, would be superfluous. However, reality indicates otherwise, and the market features numerous intermediaries that cumulate specific transactions to reduce overall transaction costs. Typical frictions resolved by a bank include geographical divergence, volume divergence, maturity divergence, and particularly risk divergence (Bessler, 1989, 21–24). Due to economies of scale, only a limited number of lenders or borrowers are able to underprice the intermediary costs of a financial institution and create the necessary procurement conditions in their own capacity. For example, any lending transaction usually requires a risk assessment. If the lender possesses a significantly larger amount of money available than different borrowers demand, splitting the amount into multiple loans would represent a possible solution. In these circumstances, the absolute risk assessment costs would increase, and the average return would, ceteris paribus, decrease. The lender would then have to decide whether utilizing an intermediary would constitute a cheaper solution for resolving the friction of volume divergence.

Projecting the theory of the bank on the initial task, an important conclusion can be drawn. Intermediary costs moderate the relationship between key interest rates implemented by a central bank and bank interest rates that are accessible to market participants, such as corporates (cf. Figure 4). This conclusion from new institutional economics is consistent with Keynes’ (2018, 183, 192) assessment of the possibility of zero or negative interest rates. He rules out this possibility due to the necessity of a minimum interest rate level based on given intermediary costs. The model is also consistent with the transmission mechanism, which describes an indirect relationship between monetary measures and bank interest rates. Concluding, it is safe to say that financial institutions are in competition regarding the level of intermediary costs, as principals strive to maximize their utility by minimizing transaction costs. The determinants of those costs and implications of their variation are discussed further in Chapter 3.

Figure 4: Intermediary Costs Moderate the Key and Bank Interest Rate Relationship

Source: Own representation based on the indirect financing relationship presented by Bessler (1989, 20).

The final spotlight of this subchapter concerns the modern portfolio theory. This theory describes how investors can reduce their overall portfolio risk by combining non-perfectly correlated assets. Taking into account the expected returns and risks (i.e. standard deviation) of investment opportunities, it is possible to derive an efficient frontier at which no other portfolio of assets exists that presents a higher expected return at given risk or a lower risk at given expected return. Furthermore, assuming that a risk-free investment opportunity exists, an investor will be able to create a portfolio along the capital allocation line according to his personal risk preferences. The capital allocation line connects the risk-free rate with a tangential point on the efficient frontier representing the market portfolio (for a more detailed discussion of the relationships cf. Elton, Gruber, Brown, & Goetzmann, 2014, 302–304; Markowitz, 1952; Sharpe, 1966).

This brief summary contains two topics relevant for the further discussion. The first topic emphasizes the personal risk preference of an investor, who is usually assumed to be risk-averse (cf. e.g. Sharpe, 1966, 122). The second subject focuses on the nominal value of the risk-free interest rate currently being affected by the ECB’s key interest rate policy. Combining both, the question arises as to whether an investor’s risk preferences are stable over time or if an exogenous shock, such as the alteration of key interest rate to or below zero, influences the investor’s behavior. Scientific literature regarding portfolio theory usually focuses on the alteration of the capital allocation line slope without variation of the actually achievable risk- free rate (cf. e.g. Singal, 2017a, 340). As presented in Figure 5 below, the decrease of the risk- free interest rate would automatically influence the slope of the capital allocation line, and thus change the market portfolio position on the efficient frontier. Adding an element from the utility 36 theory to the consideration, this alteration would also produce a lower utility curve achievable for the investor at a given level of risk. In other words, if the investor combines the market portfolio with the risk-free investment opportunity along the capital allocation line at the same level of risk, he would achieve lower utility than before. In a world where this state would be accepted by the investor without countermeasures, a discussion about reaching for yield should not arise (cf. e.g. Neubauer, 2018, 22–23). However, the current medial discussions suggest otherwise and induce the scientific scrutiny of the assumed risk preference stability, as stated in the aforementioned question (cf. e.g. Baucells & Villasís, 2010, 209; Schildberg-Hörisch, 2018, 148–150). The portfolio theory does not yet offer a specific solution to this question, as risk preference stability comprises a given assumption in the framework. The impact of the zero interest rate environment in this context should thus be analyzed in detail.

Figure 5: Impact of Changing Risk-Free Rate on Indifference Curve and Return

Source: Schaab, Frère, and Zureck (2019, 481). Summarizing, both the theory of the bank and the portfolio theory offer important insights into possible effects of the zero interest rate environment, as is considered in the following hypothesis development. While the theory of the bank tackles the intermediary level, as elaborated in Chapter 3, the portfolio theory is especially important for asset allocation and investor behavior, which constitutes the topic of Chapter 5. The following and conclusive subchapter covers the recent contributions on the topic of zero lower bound.

2.3.6 Liquidity Trap and Zero Lower Bound Problem In recent scientific literature, especially in the context of the analysis of Japanese monetary policy during recent decades, the term zero lower bound problem was actively employed by several authors (cf. e.g. Krugman, Dominquez, & Rogoff, 1998). This describes a situation in which a central bank’s scope of action is limited by market interest rates equal to zero. Assuming that a recessionary shock hits an economy with key interest rates already at a rather low level, the countermeasure options for the central bank remain limited according to this approach. The central bank could decide to further lower the key interest rates to a level below zero to stimulate consumption and inflation. However, as long as market participants can choose between an interest-free currency (i.e. cash hoarding) and a negative interest rate, the central bank’s stimulus experiences a fixed boundary. Before paying a negative interest rate, the market participant could hypothetically withdraw the liquidity from his accounts and hoard the cash (Coenen & Wieland, 2003, 1072). This topic has been the cause of numerous controversial discussions regarding alternatives to stave off a recessionary shock. While several authors have advocated the zero lower bound problem, and thus insist on utilizing fiscal policy instruments in times of crises (cf. e.g. Krugman et al., 1998; Ullersma, 2002), others reject the boundary’s importance for a central bank’s scope of action and instead suggest monetary measures that aim at negative real interest rates (cf. e.g. Friedman, 2000; Fuhrer & Madigan, 1997). Several of the contributions have also discussed how to avoid or solve the hoarding problem. One prominent example includes the proposed devaluation lottery, in which a randomly chosen one-digit or two-digit number decides which notes with the same end serial number could become worthless (Mankiw, 2009). If hoarding’s negative effect exceeded that of keeping the money in a savings account, the problem of zero lower bound could be resolved.

However, the zero lower bound problem could also be described as an old problem with a new topic. Comparing the discussion with Keynes’ framework regarding speculative reserve, it becomes obvious that both concepts pursue similar ideas. Keynes also argued that, starting at a specific interest rate level, market participants would shift their investments from assets to cash due to the inappropriate inducement of surrendering liquidity (Keynes, 2018, 172–180). Therefore, the problem of hoarding in a zero or negative interest rate environment is not a new concept. Due to given redundancies and the zero lower bound problem’s embedding in neoclassical and Keynesian theory, the separate derivation of cause and effect is not explored further here.

Based on the extensive theoretical analysis of cause-and-effect relationships, the following subchapter covers the first conceptualization of research hypotheses, which are refined in the following main chapters.

2.4 Theory-Based Hypotheses Conception The first point to make is that the results of the theoretical analysis are sobering. Today, the interest rate theory offers no paradigmatic cause-and-effect relations between the zero interest rate environment and the three introductory named problem groups (i.e. banks, corporations, and investors). Instead, the theory has been coined by several major and countless other opinions, none of which are able to completely conquer the scientific mindset or admit theoretical defeat. The same conclusion was already reached by Böhm-Bawerk (1921a, 5) approximately 130 years ago. On the one hand, it is rather surprising how the theory is still characterized by such granularity after centuries of economic research. On the other hand, these circumstances also provide a rich breeding ground for empirical research to further develop the topics of interest. In the following, several of the discussed aspects are assessed from a logical perspective to derive the first conceptualization of research hypotheses for the respective group of interest.

Focusing first on Germany’s banking sector, the previously presented Figure 4 can be used to discuss implications of the zero interest rate environment. From a bird’s eye perspective, a bank’s business model should be heavily affected by the ECB’s key interest rate alteration. If the opportunity interest rates decrease due to lower key interest rates and changing market expectations (cf. transmission mechanism), customers who are lending money to the bank should be confronted with decreasing interest rates in new or variable contracts as well. Otherwise, the bank would pay more to its lenders than necessary. For customers borrowing money from the bank, decreased opportunity interest rates should produce a similar effect. However, this effect could rather be explained by competitive market mechanisms in place in today’s digitalized and transparent society. Customers can easily compare prices online (e.g. Check24, Verivox) and choose the institution of their preference. Therefore, a significantly higher price for loans would be difficult to assert on the market for a longer period of time, and the contractual interest rates should decrease to a new equilibrium. The actual room for slight competitive differentiation is the amount of intermediation costs. If a bank is able to produce the same service for lower costs than its competitors, it will be able to either generate higher returns or achieve higher growth due to the possibility of offering lower prices.

The special set-up of the zero interest rate environment also adds the possibility of completely eliminating the intermediary. Assuming that the intermediary costs in such an environment exceed the current opportunity interest rate, the lender or investor who brings his money to the bank would receive either a zero or a negative return. As of August 2019, negative returns have already been a reality for a total of 112 German banks, who charge their customers for high deposit amounts (Siedenbiedel, 2019). Thus, market participants already have to decide whether to accept a zero or negative return on deposits or to search for alternative ways to employ the money. The first alternative would be opportune if no available investments correspond with the market participant’s risk-return profile. The negative yield could be seen as a cost of carry component for holding the liquidity as a deposit (cf. Gesell, 2003, 264–265). It should be lower or at least equal to the costs of insurance, safekeeping, and managing an actual cash reserve in notes. Otherwise, withdrawing money from the deposit account and the subsequent hoarding would be the rational consequence. The second proposed alternative would be to search for other investment opportunities or intermediaries who are still able to offer some positive return. This could also mean eliminating the intermediary in between, and thus sacrifice specific services (e.g. risk analysis) that then have to be insourced. This behavior has already been seen in the German financial industry as, for example, insurance companies have entered the market for mortgage loans and contracted retail customers directly rather than using an intermediary (cf. e.g. Gesamtverband der Deutschen Versicherungswirtschaft e. V., 2018, Table 16).

Summarizing, two cause-and-effect assumptions are worth further investigation. The first is that a bank’s internal costs should significantly influence profit growth during periods of a low or zero interest rate environment (H1A). The relationship between low intermediary costs and, ceteris paribus, higher returns is quite obvious, and is thus not pursued separately. Profit growth, on the other hand, should especially result from customers who either have no other investment opportunities (i.e. lenders) or are able to invest in projects with returns above the lending rate (i.e. borrowers), and who both strive to rationally choose a partner with the lowest costs of intermediation under the given conditions. The second assumption covers those customers who are not willing to pay intermediary costs at the expense of their positive returns. Therefore, low key interest rate environments should also significantly influence the growth of peer-to-peer markets or the growth of intermediaries whose intermediation costs significantly differ from those of a bank (H1B).

Focusing on the second group, corporations, deriving research hypotheses is comparably easy. This mostly follows from the theoretical consensus regarding investment activity in times of decreasing interest rates. According to multiple theories, companies should increase their investment activity and especially pursue more investment projects with lower returns. This follows from the lower financing costs that have to be earned through the executed project, and the ensuing opportunity for the entrepreneur to increase his earnings in absolute figures. Thus, decreasing key interest rates should significantly negatively affect corporations’ investment activity (H2A). However, at the same time, a company’s average return on capital should, ceteris paribus, decrease (H2B). The more low-margin projects a company pursues in times of decreasing interest rates, the lower the average return on capital. It is important at this point to strictly differentiate between average percentage returns and absolute returns. The latter would obviously increase as the company increases its total assets generating higher absolute returns.

The third hypothesis covers an impulse from the Keynesian school. Assuming that companies act comparably to investors, a certain interest rate level could exist at which they would also start to build up a speculative reserve. This assumption is logically traceable, since the continuously decreasing investment project margins would eventually lead to a level wherein the execution will no longer generate a positive return. Thus, there should be two alternatives, as previously discussed in the context of intermediary costs. The first would be to build up the speculative reserve and hoard the money as a bank deposit due to missing investment opportunities. The second would be to adjust the risk profile and choose riskier projects for execution. However, the second option is limited by the principal-agent relationship between the company’s management and shareholders. Therefore, the topic of risk preference alteration is discussed in relation to investors in order to utilize a direct rather than indirect relationship. The third hypothesis states that the key interest rate should significantly negatively influence a company’s cash position (H2C).

The hypotheses for the third group originate from the portfolio theory discussion. One of the assumptions based on the medial topics is that investors tend to significantly change their investment behavior in times of zero key interest rates (H3A). More specifically, investors tend to employ riskier investments to generate positive returns, and thus alternate their risk preference compared to a normal interest rate environment (H3B). Table 9 below contains the synopsis of the hypotheses conceptualization, as well as the respective chapters where their refinement occurs.

Table 9: Hypotheses Conceptualization Number Hypothesis Chapter Internal costs of a bank significantly negatively influence profit

H1A growth during periods of a low or zero key interest rate 3 environment.

Banks Key interest rates significantly negatively influence the growth H 3 1B of financial-intermediation-light industries.

Key interest rates significantly negatively influence a H 4 2A company’s investment activity. Key interest rates significantly positively influence the ratio of H 4 2B average returns to total assets. Key interest rates significantly negatively influence a

Corporations H 4 2C company’s cash position.

Investors significantly change their investment behavior in H 5 3A periods of a zero key interest rate environment. Investors allocate significantly more capital to riskier H3B 5 Investors investments in periods of a zero key interest rate environment. Source: Own representation.

Each of the named research hypotheses should be able to produce a valuable scientific contribution. However, it is their interrelation that is most interesting, and which was therefore chosen for discussion as the fourth sub-objective of this dissertation project. Due to the complexity of the market, the participants, and the (theoretically still not fully understood) operating principles, the ideal solution of empirically testing such an interrelation remains quite limited. Nevertheless, the research should provide some of the necessary puzzle pieces to solve the question of the direct lending market’s rapid growth.

3. Financial Intermediation in a Zero Interest Rate Environment This chapter is dedicated to refining the research hypotheses concerning the banking industry. After discussing the intermediary cost composition, the financial transaction model is reviewed from a customer’s perspective. The third subchapter focuses on the competitive environment in the German banking industry, leading into one of its contenders: the direct lending industry. Finally, the research hypotheses are reformulated and specified for operationalization purposes.

3.1 Components of Intermediation Costs Hypothesis H1A emphasizes the transaction costs theory in relation to the theory of the bank, stating that lower transactional costs in the trilateral governance set-up would lead to increased profit growth of the intermediary. To provide deeper insight into this hypothesis, the composition of the banking industry’s intermediary costs is analyzed in detail to identify particularly problematic fields of business during the zero interest rate environment.

3.1.1 Classical Frictions Resolved by Banks The bank itself can be identified as a result of unified governance implementation. To efficiently execute recurring transactions with an idiosyncratic specificity (e.g. lending and borrowing), a commercial organization—the bank—was established at an unknown point in human history. This organization can efficiently combine different functions at a specific level of costs. Examining standard banking management textbooks, two major function categories are usually differentiated. The first category consists of transformational functions, while the second focuses on transactional functions (cf. e.g. Tolkmitt, 2007, 4).

Banks feature three transformational functions that resolve market frictions. The first involves transforming lot sizes. Since capital suppliers and capital demanders will only in rare cases be able to match the respective amounts, the bank can fulfill its intermediary tasks by lot size transformation. The same concept applies to the maturity transformation, in which the bank resolves the friction of its customers’ different maturity preferences. The third function concerns risk transformation. Capital suppliers are, in most cases, uninterested in bearing default risks resulting from specific capital demanders. Instead, the bank becomes the contractual partner, and thus the liable money lender. All three functions require an active portfolio management to fulfill resulting obligations at any point of time (for more details on the transformational functions cf. e.g. Casu, Girardone, & Molyneux, 2015, 7). A bank’s transactional functions include the service of searching for and screening possible contractual partners, negotiating contracts, concluding contracts, and subsequently monitoring the counterparty (Tolkmitt, 2007, 4). These elements comprehensively reflect the previously

43 discussed transaction cost theory. Table 10 below presents a summary of the major transformational and transactional functions.

Table 10: Transformational and Transactional Functions of a Bank Transactional Functions Transformational Functions Screening services Lot size transformation Negotiation services Maturity transformation Execution services Risk transformation Monitoring services Primary purpose: Primary purpose: Transaction cost reduction Harmonization of money supply and demand Source: Own representation based on Tolkmitt (2007, 4).

Through economies of scale, the bank is able to provide its services at lower average costs than in a disintermediated set-up (Casu et al., 2015, 8). Typical costs resulting from both transformational and transactional functions include payroll (e.g. employees, external service providers), asset (e.g. buildings, technology), and other expenses (e.g. marketing). Another important position includes risk provisions resulting from the bank’s risk transformation function. Minimizing intermediation costs should, as in any other commercial institution, be related to typical management tasks, such as process optimization, cost variabilization or reduction, and especially risk management (Casu et al., 2015, 235). However, banks are also strongly influenced by external constraints (i.e. regulatory framework), which significantly affect internal costs (cf. e.g. Dia & VanHoose, 2018).

3.1.2 Regulatory Framework The finance and insurance industry is one of the most heavily regulated sectors in the USA. The number and reach of regulations particularly accelerated following the financial crisis, reaching new record levels every year (McLaughlin & Sherouse, 2019). Due to the lack of a scientific measurement method hitherto, a similar regulatory environment can at least be assumed for the European financial market based on recent developments. The Basel Committee on Banking Supervision (2010, 2017), for example, used Basel III and its finalization (better known as Basel IV) to provide two frameworks for banking regulation, which should help to avoid the next financial crisis. However, these frameworks initiated numerous follow-up initiatives as well as parallel framework developments by other supranational (e.g. ECB, European Banking Authority) and national (e.g. Bundesanstalt für Finanzdienstleistungsaufsicht, Deutsche Bundesbank, Fed) institutions. Thus, European regulation development should have accelerated in recent years as well. The complexity and variety of regulations impedes not only people outside the field in keeping track with compliance necessities, but also presents a challenging environment for banks. Figure 6 below illustrates an attempt to structure the current regulatory 44 landscape in Germany. The focus is on most recent developments, and although based on extensive research, no claim is made regarding completeness.

Figure 6: Regulatory Landscape in the German Banking Industry

Source: Own representation with elements from Pohl (2018, 9). Summarizing, the previously discussed ordinary costs of business are accompanied by industry- specific regulatory costs, which, in the case of the financial industry, significantly influence total intermediation costs (cf. e.g. Dahl, Meyer, & Neely, 2016, 1; Demirguc-Kunt, Laeven, & Levine, 2004, 593; Elliehausen, 1998, 23). To reduce overall intermediation costs, bank management should also focus on process and cost optimization in the context of regulatory tasks. This objective comprises the uninterrupted compliance with the regulatory framework, and is thus bounded by external governance.

The regulatory environment, however, not only causes expenses for compliance in general. Rather, authorities have also the ability to actively limit specific areas of business identified as potential future risks. By issuing additional regulations for a specific product, the overall costs of a contractual agreement can be brought to a level where the conclusion would no longer be viable from an economic perspective. A recent development offers an effective example of this practice. Specifically, capital requirement regulations generally aim at a risk-adequate provision of equity or equity-like instruments for issuing loans (cf. e.g. Bank for International Settlements, 2013, 10–11). Herein, the risk-weighted assets (RWA) mechanism determines that 45 loans with low risks require less equity capital than loans with high risks. Most recently, the ECB identified the growing business area of leveraged transactions as a possible cause of future crises (European Central Bank, 2019b, 8–9). Leverage transactions usually describe the financing process of merger and acquisition transactions. The company’s buyer can pay for the transaction by borrowing a high volume of debt (i.e. leveraged loan) through the purchased company (cf. e.g. Dänzer, 2010, 25–27; Reed, Lajoux, & Nesvold, 2007, 5). This area of business is already limited by high RWA requirements, resulting largely from non-investment- grade ratings (Deloitte, 2019, 31–34). Based on the ECB’s situation assessment, an additional guideline for leveraged transactions has recently been published, imposing additional reporting and controlling requirements on banks, making this area of business less attractive (European Central Bank, 2017). The possibility of forwarding the additional costs to customers is discussed further in Chapter 3.3.

3.1.3 Additional Costs Emerging from the Deposit Protection Scheme The previously discussed cost components of intermediation can, to a certain level, be influenced by bank management. Accordingly, increasing or decreasing process efficiency directly relates to the expenses. However, a cost component of intermediation also exists, the extent to which has largely been fixed by regulation. During the financial crisis, and later during the European debt crisis, authorities were confronted with decreasing trust in the financial system and the subsequent possibility of a bank run. To avoid further escalation, several declarations of intent (cf. e.g. the well-known declaration by Merkel, 2008) were followed by acts and regulations (especially Einlagensicherungsgesetz, 2017) ensuring deposit repayments to private customers to a certain level (i.e. deposit protection schemes). Today, four organizations in Germany would execute the Deposit Protection Act should a bank be unable to fulfill its payment obligations. In such a case, a mutual fund would be used to pay out the compensation of up to €100,000 or, in specific cases, €500,000 to every private bank customer. To build up those funds, every member of the respective deposit protection system has to pay contributions according to the by-laws (i.e. Association of German Banks, 2018; Bundesverband Öffentlicher Banken Deutschlands e.V., 2014; BVR Institutssicherung GmbH, 2019; Deutscher Sparkassen- und Giroverband e.V., 2018). These contributions are neither voluntary nor adjustable, and are consequently relationally equal for all participants of the respective market.

It can be summarized that intermediation costs are composed from fixed and variable components, that they are strongly affected by regulatory developments, and that management remains unable to identically influence all components. Universal banks in Germany are largely 46 all facing the same challenges. However, the initially formulated profit growth hypothesis remains valid. Banks with the lowest intermediary costs should be able to generate a higher profit growth than competitors due to this competitive advantage. However, examining net return growth ratios alone seems inadequate, since this would not account for the bank’s size or its business model. Furthermore, examining the risk-weighted assets alone would not account for the profitability growth. Thus, the growth of a ratio such as net returns to risk-weighted assets (RORWA; i.e. return on risk-weighted assets) would be more adequate as an instrument for performance measurement. Numerous empirical studies researching bank profitability have applied this or comparable ratios as dependent variables (cf. e.g. Athanasoglou, Brissimis, & Delis, 2008, 126; Dietrich & Wanzenried, 2011, 311). The internal costs also represent a typical determinant in these studies. Moreover, recent contributions were able to provide proof of their significance following the financial crisis (cf. e.g. Adelopo, Lloydking, & Tauringana, 2018,

394; Altavilla, Boucinha, & Peydró, 2018, 547–548). However, hypothesis H1A deviates from the available methodological approaches, thus requiring further empirical research.

3.2 Modelling Financial Transactions Using Utility Maximization Problem In the following, a simplified application of the utility maximization problem from microeconomics is employed to mathematically discuss the research hypotheses. The utility maximization problem states that a rational consumer will attempt to maximize his utility by choosing the most preferred consumption bundle with a given amount of wealth (cf. e.g. Mas- Collel, Whinston, & Green, 1995, 50–57). The consumption bundle can be described as a combination of different commodities at specific price levels (cf. Formula 1).

with: 푃 Prices (푃 ≫ 0) max 푢(푋) 푠푢푏푗푒푐푡 푡표 푃 ∗ 푋 ≤ 푊 (1) 푋≥0 푢(푋) Utility function 푊 Wealth level (푊 > 0) 푋 Commodities

The consumption bundle also includes financial services as commodities demanded by the consumer. As there is more than one financial service, the price determination can also be performed on a business area or product level. As previously discussed, the price of the specific financial service could be described by different intermediation costs. In Formula 2 below, these are represented by three components: the allocated business-related costs (i.e. payroll, assets, risk, and others), including overhead costs and an institute-specific margin; the cumulated regulatory-related costs allocated to the business area; and the allocated costs for the deposit protection funds, which are determined by the respective by-laws.

with:

푐푝푖푗 Allocated payroll costs 푐푝

푐푎푖푗 Allocated asset costs 푐푎

푐푟푖푗 Allocated risk costs 푐푟

푐표푖푗 Allocated other costs 푐표

푐푗 Costs of business area 퐽 regulation 푃푖푗 = 푐푝푖푗 + 푐푎푖푗 + 푐푟푖푗 + 푐표푖푗 + ∑ 휃푖푗 ∗ 푐푗 + 푐푓푖푗 (2) 푐푓푖푗 Allocated costs of deposit 푗=1 protection 푐푓 푖 Index of individual bank 푗 Index of specific business area (푗 = 1, … , 퐽)

휃푖푗 Binary multiplier for existence or non-existence of business areas

Two key findings can be derived from the presented formulas. The first is that a rational market participant would attempt to maximize his utility, and thus utilize short-term price differences on the market to reduce his commodity-related expenses. This procedure enables him to invest the free budget in other goods or services to increase utility. This finding is consistent with hypothesis H1A. The second finding is that universal banks should always lose in a regulated market. Assume that a specialized intermediator provides only loans to customers. On the one hand, he would lack expenses for the deposit protection scheme, and on the other hand, he would face significantly lower overhead regulation costs due to the specialization in one specific business area. Ceteris paribus, and if the universal bank is unable to compensate through regular business expenses, the price of the financial service provided by the specialized intermediator should be lower. Thus, the regulatory environment today should benefit specialized institutions and enable growth in intermediation-light (i.e. only a small portion of regulations is relevant) environments. This finding is consistent with hypothesis H1B.

The discussion so far has focused on services for which the consumer actually has to pay money. However, the arguments also work for a bank’s deposit business. Assuming that the consumer utilizes a bank’s interest payments to increase his wealth, the costs allocated to the product should directly influence his income. The lower the interest payment becomes, the less consumption is possible due to decreasing wealth. Moreover, if the bank charges the consumer for his deposits, the financial service gains an actual price. In any case, the consumer’s utility is also being reduced by the bank’s intermediation costs, and he would attempt, ceteris paribus, to maintain or increase his utility level given the circumstances. One possible solution would

48 be, as in the general case, to switch the intermediary to a specialized supplier. It would also be possible to increase risk to maintain the given level of wealth, which would correspond to the investor-related hypotheses.

3.3 Competitive Landscape in the German Banking Industry To analyze the consumer’s options for switching suppliers of a specific financial service, the following subchapter first presents an overview regarding the German banking industry. The second subchapter focuses on this industry’s margin development in recent years, followed by a brief analysis of disruptive market entrances that additionally burden the incumbents’ business development.

3.3.1 Three-Pillar System As of December 2018, there existed 1,783 banks with a total of 27,887 branches in Germany. Although these numbers have noticeably decreased in recent years (i.e. -9.0% in the number of banks and -18.1% in the number of branches since 2015), Germany still represents the European market with the most competitors (Deutsche Bundesbank, 2019a, 1, 5; European Banking Federation, 2019). The German banking system is divided into three major market participant groups (cf. e.g. Lütke-Uhlenbrock, 2007, 7–8). The first consists of the savings banks and their umbrella organizations (2018: 22.0%), while the second features cooperative banks and their umbrella organization (2018: 48.5%), and the third comprises all other banks (2018: 29.5%) which are either universal or specialized banks (Deutsche Bundesbank, 2019a, 1). This system is better known as the German three-pillar system (cf. Figure 7).

Figure 7: Three-Pillar System of the German Banking Industry

Source: Own representation. 49

Based on the numbers, there is no necessity to answer the question of whether a bank’s customer is, in general, able to switch suppliers if the prices are not in line with the market. As previously stated, Germany is the most competitive market for banks in Europe and has often been described as overbanked in general (cf. e.g. Hufeld, 2019, 15; Zielke, 2019, 18). Following up on the discussion regarding intermediation costs, banks that are unable to comply with the market should face difficulties regarding their earnings. This effect can particularly be assumed for small organizations that have to meet mostly the same regulatory requirements as their large competitors, and thus face significant cost impacts. The continuous market consolidation is primarily affecting those small- and medium-sized banks (cf. e.g. Schackmann-Fallis, 2019,

21–22). These findings support further pursuit of hypothesis H1A.

3.3.2 Margin Development in Zero Interest Rate Environment Beyond the intermediation costs, the feasible market margin also influences the market developments. As previously seen, the total number of market participants implies a high level of competition, which could also negatively influence margins. Additionally, in times of the zero interest rate environment, banks could decide to lower their margin rather than upset customers by amending the contractual rates for deposits. Examining the actual numbers, two surprising findings can be derived (see Figure 8). First, the net interest income in relation to the average balance sheet total (i.e. margin) demonstrates an overall stable development during the recent years of low key interest rates. Although the relative interest incomes and expenses have obviously decreased since 2007, the margin was already narrowed down to a level slightly above 1.0% at the turn of the millennium, where it has remained ever since (i.e. 1.1% on average). Previously, the average net interest income margin in relation to the balance sheet total equaled 1.9% (Deutsche Bundesbank, 2019e). Keynes (2018, 183) assumed that, regardless of the market circumstances, a bank would charge at least 1.5–2.0% to cover its expenses. According to the data from the German market, his hypothesis was valid for quite some time. However, the market customs changed prior to introducing the euro, remaining at a constant level ever since. The second surprising development can be seen since 2016. Here, the net interest income exceeded the interest expenses for the first time. The statistical data generally supports both research hypotheses, since customers could either attempt to mitigate deposit charges in the future and switch to other suppliers or even switch into an intermediation- light environment where the intermediary cost mechanism and the minimum margin requirements only apply to a certain extent.

Figure 8: Development of Net Interest Income of German Banks

Source: Own representation based on Deutsche Bundesbank (2019c, 2019d, 2019e). 3.3.3 Market Participants Utilizing Intermediation-Light Environments Turning next to the intermediation-light environments, the first necessary task involves concretizing the terminology. Up to this point, the discussion has primarily focused on universal banks offering their customers a broad variety of services. Assuming that each of those services could also be offered separately, and especially with a different range of intermediation services, presents a new view on the topic. Emphasizing the first assumption (i.e. the offering of specialized services), Formula 2, which describes the market price composition, could be reviewed. A market participant who offers only one service to the customer, for example point of sale financing, should, under ceteris paribus conditions, feature at least a lower value behind the sigma sign due to the lower overall regulatory costs. In this example, the costs for the deposit protection funds would be additionally eliminated in the formula due to the nonexistent deposit business (i.e. financing through a different source of funding). As a result, the components for price determination should be lower, and the specialized market participant should experience a competitive advantage compared to universal banks. The results so far are not surprising, as the topic of specialization into niche rather than broad mass markets is being repeatedly discussed in management literature (cf. e.g. Wheelen, Hunger, Hoffman, & Bamford, 2018, 207–209). Moreover, the German banking market already features a variety of specialized suppliers with a rather limited scope of products (e.g. Bergfürst Bank specialized on crowd investing; DSL Bank with its focus on real estate financing). 51

The second assumption (i.e. reduced intermediary functions as presented in Table 10) presents a more extensive implication. Assuming that market participants are able to not only specialize in a specific range of services, but can also eliminate specific intermediation functions, the competitive advantage should be even greater. Reviewing Formula 2 again, not only can the regulatory and, dependent on the business model, deposit protection costs be minimized, but the regular costs of business (i.e. payroll, asset, risk, and other costs) could also be partially eliminated through the transfer of functions. The following example should illustrate this hypothetical construct. Assuming that a company is specialized in mediating peer-to-peer contracts (e.g. loans, investment opportunities), the business cost components would differ compared to a universal bank. Payroll, asset, and other expenses would be determined by the mediation platform and not by the financial service itself, since this would be contracted between both counterparties. However, the most important aspect concerns the costs for the risk transformation, which are not present in this business set-up. The transformational function would be transferred to the counterparties, and thus does not comprise an element of price determination. According to this thought experiment, customers facing inadequate prices presented by a universal bank (e.g. charges for deposits) could utilize an alternative intermediation (or rather mediation) service if they are ready to bear the corresponding risks.

Shifting from the hypothetical discussion to the real world, the following analysis implies that those two are not too far apart. In recent years, more and more financial technology (Fintech) start-ups have entered the financial intermediation market in Germany (see Figure 9). Accompanied by media and scientific attention, several such start-ups, often called disruptive, have managed to gain noteworthy market shares (cf. e.g. N26, 2019; Reinig, Ebner, & Smolnik, 2018), or to at least influence the technical market development (on the impact of blockchain technology cf. e.g. Reuse, Frère, & Schaab, 2019). The original meaning of the term disruptive describes a situation in which a company is able to penetrate the least profitable market or customer segment with a new or adjusted—and especially profitable—product. By continuously gaining market shares, the product becomes mainstream, and thus relevant for other customers over time (Christensen, Raynor, & McDonald, 2015, 48–51). Two start-ups— wikifolio and auxmoney—are pointed out in the following as specific examples of disruption in the German financial sector.

The platform wikifolio enables issuing financial certificates based on individually composed trading portfolios. The issuance can be performed by anyone capable of collecting investment commitments of €2,500 or above (wikifolio, 2019). Prior to the company’s market entrance,

52 this field of business was reserved for a selected range of market participants (e.g. banks), and thus inaccessible for people who, despite having good investment ideas, could not afford to provide the necessary capital for an issuance. Summarizing, wikifolio aimed at an, at that time for banks, unprofitable market with an adjusted product, creating a peer-to-peer platform that is not subject to usual intermediation costs. Banks are currently striving to catch up with their own solutions (e.g. Wunschzertifikat).

Founded in 2007, auxmoney specializes in mediating peer-to-peer loans. Comparable to wikifolio, auxmoney also aimed at a market segment previously reserved for financial intermediaries or organized on a local or private level. The business idea explicitly aims at eliminating banks as financial intermediaries through the created platform economy (auxmoney, 2019). Accordingly, the prices for investors and lenders follow different rules (e.g. no risk transformation costs) than those of a universal bank.

Both companies offer successful examples of the emerging platform economy (for a detailed discussion on multisided platform economies cf. Evans & Schmalensee, 2016). However, not all Fintech start-ups are automatically successful. Instead, the numbers of bankruptcies have also heavily increased since 2017, reflecting the challenging competitive environment (see Figure 9).

Figure 9: Fintech Start-Ups and Bankruptcies in Germany

Source: Own representation based on comdirect (2018, 9), PwC (2019, 8).

Table 11 presents the largest German Fintech fundings in 2018. Leaving the already noteworthy funding volumes aside, four of those five companies focus on developing platforms without providing their own banking solutions. For example, Deposit Solutions, with its two subsidiaries, focuses on connecting savers with banks offering the best interest returns on short- to long-term deposits. Liqid provides a platform for bundling capital, thereby enabling investments into asset categories that would otherwise be inaccessible (e.g. top-level private equity funds). This approach also corresponds with the disruptive strategy by focusing on accumulating small customers that, considered for each other, would otherwise be unprofitable for the counterparty. Concluding, Fintech companies present a possible opportunity for dissatisfied customers of universal banks to pursue an alternative option. However, as previously discussed, the customer has to decide whether the risks in relation to the gains are appropriate and worth taking.

Table 11: Top 5 Fintech Funding Deals in Germany in 2018 Name Segment Funding Volume N26 Banking & Lending €130m Deposit Solutions Banking & Lending €88m solarisBank Enabling Processes & Technology €57m Smava Financial eMarketplaces €55m Liqid InvesTech €33m Source: Own representation based on Ernst & Young (2018, 5). Having discussed universal banks, specialized banks, and Fintech start-up companies, there remains one group of competitors to emphasize. This group comprises financial or non- financial incumbents that are horizontally, vertically, or even laterally developing their own business model and, in doing so, entering the classical banking environment. Focusing on non- financial companies first, Germany’s automotive industry in particular presents an effective example of this process. Most of the automobile manufacturers decided at some point to vertically integrate their business model and established their own bank for sales financing (e.g. BMW Financial Services, Mercedes-Benz Bank, Volkswagen Bank). Since these banks are not only providing loans for car purchases, but are also offering deposit accounts and other financial services, the competitive landscape is negatively impacted. Examining financial companies next, the previously briefly mentioned market entrance of insurance companies exerts a comparable effect on competition. By offering mortgage loans to retail customers directly, insurances are attempting to take market shares away from banks. Both mentioned groups, however, are exposed to comparable regulatory frameworks as universal banks, and would thus not directly qualify for the intermediation-light peer group.

Reviewing the results, most of the challengers capable of winning market shares from incumbents are focusing on the retail market. During the research, however, one specific finding also presented an interesting development in the wholesale business. In 2013, private debt funds entered the German market and began offering loans in leveraged transactions. Figure 10 illustrates that, since then, the private debt funds have managed to conquer more than 50% of market shares in the German mid-cap market (i.e. leveraged financing between €20m and €500m according to GCA Altium (2019, 39)).

Figure 10: Market Share of Debt Funds and Banks in the German Mid-Cap Market

Source: Own representation based on GCA Altium (2019, 12). As previously discussed, this business area is heavily regulated for banks. Private debt funds, on the other hand, can benefit in this market in two ways. First, as specialized investment funds (SIF), they are only exposed to a small fraction of the regulatory costs (for a current assessment of European private debt fund regulation cf. Smith, Volhard, Davies, Maugüé, & Paruzzolo, 2017; for a different regulation approach on the US market cf. Judge, 2015). Second, the fund itself is not inhering the risk transformation function, and accordingly also features lower business operation costs compared to a bank.

The regulatory set-up, combined with the market development since 2013, presents an unparalleled research opportunity. First, the niche market corresponds to the previously discussed hypothetical implications of intermediation costs during the zero interest rate environment. Herein, private debt funds qualify as intermediation-light competitors (i.e. lower regulatory expenses, no risk transformation expenses). Second, the lending business is more

55 transparent than most of the financial services provided by banks or other financial intermediaries, thus enabling statistical hypothesis tests. Therefore, the topic of private debt funds or rather direct lending is subject to a detailed discussion in the following subchapters and has been chosen as a topic for narrowing down hypothesis H1B.

3.4 Direct Lending Since private debt funds represent a still young industry in Europe, their scientific foundation remains mostly negligible. Therefore, the first step of discussion requires defining the private debt term as well as providing an overview of European developments. In the second step, direct lending is assessed from an asset allocation perspective. Finally, the current status quo of empirical research on the German market is briefly assessed in the third subchapter.

3.4.1 Definition and European Development The terms private debt, direct lending, peer-to-peer lending, and crowd lending are frequently employed in today’s financial media contributions (cf. e.g. Kühn, 2019). Although implied to represent different forms of financing, they largely describe the same facet. For the purposes of the following discussion, these terms are subsumed under the term direct lending and defined as follows: Direct lending describes the process in which a lender provides funds to a borrower through a non-capital-market channel and in which the risks of default are not transferred to an intermediary in-between. The funds disbursement and subsequent repayments can be realized directly between the counterparties or indirectly by utilizing platforms or special purpose vehicles. Figure 11 below illustrates the definition and differentiates it from classical intermediated relationships.

Figure 11: Comparison of Direct Lending to Classical Financial Intermediation

Source: Own representation. 56

Direct lending can manifest itself on different scales. The differentiation could be undertaken based on the borrower’s characteristics, who nowadays is either a private person or a company. However, this differentiation is not exhaustive, since it is possible for governments, public institutions, and other market participants to utilize direct lending in the future. Due to the direct lending market’s lack of transparency for individuals, the following overview of the European market emphasizes the direct lending market for corporations. The focus, however, remains on participations of funds as a lender party in leveraged transactions, and explicitly does not cover the issuance of promissory loan notes (i.e. Schuldschein) due to banks’ involvement as arranging parties.

According to Nesbitt (2019, 167–172) and Ares Management (2018, 10), the European corporate direct lending market follows the example of the U.S. market, with a delay. In the U.S., the direct lending market was already established before the financial crisis due to consolidation tendencies in the banking industry and subsequently diminishing lending capacities. Today, direct lending represents a mainstream financing instrument accepted and demanded by corporate borrowers, represented by specialized indices (e.g. CDLI), and accounting for a market share of approximately 91% of the leveraged loan market (Ares Management, 2018, 7). The European direct lending industry began its strong growth in 2013 in almost every nation. As of December 2018, with 184 completed direct lending deals, Germany ranked third place, following the UK with 664 deals and France with 444 deals (Deloitte, 2019, 6–7). In general, the previous market estimations correspond with the trend illustrated in Figure 10. Started slowly, direct lending accelerated in recent years, gaining more than half of the market shares from banks. The estimated European direct lending market size per 2018 equals €150b, largely covered by approximately 55 fund managers (Nesbitt, 2019, 170).

Summarizing, corporate direct lending in Europe is in the process of catching up with the U.S. example. The intermediation-light business model could therefore be perceived as successful; scientific verification of this hypothesis, however, has yet to be performed.

3.4.2 Direct Lending in the Context of Asset Allocation Asset classes are usually defined as a group of financial instruments possessing homogeneous characteristics in relation to returns and risks (cf. e.g. Nesbitt, 2019, 57). Cash, bonds, and stocks comprise universally accepted asset classes. However, real estates, commodities, foreign currencies, private equity, and hedge funds could also be considered their own asset classes (cf. e.g. Basile, 2016, 327–328). In this context, the question arises as to whether direct lending

57 could represent its own asset class for utilization in an asset allocation strategy. Following the above definition, investments in direct lending would face similar risk and return propositions. On the one hand, the investor would be exposed to a counterparty default risk, while on the other hand, he would have the opportunity to gain an additional yield compared to syndicated loans and publicly traded bonds (Nesbitt, 2019, 80–84). Thus, credit in a general sense and direct lending in a narrow sense could be considered their own asset class for investors.

With this in mind, formulating a specific research question, and thus refining hypothesis H1B, is quite difficult. The researcher has to assess whether market participants are investing into a new asset class because of its availability and its characteristics (i.e. return and volatility correlations) or because of the otherwise non-existent opportunity to generate yields in a zero interest rate environment. The idea of asset allocation in general is discussed in Chapter 5.1, and the investment-related hypotheses H3A and H3B should provide an approach for solving the above-mentioned question.

3.4.3 Status Quo of Empirical Research Unfortunately, empirical research on the topic of corporate direct lending in Germany does not yet exist. Even though the market started growing in 2013, the delay period for scientific publications seems to be longer than six years. Checking for combinations of private debt, direct lending, peer-to-peer, and crowd lending with the keyword Germany in both German and English produced zero relevant findings in pertinent (i.e. SCOPUS, EBSCO, SSCI) and meta databases (i.e. DigiBib of FernUniversität in Hagen). At least the databases demonstrated first contributions on individual direct lending in Germany (cf. e.g. Barasinska & Schäfer, 2014), though these remain scarce resources. One descriptive study by Bundesverband Alternative Investments e. V. (2019) could be found during search engine research. However, this does not provide additional insights for the discussed topic.

3.5 Hypotheses Refinement – Banks Reviewing hypothesis H1A following both the deep dive into the competitive landscape of Germany’s financial industry and the discussion regarding general implications of intermediation costs results in requiring marginal to no alterations. The marginal adjustment would be the discussed specification of the profit-growth variable. This dependent variable should, for logical reasons, comprise a ratio between earnings and risk-weighted assets (i.e. RWA). The intermediation costs as major determinant should thus also be represented by relational variables and accompanied by different control variables to provide reliable research results.

Hypothesis H1B, on the other hand, presents a challenging problem following the discussion regarding different intermediation-light market participants and their specific drivers. The question thus concerns whether it would be possible to directly relate key interest rates to the growth of an intermediation-light market niche. Even so, it must then be determined whether it would be possible to solve the endogeneity problem in a scientifically adequate manner. The answer is no, since the complexity of interrelations between determinants leading to a specific market development limits feasible research. Therefore, the answer to the conceptualized hypothesis H1B would be desirable, but not achievable. Of course, this assessment of the limitations calls into question what would be scientifically possible. It would be possible to test the significance of corporate direct lending market shares over time. The variable for comparison, however, should not be the total market for leveraged transactions, as banks and private debt funds are already at eye level in 2018 (cf. Figure 10). Instead, the market should be the total corporate lending market in Germany, on the one hand, and the total market for investments on the other. With this approach, it should be possible to determine whether or not this specific intermediation-light competitor already managed to conquer a significant market share. Hypothesis H1B is thus adjusted accordingly. Investors’ behavior in a zero interest rate environment, on the other hand, is postponed to Chapter 5. Table 12 below presents the finalized bank-related hypotheses for the dissertation project.

Table 12: Refinement of Bank-Related Hypotheses Number Hypothesis Internal costs of a bank significantly negatively influence growth (i.e.

H1A growth of return on risk-weighted assets) during periods of a low or zero key interest rate environment. Corporate direct lending in leverage transactions achieved a significant

Banks H1B market share compared to the total corporate lending market and the total investment market in Germany. Source: Own representation.

4. Corporate Finance in a Zero Interest Rate Environment This chapter is dedicated to refining the research hypotheses regarding corporations. After discussing investment decisions under uncertainty, the net present value approach, and the weighted average costs of capital, the financing sources are then reviewed from a company’s point of view. The third subchapter is dedicated to analyzing accounting-implied alterations of balance-sheet positions in the zero interest rate environment. Based on this analysis, the fourth subchapter presents a selection of specific financials for the subsequent research approach. The fifth subchapter presents the refinement of corporation-related research hypotheses.

4.1 Investment Decisions under Uncertainty The necessary preparatory step for further discussing how the zero interest rate environment affects corporate finance involves more closely examining the major drivers of investment decisions. Decision-makers in corporations are generally exposed to an imperfect market, and thus face uncertain future developments. Corresponding to new institutional economics, these uncertain future developments are largely rooted in information asymmetries, transaction costs, and other market imperfections (Ménard & Shirley, 2008, 1–2). Standard literature on financial management has offered different quantitative and qualitative instruments to substantiate decisions in this uncertain environment. What most of them have in common is the net present value (NPV) approach combined with probability weightings (cf. e.g. Block, Hirt, & Danielsen, 2017, 380–435; Brigham & Houston, 2019, 386–438). Herein, projects are evaluated from two perspectives. First, expected future cash flows from the respective project are discounted with an internal rate of return (IRR) and confronted with the initial investment costs necessary to execute the project (see Formula 3). If the NPV is positive, the project is able to earn the IRR, the initial costs, and thus is, from a general perspective, worth pursuing. The second perspective includes the probability of occurrence, stress testing of the cash flow assumptions, and the general business framework (e.g. strategy, resources). If, after assessing the possible opportunities and risks, the expected NPV remains positive and the best given alternative, the project should be pursued (Block et al., 2017, 418–435). As the IRR has the highest relevance for the present discussion, the other factors will be considered as constants in the following.

with: 푇∗ ∗ ∗ 퐶퐹푡 퐶퐹푡∗ Cash flow in period 푡 (3) 푁푃푉 = −퐼0 + ∑ ∗ (1 + 퐼푅푅)푡 퐼 Initial investment 푡∗=1 0 푡∗ Index of periods (푡∗ = 1, … , 푇∗)

The IRR represents either an output value from a project given initial investment and future cash flows (i.e. IRR in which NPV equals zero) or a determined minimum value that must be

60 earned through the future cash flows. The second perspective in particular is employed for the initial decision of pursuing or abandoning a project. The specification of the target IRR can, in general, be accomplished using different instruments. In practice, however, utilizing the weighted average costs of capital (WACC) as a minimum threshold IRR has been established as state of the art (cf. e.g. Block et al., 2017, 389). The WACC are calculated by weighting return expectations of equity capital providers and those of debt capital providers (cf. e.g. Brigham & Houston, 2019, 359–360). The return expectation of debt capital providers equals the interest rate on debt capital. This weighting factor is additionally multiplied with a tax shield component resulting from the deductibility of interest rate expenses from the project’s net earnings (cf. Formula 4). Summarizing, the WACC represent the minimum value that must be earned to satisfy debt and equity capital providers.

with:

퐷푀푉 Market value debt 퐸푀푉 Market value equity 퐸푀푉 퐷푀푉 Costs of debt capital 푊퐴퐶퐶 = 푟 ∗ + 푟 ∗ ∗ (1 − 푠) 푟퐷 (4) 퐸 푇퐶 퐷 푇퐶 푀푉 푀푉 푟퐸 Costs of equity capital 푇퐶푀푉 Market value total capital 푠 Tax rate (1 − 푠) Tax shield

The WACC formula features equity costs as an important component. However, it remains unclear what the equity costs are and how they are determined. Based on a project’s risk assessment, the risk-averse provider of equity capital would possess subjective expectations regarding yield. The capital asset pricing model (CAPM) is usually employed as an instrument to objectively quantify these expectations. Accordingly, the equity provider should earn the risk-free market interest rate and an adequate risk premium from the investment (cf. Formula 5). The risk premium is calculated by multiplying the market portfolio yield above the risk-free interest rate with the beta factor, which represents the correlation between the market portfolio and the specific valuation asset (Perold, 2004, 16). The equity capital provider receives only a premium resulting from systematic risks, as the theory assumes that any unsystematic risk can be diversified away (cf. e.g. Singal, 2017b, 375).

with:

푟푓 Expected return of risk-free asset 푟 = 푟 + (푟 − 푟 ) ∗ 훽 (5) 퐸 푓 푚 푓 푟푚 Expected return of the market 훽 Coefficient of idiosyncratic correlation with market portfolio

The link between the above explanations and the zero interest rate environment is possible through a logical chain. As previously discussed, decreasing key interest rates should result in decreasing market and, subsequently, risk-free interest rates through the transmission mechanism. Under ceteris paribus conditions, the return expectation of the equity capital provider should also decrease (see also the illustration in Figure 5). Following the impact to the WACC formula, the discount factor should also decrease as the weighting component for equity capital grows smaller. The described effect manifested itself in this manner during recent years (KPMG, 2018, 19, 22, 27). The important impact for the present discussion can now be seen in the NPV calculation. With a decreasing discount factor, the present value of future cash flows is, ceteris paribus, increasing. Therefore, less cash flows are required to earn the initial investment costs on the one hand and the necessary WACC on the other. Accordingly, it would be opportune for company management to pursue investment projects with lower profitabilities. These considerations are consistent with the theoretical discussion in Chapter 2.3 and with hypotheses H2A (i.e. increasing investment activity) and H2B (i.e. decreasing overall yield on total assets).

4.2 Financing of an Investment Decision The funding of a positive investment decision is, from a company’s perspective, one of the most important following steps. Details regarding structure, maturity, securities, and funding costs have to be considered, but furthermore, the funding source has to be determined in general. Capital structure theory offers different approaches for the consideration of investment funding, as is the focus of the following subchapter. Subsequently, the specialties of financing a leveraged transaction are placed into the spotlight, as this also represents an investment decision by a sponsor (i.e. new shareholder of a company).

4.2.1 Capital Structure Theories Despite the scope of scientific literature and research, a common consensus regarding the optimal capital structure of a company or project has yet to be found (Myers, 2001, 81). The following discussion thus focuses on the major schools of thought that emerged after Modigliani’s and Miller’s (1958) irrelevance theorem.

Modigliani and Miller (1958, 291) were able to demonstrate that both a company’s value and the average costs of capital are independent from the capital structure due to the market’s arbitrage function. Based on this result, the question of the optimal financing structure becomes irrelevant (i.e. irrelevance theorem). However, in order to prove this, the premises of the perfect market had to be supplemented by further restrictions (e.g. the existence of a single interest rate on investment and borrowing). The projection of the model on reality was thus impossible. However, the two authors deliberately opted for this form of presentation, on the one hand to illustrate the premises under which the capital structure would actually be irrelevant, and on the other hand to initiate further research projects in the scientific community (Miller, 1988, 99– 102; Modigliani & Miller, 1958, 296).

Initiated by Modigliani and Miller (1963) themselves, the classical trade-off theory represented the first approach to theoretically explain a company’s capital structure. Herein, a mixed- financed company is, due to the tax deductibility of interests paid on debt, able to make higher distributions to its capital providers than a purely equity-financed company, which has to subject all profits to income tax. Consequently, the enterprise value of a mixed-financed company should be higher (Modigliani & Miller, 1963, 438–439). In conclusion, it would also be opportune for shareholders to maximize the company’s debt ratio.

The discrepancy between these theoretical assumptions and reality required introducing a further model variable. This variable has been identified by several researchers as the company’s bankruptcy risk. For example, the lenders’ willingness to provide further funds decreases with an increasing debt ratio, resulting in rising interest rates. A company’s optimal capital structure is thus reached in classical trade-off theory when the marginal utility of the tax advantage corresponds to the marginal cost of insolvency risk (cf. e.g. Chen & Kim, 1979).

The assumptions of the classical trade-off theory could not be reconciled with empirical studies based on it (Miller, 1977, 266; Myers, 1984, 589). Therefore, scientists developed the integrated trade-off theory, which extended the classic approach with the non-debt tax shield and agency costs as two essential components. The tax deductibility of debt costs requires company profits to at least exceed tax credits and non-cash expenses (especially depreciation). The tax advantage, and thus the incentive to finance the company through debt capital, would therefore be substituted by the amount of an existing non-debt tax shield or completely eliminated in the event of a loss (DeAngelo & Masulis, 1980, 26–27; Downs, 1993, 576). Meanwhile, the development of the principal agent theory also allowed agency costs to be linked to the capital structure theory. The goal divergence between principal and agent induces measures to reduce 63 information asymmetries (cf. Table 8). The predominant carrier of costs for these measures involves either the equity capital provider in case of a high equity ratio or, vice versa, the debt capital provider. As a result, the capital structure optimum can be achieved if the total agency costs of equity and debt capital providers can be minimized (Jensen & Meckling, 1976, 333– 351).

The dynamic trade-off theory does not provide an independent model for the optimal capital structure. Instead, it presents an explanation of why the postulated capital structure of classical trade-off theory cannot be directly observed in practice. Therefore, the theory is extended by the factor of time, taking into account funding variability. Deviation from the optimal capital structure can be explained as a result of exogenous shocks on which a reaction was not yet possible or where the adjustment has not yet been completed due to the associated transaction costs. Accordingly, a company would be outside the optimal capital structure for as long as the cost advantage resulting from the adjustment is smaller than the associated costs (cf. e.g. Lev & Pekelman, 1975, 75). That a continuous adjustment process for the optimal capital structure actually occurs in reality has been proven in several empirical studies via the partial adjustment approach (cf. e.g. Jalilvand & Harris, 1984; Taggart, 1977).

The pecking order theory was established based on the discrepancy between the model- theoretical assumptions of the trade-off theory and the observed company-specific capital structures in reality. The pecking order theory rejects enterprise-value maximizing, and thus optimal capital structure. Instead, the model focuses the explanation of the financing behavior taking into account the management acting in the current shareholders’ interest. Accordingly, investment projects financed by issuing new equity capital (i.e. capital increase) would only be realized if the project’s value contribution is able to offset the dilution of the current shareholders’ share in the company. This raises the problem that, on this basis, only a small proportion of the planned investments would be realized, but this can be mitigated by the company’s sufficient internal financing power. The use of debt capital also does not affect the shareholders’ position to the extent of a capital increase, so a pecking order can be derived from these assumptions. Accordingly, the company management would first access internal funds and then ask debt capital providers for funds before proceeding with an equity capital increase (Myers, 1984, 581). However, though the pecking order theory is consistent with different effects seen on the capital market (e.g. negative price reactions after the announcement of equity capital increases), it still cannot explain all of its processes. This includes, for example, the

64 issuance of new shares, although other forms of financing are available, or increasing the debt level to finance a share buyback (cf. e.g. Myers, 1993, 8).

The discussion has so far focused on assumptions regarding trade-off and the pecking order theory. Going further, the signaling theory (cf. Leland & Pyle, 1977; Ross, 1977) could also be pointed out. This assumes that the company’s capital structure represents its capacity to pay back liabilities. Managers would not borrow money and risk their jobs if they knew that the company could not pay back the debt. Therefore, debt should signal strong profitability. Of course, still other approaches exist as well, such as the market strategic theory (cf. Brander & Lewis, 1986; Titman, 1984), the inertia theory (cf. Welch, 2004), or the persistence theory (cf. Lemmon, Roberts, & Zender, 2008), which consider an optimal capital structure. However, in contrast to the trade-off, pecking order, and signaling theories, these approaches have yet to reach comparable mainstream recognition, as these are usually not included in standard literature (cf. e.g. Brigham & Houston, 2019, 495–502; Quiry, Dallocchio, Fur, Salvi, & Vernimmen, 2018, 601–611). Therefore, they are not discussed in greater detail here.

Projecting the capital structure theories on the conceptualized hypotheses, hypothesis H2C in particular (i.e. the key interest rate’s impact on the cash position of a company) should be discussed controversially. The trade-off theory implies that a company would increase its debt- capital ratio to some extent due to the decreasing market interest rates and subsequent decreasing bankruptcy risks. This behavior would also be consistent with the signaling theory, since management would signal company profitability by continuing to finance projects with debt capital. Table 13 below enables reviewing these assumptions using empirical data from the syndicated loan market in Germany. The constant market volumes since 2015 indicate that corporations continuously utilized debt capital as a source of financing during the zero interest rate environment. However, the total volume did not increase in recent years, which would contradict the trade-off theory implication. Furthermore, the constant median terms since 2011 also indicate an absence of excessive exploitation of this capital source, instead implying a stable lending environment. However, neither the trade-off nor the signaling theory provide any implications regarding a company’s cash position in times of decreasing market interest rates. The pecking order theory even implies that the cash position should decrease, since the company would first utilize internal funds for investments before accessing debt or equity capital. Schneider’s (2010, 155–161, 284–295) meta-analysis provided evidence for the empirical dominance of the pecking order theory, which would also support the assumption of decreasing cash reserves.

Table 13: Volumes and Terms of the Syndicated Loan Market Germany 2001–2017 Number of Volume Mean Term Median Term Year Transactions (in billion €) (in months) (in months) 2001 40 46.129 59.83 60 2002 47 59.100 55.57 60 2003 55 75.977 53.69 60 2004 97 94.256 58.03 60 2005 120 126.740 72.26 74 2006 153 152.181 73.88 72 2007 114 119.160 69.55 72 2008 58 45.749 74.60 60 2009 42 59.113 42.05 36 2010 109 81.662 49.41 48 2011 160 80.053 67.38 60 2012 159 74.723 57.29 60 2013 190 106.780 55.57 60 2014 215 141.861 54.49 60 2015 217 125.520 58.46 60 2016 192 124.019 57.58 60 2017 193 121.384 57.58 60 Source: Own calculations based on dataset from Schaab, Frère, and Zureck (2018).

The question is thus raised as to whether reformulating hypothesis H2C would be appropriate. Examining capital structure theories alone, the answer should probably be yes. However, the derivation of this hypothesis was originally based on Keynes’ model of the speculative reserve. Herein, the company would not surrender the liquidity advantage when the return on investment is lower than a specific threshold. Assuming, that a company can reach a state in which the internal funds or the cash position exceed the investment opportunities with such a threshold return, the pecking order theory would be in accordance with Keynes’ assumptions. In this state, the cash position should, ceteris paribus, increase due to the available and subsequently retained excess cash flow assumed in a zero interest rate environment (e.g. through decreasing interest expenses). Since this assumption presents no contradiction with trade-off or signaling theory, hypothesis H2C is tested with the previously defined effect. In general, there are also no contradictions resulting from capital structure theories regarding hypotheses H2A and H2B, which are therefore retained in their current formulation. Additionally, the development of debt capital in a zero interest rate environment presents an interesting topic worth further pursuing.

4.2.2 Financing Sources in Leveraged Transactions Even though capital structure theories provide divergent statements for utilizing financing sources for investments, debt financing remains the most important funding cornerstone in leveraged transactions (see the definition in Chapter 3.1.2, and for a case study on the financing of such transactions, cf. e.g. Schaab & Frère, 2019). The point to review concerns which advantages or disadvantages could result for the sponsor or the company due to choosing an alternative lender rather than a bank. 66

Deloitte (2019, 29) presented a detailed analysis on this topic. Herein, the key advantages from choosing an alternative lender include faster execution speed, greater structural flexibility, cost- effective simplicity, the offer of a one-stop solution, and most importantly, the financing scale compared to a classical bank loan. Alternative lenders are willing to accept greater risks by providing unitranche financing, which blends debt with mezzanine capital, thus offering a greater leverage possibility for the transaction sponsor (Deloitte, 2019, 36). However, statistical data indicates that the alternative lenders’ approach is not exclusively performed in leveraged transactions. Instead, the aforementioned advantages are also utilized by companies that are not subject to a merger and acquisition transaction (Deloitte, 2019, 37). The downside of these funding sources includes the significantly higher margin on the one hand and the limited possibility of alternative lenders for providing revolving credit facilities on the other (Deloitte, 2019, 29, 32, 36). From a sponsor’s perspective, utilizing alternative lending sources would always be opportune, since the debt ratio, and thus the leverage effect, can be increased (cf. e.g. Hutzschenreuter, 2015, 109–110). Accordingly, the necessary equity contribution would either be lower or the sponsor would be able to pay a higher price for the target company due to overall increased funding. From a borrower’s perspective, the advantages have to be weighed against the higher price of money and the limited flexibility in short-term borrowing. As practice indicates, these are no insurmountable hurdles for implementing financing provided by an alternative lender. Although not primarily intended, the results of the discussion present additional support for hypothesis H1B.

4.3 Key Interest Rate Impacts on Accounting Practices Preparing an empirical study analyzing stock-listed companies applying the International Financial Reporting Standards (IFRS) or the International Accounting Standards (IAS) requires a preliminary analysis to mitigate a biased research set-up. The question to answer now becomes whether alterations of key interest rates automatically alter the valuation of different balance sheet, profit and loss, or cash-flow positions. A short comparison against the German Generally Accepted Accounting Principles (GAAP) is performed following this analysis.

4.3.1 Impact Analysis Focusing the Balance Sheet Starting with the analysis of the asset side of the balance sheet, the topic of initial and subsequent measurement has to be discussed first. The initial recognition of assets is based on the cost model, which considers acquisition or production costs for initial asset measurement (Müller & Saile, 2018, 30–34). On the other hand, for subsequent measurement, the company can utilize two different methods. If opting to remain in the cost model, the asset is depreciated or amortized over the period of its useful life. The company, however, also has to perform 67 impairment tests on a yearly (i.e. intangible assets where period of useful life cannot be determined) or incident-related (i.e. internal or external indications) basis and compare the carrying amount (i.e. current book value) against the recoverable amount. The recoverable amount equals the higher of value-in-use or fair value less costs to sell. The value-in-use is determined by calculating the asset’s present value (IAS 36.6; Müller & Saile, 2018, 34–42). The second method for subsequent measurement concerns the revaluation model. Herein, the company can revaluate assets based on their current fair value. The fair value is determined with a hierarchy mechanism starting from market prices going over comparable transactions to internal present value determination (IFRS 13; Müller & Saile, 2018, 42–50).

In both subsequent measurement methods, the company has to calculate the present value based on the circumstances. Since the recommended procedure for the discount factor includes utilizing the WACC, key interest rate alterations could directly influence accounting (IAS 36.55–36.57, 36.A15–36.A21). As the WACC decreased in practice primarily due to the decreasing risk-free interest rate (KPMG, 2018, 19, 22), the present value in impairment tests, or as last instance in the fair-value determination, should, ceteris paribus, increase. Thus, the company’s exposure to unscheduled depreciation or amortization should decrease accordingly. Table 14 below presents an overview of the asset-side positions that could possibly be affected by an interest-rate alteration. In total, the effect on the asset side of the balance sheet should be negligible, as it would primarily mitigate unplanned depreciation and amortization in the value- in-use assessment. On the other hand, the present value represents the final instance of the fair- value determination and would only be applicable in a minor number of cases. Thus, utilizing balance sheet positions from the asset side in the empirical study seems, nevertheless, to be appropriate.

Table 14: Key Interest Rate Impacts on Assets in IFRS Accounting Impact by Key Asset Comment Standard Interest Rate Intangible Assets  IAS 38 (Investment) Property,  IAS 16, IAS 40 plant and equipment Assets could be affected Inventories  through subsequent IAS 2 measurement utilizing Investments at-equity  present-value or fair-value IAS 7, IAS 17, IAS 19, IAS Trade and other  assessment (i.e. decreasing receivables 27, IAS 28, IAS 32, IAS 39, discount factor). IFRS 2, IFRS 4, IFRS 9, Cash and cash equivalents  IFRS 10, IFRS 11 Other financial instruments  Source: Own representation based on International Accounting Standards Board (2017), Müller & Saile (2018).

Examining the liabilities side next, IFRS accounting presents a more complex framework. Herein, several liability positions (i.e. financial liabilities, trade and other payables, other liabilities) are initially and subsequently measured at amortized costs (Hans Böckler Stiftung, 2017, 12; IFRS 9.4.2). Others (e.g. derivatives) are exposed to a fair-value measurement that could also consider a risk-adjusted market interest rate for cases in which the liability’s maturity exceeds a period of 12 months (Müller & Saile, 2018, 179–180). For provisions, IFRS suggests a best estimate value, which could include different applicable interest rates, thus making its calculation complex and not directly verifiable (IAS 37.36–37.47). Furthermore, pension provisions are being discounted with a specific discount factor based on actuarial derivation (IAS 19.78). The total equity is also affected by the other comprehensive income position, which could be a result of value-in-use or fair-value valuation on the asset side, and accordingly a result of key interest rate adjustments. The lack of transparency and the complexity of valuation approaches complicates reliable selection of variables (see Table 15). However, at least the financial liabilities could be utilized in the research design without considering key interest rate implications.

Table 15: Key Interest Rate Impacts on Liabilities in IFRS Accounting Impact by Key Liability Comment Standard Interest Rate Other comprehensive income Equity  position alterations are based on IAS 32 subsequent asset measurement. Amount is based on a market interest Pension provisions  IAS 19 rate dependent discount factor. Application of best estimate Other provisions  measurement which also could IAS 37, IFRS 2 include present value determination. Leasing and financial IAS 17, IAS 39, IFRS liabilities  9, IFRS 16 Application of cost model. Trade and other IAS 12, IFRS 9 payables  Dependent on the liability, the application of fair value measurement Other liabilities  /  IAS 12, IFRS 9 (incl. risk-adjusted market interest rates) could be necessary. Source: Own representation based on International Accounting Standards Board (2017), Müller & Saile (2018). 4.3.2 Impact Analysis Focusing the Profit and Loss Statement The profit and loss statement (IAS 1.81–1.99) in IFRS accounting presents fewer points of contact with the key interest rate compared to the balance sheet. The general approach is to measure positions of the profit and loss statement using actual transaction prices (IFRS 15.46). Thus, discount rates play only a subordinated role in situations in which a transaction price is unavailable due to non-cash settlement of a transaction (IFRS 15.66). Furthermore, the fair- value alterations from the balance sheet are presented in the other comprehensive income 69 statement, and thus do not directly influence the profit and loss statement (PwC, 2017, 6–43). One point to consider involves the earnings position, which should be placed in relation to the total assets for testing hypothesis H2B. Since companies can utilize either the cost-of-sales method or the period-costing method, comparability could be restricted based on the selected financial position. To ensure comparability, the earnings before interest and taxes (EBIT) are utilized for the period-costing method and the operating profit for the cost-of-sales method. Both financials present earnings after depreciation and amortization of assets, but before financial costs or earnings and the company’s tax position, and thus should offer a reliable basis for analyzing business profitability. Using the earnings before interest, taxes, depreciation, and amortization (EBITDA) would require the manual retrograde addition of depreciations and amortizations on the operating profit in the cost-of-sales method.

4.3.3 Impact Analysis Focusing the Cash Flow Statement The cash flow statement in IFRS accounting presents an even smaller exposure to discount rates. The statement’s primary purpose is to provide information regarding a company’s actual cash in- and outflows (IAS 7.4–7.6). Therefore, the measurement is performed at actual cash and cash-equivalent values, considering the company’s operating (IAS 7.13–7.15), investing (IAS 7.16), and financing activities (IAS 7.17) and explicitly excluding non-cash transactions (IAS 7.43). In particular, the cash flow development from investing activity presents a convenient financial ratio for testing hypothesis H2A.

4.3.4 Discrepancy between IFRS and German-GAAP Accounting As previously described, market interest rates could comprehensively influence a company utilizing IFRS accounting, especially in regards to different balance sheet position measurements. In contrast to the IFRS fair presentation principle, the German-GAAP follows the prudence principle. Therefore, the only positions with exposure to a market interest rate comprise the provisions on the liabilities side of the balance sheet (Philipps, 2010, 90). To reflect a company’s true liability position, provisions with a maturity exceeding 12 months should be discounted—according to their remaining term—with a seven-year average market interest rate. Pension provisions could also be sweepingly discounted with an assumed remaining term of 15 years and the according market interest rate (Philipps, 2010, 93–94).

In recent years, decreasing market interest rates have resulted in increasing provisions on the liabilities side of the balance sheet, followed by a negative impact on the profit and loss statement (i.e. interest expenses from accumulation of provisions). This impact has manifested itself especially in pension provisions and, after several years in the zero interest rate environment, finally led to an amendment in law. Since then, pension provisions can be 70 discounted with a 10-year average market interest rate according to their remaining term in order to mitigate the accumulation effect (cf. e.g. Deloitte, 2016, 1).

In summary, data from companies applying German-GAAP should, compared to companies applying IFRS, provide less distortion from key interest rate alterations. The trade-off to consider, however, is that stock-listed companies usually offer a higher degree of transparency, and are thus preferred for retrieving variables and data. On the other hand, such companies are obliged to apply IFRS, and are thus exposed to the above-discussed impacts. A compromise would be to focus on variables that are not or only moderately affected by these impacts. The selection of variables according to this principle is performed in the following subchapter.

4.4 Selection of IFRS Financials for Empirical Research Reviewing the previous subchapter results, not all financial variables in IFRS accounting present unbiased data sources. Regarding the conceptualized hypotheses, Table 16 below presents an overview of the selected variables for further utilization in the empirical study design. The balance sheet positions were selected for either primary (i.e. dependent variables) or secondary (i.e. control variables) test purposes, taking into account a possible but moderate distortion. The variables from the profit and loss and cash flow statement, on the other hand, present an unbiased data pool, and thus additionally serve as control variables in the research design. Furthermore, several controlling dummy variables (i.e. industry, geography) are included in the study design. A detailed discussion of the study design occurs in Chapter 7.

Table 16: IFRS Variables for Further Utilization in Empirical Research Data Source Variable Total non-current assets Total current assets Cash and cash equivalents Total assets Balance sheet Short-term financial liabilities Long-term financial liabilities Total financial liabilities Total liabilities Revenue Profit and loss statement EBIT / Operating profit Earnings before taxes (EBT) Cash flow from operating activities Cash flow statement Cash flow from investing activities Cash flow from financing activities Source: Own representation.

4.5 Hypotheses Refinement – Corporations The analysis of financing decisions under uncertainty presented two important results. First, applying the popular NPV approach in practice benefits the assumption that, in times of a zero interest rate environment, a company’s investment activity should, ceteris paribus, increase. This follows from the risk-free interest rate utilization in the expected return on equity formula (i.e. CAPM), which subsequently influences the WACC and the NPV itself. From this follows the second finding—namely, that the overall threshold yield for a project should also decrease given the circumstances. The realization of projects with a lower yield should result in decreasing returns on total assets. Accordingly, hypotheses H2A and H2B do not require amendment, as the theoretical discussion remains consistent with the tools applied in financial management practice. Furthermore, both hypotheses can be tested within the IFRS framework, which applies to stock-listed companies.

The discussion of capital structure theory, on the other hand, did not provide clear implications for hypothesis H2C. Arguing with Keynes’ speculative reserve, reformulating the hypothesis is not performed here. However, it could also be possible that the cash position will be used for the increasing investment activity, as proposed by the pecking order theory. Given sufficient investment opportunities above the return threshold, it would decrease rather than remain stable or increase in a zero interest rate environment. The empirical study in Chapter 7 analyzes which effect is dominant.

The discussion concerning a company’s debt position could also be seen as controversial in the context of capital structure theory. The company’s behavior would be in line with trade-off and signaling theory when deciding to fund new projects with debt despite internal funds being available. Further increasing the debt position would benefit the tax shield, the signaling effect of debt repayment capacity, and the leverage effect for shareholders, which is not only applicable in leveraged transactions. The presented empirical data from the syndicated loan market in Germany, however, implies that excessive lending failed to appear in the zero interest rate environment. As the IFRS framework presents a rather simple initial and subsequent measurement approach for financial liabilities, and this position generally presents an interesting perspective on corporate behavior, they are also included in the empirical study.

Therefore, hypothesis H2D focuses on how key interest rates influence financial liabilities, assuming a negative relationship in accordance with trade-off and signaling theory. Table 17 below illustrates the refinement of corporation-related hypotheses. Hypotheses H2A and H2B were supplemented with the respective IFRS positions, and hypothesis H2D is presented as an additional research focus. 72

Table 17: Refinement of Corporation-Related Hypotheses Number Hypothesis Key interest rates significantly negatively influence the investment activity H2A (i.e. cash flow from investing activity) of a company. Key interest rates significantly positively influence the ratio of average H 2B returns (i.e. EBIT) to total assets. Key interest rates significantly negatively influence the cash and cash- H 2C equivalents position of a company. Corporations Key interest rates significantly negatively influence the financial liabilities H 2D (i.e. current and non-current borrowings) of a company. Source: Own representation.

5. Investor Behavior in Times of Zero Key Interest Rates This chapter emphasizes the refinement of investor-related research hypotheses. First, a brief overview regarding asset allocation approaches is used to connect the previous portfolio theory discussion with practice. This also includes presenting the primarily utilized asset classes in Germany. The second subchapter intensifies the discussion of a possible zero yield bias, including first empirical results based on the assumed behavioral friction. The third subchapter presents the refinement of investor-related hypotheses.

5.1 Asset Allocation in Practice To connect the dots between portfolio theory, autonomous asset classes, and investor behavior, a brief overview of asset allocation practice is provided in the following. Stating one important point first, there remains no generally accepted approach to asset allocation. Instead, the practical application can, for example, vary based on investor type (e.g. institutional or private investor) or idiosyncratic demand (e.g. pension fund or hedge fund). Most approaches, however, share in their differentiation between strategic and tactical asset allocation at least one common element (cf. e.g. Lingner, 2003, 275; Lumholdt, 2018, 6). Strategic asset allocation could be defined as a collection of long-term decisions determining an investor’s general portfolio structure. Tactical asset allocation, on the other hand, can be described as the short- to mid-term realization of those decisions.

Lumholdt (2018, 6–9), for example, further differentiated strategic asset allocation into the investment policy as a general framework and strategic asset allocation as the long-term realization process within this framework. The investment policy considers elements such as investment objectives (e.g. capital preservation or capital appreciation), time horizon, investment universe (i.e. selection of asset classes), investment strategy (e.g. active or passive), overall risk tolerance (e.g. maximum drawdown), taxation, and other possible portfolio constraints (e.g. minimum cash holdings). Based on the investment policy and the long-term risk-return expectations, the strategic asset allocation determines target weights for the selected asset classes and the permitted deviation from those targets (Lumholdt, 2018, 8–9). If the investment policy includes a variety of constraints, portfolio realization on the efficient frontier in accordance with portfolio theory could pose a challenging task for the portfolio manager. Thus, optimization techniques can be utilized in practice to determine the best possible restricted efficient frontier along which the portfolio would be positioned (Lingner, 2003, 276). Conclusively, strategic asset allocation or the investment policy represent crucial frameworks in the investment process. With the decision regarding which asset classes are allowed for diversification and which are not, the investor also decides on possible risk-return outcomes, 74 and thus the reward for bearing specific systematic risks as well (Sharpe, Chen, Pinto, & McLeavey, 2007, 231–236). Tactical asset allocation then comprises the short-term-oriented portfolio management. This includes individual title selection, reaction to possible exogenous shocks (e.g. cyclical or monetary policy changes), and utilization of pricing anomalies (Lumholdt, 2018, 8–9). The implicated impact on portfolio performance by decisions made on the tactical asset allocation level is assumed with a minority share of 10–30% (Lingner, 2003, 285). Figure 12 below offers an illustration of Lumholdt’s (2018, 6–9) asset allocation process with its components.

Figure 12: Asset Allocation Process with Typical Elements

Source: Own representation based on Lumholdt (2018, 6). The importance of strategic asset allocation, and especially of asset class selection, is evident. Examining the current wealth allocation in Germany, however, overall diversification appears to focus especially on the generally accepted basic asset classes. The necessary data for this analysis needed to be retrieved in two steps, since the top level wealth allocation statistics include insurances as their own asset class and present a breakdown of securities to bonds, stocks, and investment funds investing into stock funds, bond funds, and so forth by themselves (Statistisches Bundesamt, 2019, 19). Thus, reclassifying assets into the respective asset baskets was performed first and supplemented by the actual investments of insurance companies in Germany afterwards (Gesamtverband der Deutschen Versicherungswirtschaft e. V., 2018, Table 15). Figure 13 below presents the calculation results (see Appendix C for more details). Herein, real estates, overnight deposits and savings accounts, and bonds account for approximately 91.8% of Germany’s gross total wealth. Rebalancing the perspective to money

75 wealth only (see Appendix D for detailed calculation), the asset classes overnight deposits and saving accounts, bonds, and stocks account for 90.8% of the total wealth. Despite the fact that alternative investments encompass diversification benefits, they play only a subordinated role in German wealth allocation. Possible explanations for this situation could be the lack of transparency (i.e. return and volatility), lack of resources to research those asset classes, or associated fees with the investments (Sharpe et al., 2007, 253–254).

Figure 13: Overview of Wealth Allocation in Germany

Source: Own calculation based on Gesamtverband der Deutschen Versicherungswirtschaft e. V. (2018, Table 15), Statistisches Bundesamt (2019, 16, 19).

In particular, hypothesis H1B represents a legitimate question in this context. Given the wealth allocation overview, it becomes unclear whether the market share of direct lending significantly differs from zero. The answer is provided in the empirical study in Chapter 6.2. The previous discussion allows at least the argumentation that, based on the level of professionalism, investors have to consciously or unconsciously adjust their strategic asset allocation (or the investment policy) to be able to invest in the direct lending asset class. The interrelation between this strategical amendment and the zero key interest rate is discussed in the following subchapter.

5.2 Zero Yield Bias As previously illustrated in Figure 5, key interest rate alterations should negatively influence the investor’s utility, as the expected return decreases given a specific risk-appetite. Assuming a stable risk preference, acceptance of new asset classes in the strategic asset allocation would mitigate, but hardly eliminate, the full negative impact. The efficient frontier should perform a left shift due to the new diversification possibility; however, the investor would still have to 76 surrender risk-free investments and move to the right on the capital allocation line in order to reach the old return level. Thus, the reaching-for-yield phenomenon (cf. e.g. Bernanke, 2013, 12) cannot be completely explained by the modified efficient frontier. Therefore, the question is raised as to why an investor would be willing to move along the capital allocation line in a zero interest rate environment.

Lian, Ma, and Wang (2019) conducted one of the first experimental studies on this topic, analyzing individual investor behavior in different interest rate environments. They found evidence that investors allocate significantly higher wealth amounts to riskier assets when interest rates are low. With these results, the authors also delivered two empirically tested and verified explanations of this phenomenon from the research field of behavioral finance. The first considers an individual reference dependence. According to this approach, investors form a reference point for returns in a normal interest rate environment. Returns in a low interest rate environment are then perceived as too low to be accepted, and are thus supplemented or substituted by returns from riskier assets in order to increase the overall return (Lian et al., 2019, 2123–2126). The second solution approach considers a possible salience of returns from risky assets compared to low interest rate returns (e.g. 6% vs. 1%) instead of normal interest rate returns (e.g. 10% vs. 5%) (Lian et al., 2019, 2126–2128).

Ganzach and Wohl (2018) conducted a contemporaneous empirical study focusing on individual investment behavior. The authors provided comparable results and explanations for the confirmed phenomenon of risky asset demand in times of low interest rates. Furthermore, several empirical studies have also provided consistent results for institutional (e.g. banks or pension funds) investment behavior (cf. e.g. Andonov, Bauer, & Cremers, 2017; Di Maggio & Kacperczyk, 2017; Jiménez, Ongena, Peydró, & Saurina, 2014; Maddaloni & Peydró, 2011). Therefore, an interest-rate-related behavioral bias in executing investment strategies appears evident (i.e. zero yield bias) and should be researched further. The above-mentioned studies by Ganzach and Wohl (2018) and Lian et al. (2019) conducted experiments with one risk-free and one risky investment opportunity, between which the test person had to decide. Schaab et al. (2019) proposed an experimental design that considered the primarily relevant asset classes in Germany, and which should thus offer deeper insight into the behavioral bias as well as an additional perspective for further developing the field of behavioral finance. The experimental design is also able to answer both investor-related hypotheses H3A and H3B, and is therefore utilized in the empirical research set-up (cf. Chapter 8).

5.3 Hypotheses Refinement – Investors Based on the previous subchapters, the refinement of the investor-related hypotheses does not require much explanation. The discussion supplemented the already provided arguments on biased investor behavior with first empirical proofs. Application of the proposed experimental design by Schaab et al. (2019) should comprehensively answer both research hypotheses. The connection of asset allocation approaches with portfolio theory and investor behavior enables minor refinements of hypotheses H3A and H3B, which largely includes definitions of the previously employed terminology. Table 18 below presents the final investor-related research hypotheses.

Table 18: Refinement of Investor-Related Hypotheses Number Hypothesis

Investors significantly change their investment behavior (i.e. their asset H 3A class allocation) in periods of a zero key interest rate environment. Investors allocate significantly more capital to riskier investments (i.e. H3B higher default probabilities than before) in periods of a zero key interest Investors rate environment. Source: Own representation. Analyzing the discussion results up to this point, the necessity of further research appears evident. However, the partially divergent arguments derived from different theoretical frameworks, the novel situation for European economies, and the scarce data availability on specific topics present scientific challenges that will only be fully mastered in the following decades. The exploratory approach of this dissertation project, however, should provide first empirical insights into the formulated questions, and thus important puzzle pieces for solving the riddle of Germany’s growing direct lending industry.

6. Testing the Bank-Related Hypotheses This chapter is subdivided into two sections. The first subchapter contains the empirical study for testing hypothesis H1A, which assumes a significant relationship between a bank’s internal costs and profit growth in times of low or zero key interest rate environments. The second subchapter covers the test of hypothesis H1B, which assumes the significance of the corporate direct lending industry in Germany.

6.1 Empirical Study: Determinants of Bank Profitability Growth The empirical study concerning bank profitability growth determinants comprises five subchapters. First, the selection process for the variables is explained in detail. The second subchapter describes the composition of the test and the control group, as well as the data collection process and the challenges encountered in its execution. The third subchapter presents the descriptive data analysis and the subsequent identification and elimination of outliers. Based on the preparations, the fourth subchapter introduces the statistical methodology and the assessment of model quality. The fifth subchapter presents the results discussion and limitations of this empirical research approach.

6.1.1 Selection of Variables The year-over-year percentage-point alteration of the RORWA ratio (RORWAYOY) was utilized as dependent variable to represent bank profitability growth. The RORWA ratio can be identified as one of the key metrics of bank profitability in today’s financial reporting and rating assessments (cf. e.g. Golin & Delhaise, 2013, 289–290; McKinsey & Company, 2017, 21; Moody’s, 2011, 5). This key status relates to national implementations of the third pillar of Basel II, which forced banks to publish a disclosure report and reveal the nominal value of their risk-weighted assets (Basel Committee on Banking Supervision, 2004, 175–190). In this context, this ratio became accessible not only for interested third parties, but also for academic research, in which the return on average assets comprised the previously dominant key ratio (cf. e.g. Dietrich & Wanzenried, 2011, 311).

For the present empirical study, the return value for calculating the RORWA ratio was defined as the sum of the net interest income (NII) and the net commission income (NCI) of the respective bank. Accordingly, the ratio focuses on earnings before administrative expenses (AE) and intentionally excludes volatile profit or loss positions, such as the trading or the impairment result (Golin & Delhaise, 2013, 155, 180–185).

This specific approach was chosen because utilizing any other earnings-growth ratio would have produced a biased panel model specification. As an example, earnings before taxes (EBT)

79 could have been considered as an alternative return-growth measure. Assuming a year where a bank suffers from a negative one-time impact of a volatile profit-and-loss position, the EBT should also present an accordingly low value. If no other negative impact manifests in the following period, the EBT should automatically increase by reversing the impact. This return growth would not necessarily account for the actual development of internal costs, however. Accordingly, a causal relationship between internal costs and profitability growth would be difficult to establish in this case. One possible solution would be to control for or smooth out those one-time effects. Based on the data availability, however, this objective is not achievable from an external third-party perspective, which impedes the according panel model specification. The same argument also applies to earnings after taxes (EAT), which are additionally exposed to heterogeneous tax-ratio effects of sample subjects. The sum of the NII and the NCI over RWA thus presents not an optimal, but at least a comparably one-time effect- resistant return ratio on core banking activities.

The main determinant of bank profitability growth was defined as a percentage ratio of administrative expenses over risk-weighted assets (AERWA). Administrative expenses represent the sum of staff costs and general administrative expenses. The hypothesis test considers the AERWA ratio as a determinant for profitability growth in the following fiscal period. As previously described, the lower the AERWA ratio is, the higher the competitive advantage, and thus the return growth in the following period, should be. The expected effect of the AERWA ratio is therefore negative. The mechanism for considering the independent variable as a determinant of growth in the following period was applied in general.

The second determinant of bank profitability growth was defined as a ratio of loan loss provisions over risk-weighted assets (LLPRWA). This ratio offers a measure of the bank’s asset quality and should inversely influence the profitability growth. A high LLPRWA ratio indicates lower asset quality, and could therefore reduce the bank’s flexibility in the operative business. Lower flexibility could subsequently result in lower competitive advantages, and thus lower profitability growth. The loan loss provisions (LLP) were defined as the sum of net loan loss provisions and the provisions for the fund of general banking risks, which is a special item in German-GAAP (Handelsgesetzbuch, 2019, § 340g). In the past, German banks were able to constitute hidden reserves through general valuation allowances on loans. With the CRR’s implementation, however, those allowances had to be dissolved and reclassified as disclosed reserves to be taken into account in the core-capital-ratio calculation (Fischer, 2014, 13–17;

Handelsgesetzbuch, 2019, § 340f). Accordingly, the LLPRWA ratio utilizes the net loan loss provisions and takes this German-GAAP anomaly into account to calculate a reliable variable.

The third independent variable was defined as the other sources of income or expenses over risk-weighted assets (OSIERWA). The OSIERWA ratio accordingly considers all other (more volatile) positions of the profit and loss statement, which, for example, include the trading result, impairment result, and extraordinary items. The OSIERWA ratio’s expected effect on bank profitability growth is positive. The higher the OSIERWA ratio, the higher the bank’s earnings and operative flexibility should be. Based on this greater flexibility, the bank should experience a competitive advantage and use this to increase profitability.

The RWA, the balance sheet total (BST), and the number of employees (NOE) were also added as independent variables. These values were converted by a natural logarithm transformation to LOG_RWA, LOG_BST, and LOG_NOE to reduce skewed variable distributions (cf. Table 22). Although all three variables present size approximations and are included as control variables in the model, the expected effects on bank profitability growth deviate from each other. The expected effect of LOG_RWA and LOG_BST is indeterminate. Some researchers have argued that the total volume of the balance sheet and the risk-weighted assets indicates a larger scope for action based on the given diversification (cf. e.g. Smirlock, 1985, 75). A large bank should be able to optimize its asset portfolio in different ways and, in this context, increase its returns on risk-weighted assets, while a small bank should be more dependent on the specific asset portfolio characteristics. Other scientists have argued that large institutions have already exploited the economies of scale, and are thus more exposed to bureaucracy, agency costs, and other management-related costs, which could negatively affect profitability growth (cf. e.g. Pasiouras & Kosmidou, 2007, 232–233). The NOE, on the other hand, does not necessarily have to be correlated with RWA or BST. For example, a bank could possess a large number of branches with low volume business, and thus lower RWA and BST amounts. The NOE should instead be correlated with the AE, which include the related staffing costs. Accordingly, a large NOE should also decrease the bank’s flexibility and impede profitability growth. The effect on the dependent variable should accordingly be negative to correspond with the basis hypothesis.

Besides the numerical variables, a total of five categorical variables were added to the model to control for specific effects. The first categorical variable points out constituents from the control group (i.e. GROUP). The expected effect is positive based on Germany’s competitive banking environment (cf. Chapter 3.3). The profitability growth of German banks should accordingly be lower compared to banks from other Eurozone countries. 81

The second categorical variable illustrates the bank group (BG) affiliation, which could be either the savings bank association, the cooperative banks association, or the group of private banks. The expected effect is indeterminate, since different arguments can be stated in favor of or at the expense of the respective bank group. For example, it could be argued that savings banks and cooperative banks possess a strong local knowledge enabling them to utilize this competitive advantage in favor of profitability growth. On the other hand, this local constraint could convey negative implications if the respective area is structurally weak and does not present any opportunities for profitability growth. It could also be argued that the influence and recommendations of the respective umbrella organization affects profitability growth in a specific manner. Accordingly, each of those three bank groups should exhibit different market behavior based on this influence, which would consequently lead to different profitability growth. However, there remains no unified opinion regarding the bank group affiliation’s impact on profitability in the scientific literature that could a priori be applied.

The third categorical variable controls for the business model’s geographical scope (GS). The respective bank can focus its business activities on either a regional (i.e. specifically defined domestic area), national, or international scope. Comparably to the BG variable, the expected effect is also indeterminate and is researched in the following empirical analysis.

The fourth categorical variable accounts for a possible stock exchange listing (SEL) of the respective bank to measure whether a different capital market access and shareholder structure could also influence profitability growth. Dietrich and Wanzenried (2011, 321) found evidence in all model specifications that stock-listed entities are less profitable compared to non-listed entities. This could, for example, result from the associated costs of a stock exchange listing (e.g. investor relations, stock exchange fees). The results were assimilated in the subsequent empirical study, leading to the assumption that an SEL will negatively influence the bank’s profitability growth.

Since several banks voluntarily apply the IFRS accounting standard (ACS) without a stock exchange listing, a fifth categorical variable was necessary as a control measure to differentiate between German-GAAP and IFRS-applying banks. Although a strong overlap should exist between the ACS and the SEL dummy variables, and thus also a tendency toward a negative relationship with profitability growth, the actual impact of the non-listed banks that apply IFRS accounting remains difficult to assess. Accordingly, this variable’s anticipated effect remains indeterminate. Table 19 below presents a summary of the model variables, their respective descriptions, and their expected effect on the dependent variables. 82

Table 19: Definition and Description of Variables for the Test of H1A Dependent Description Variables RORWAYOY Year-over-year percentage-point development of the sum of net interest income and net commission income over risk-weighted assets (%)

Independet Expected Description Variables Effect AERWA Administrative expenses over risk-weighted assets (%) – LLPRWA Sum of net loan loss provisions and provisions for the funds of general – bank risks over risk-weighted assets (%) OSIERWA Other sources of income and expenses over risk-weighted assets (%) + LOG_RWA Log transformation of the risk-weighted assets + LOG_BST Log transformation of the balance sheet total + LOG_NOE Log transformation of the number of employees – GROUP Dummy variable for the control group + BG Dummy variable for different bank groups +/– GS Dummy variable for geographical scope of the respective bank +/– SEL Dummy variable for stock exchange listing – ACS Dummy variable for IFRS accounting standard +/– Source: Own representation. 6.1.2 Definition of Test and Control Group and Data Collection Process The population of German banks is systematized by Deutsche Bundesbank (2018) in a regularly published directory. This directory and the supplementary directories from the savings bank (Deutscher Sparkassen- und Giroverband e.V., 2019) and the cooperative bank association (Bundesverband der Deutschen Volksbanken und Raiffeisenbanken e.V., 2019) served as data sources for the following test group selection. Initially, the necessary sample size was determined using the G*Power 3.1 tool (for details on development and refinement cf. Faul, Erdfelder, Buchner, & Lang, 2009; Faul, Erdfelder, Lang, & Buchner, 2007; for download cf. Heinrich Heine Universität Düsseldorf, 2019). Given a medium effect size of 푓2 = 0.15 (Cohen,

1988, 412–414), a conventional 훼푒 error probability of 5%, a power of 80% (fourfold 훽푒 error probability of 20%), and seven predictor variables (based on a corresponding dummy variable coding), the a priori sample size for the multiple regression should include at least 103 test subjects. Taking several dropouts into account, the sample size for the data collection was set at 105 test subjects.

To test deviations between the different banking groups in Germany, a non-proportional stratified selection process was applied (cf. e.g. Cook & Wissmann, 2007, 23–25). Accordingly, the total population was partitioned into three subpopulations corresponding to the three pillars of the German banking system (cf. Figure 7). In the second step, 35 test subjects were selected from each stratum, applying a random selection method with minor constraints. These

83 constraints especially referred to private banks, excluding organizations in default or liquidation, foreign banks with a banking license but without a legal entity in Germany, and public promotional institutions (e.g. KfW, NRW.Bank) due to their limited comparability to German market incumbents. Additionally, banks that could not provide the necessary data track record were excluded from the selection. The random selection group was manually supplemented by the three largest umbrella organizations in the savings bank association (i.e. Landesbanken measured by balance sheet total in 2018) and the umbrella organization of the cooperative banks (i.e. DZ Bank) due to their outstanding importance in the respective pillar. The sample composition, selection procedure, and number of subjects are summarized in Table 20. The individual banks are listed in Appendix E in alphabetical order.

Table 20: Composition of the Test Group Group Selection Procedure Number Random selection 32 Savings banks Manual supplement of the three largest umbrella 3 organizations by balance sheet total Cooperative Random selection 34 banks Manual supplement of the umbrella organization 1 Private banks Random selection 35 Total Sample Size 105 Source: Own representation. In order to test the defined hypothesis, the model variables had to be collected from the respective bank’s annual and disclosure reports. Since most banks in Germany comprise non- listed entities, the data collection relied on information disclosures. Two regulatory frameworks are in place to set the possible analysis period. First, the Solvency Regulation (Solvabilitätsverordnung, 2006, §§ 319–337; superseded by European Parliament and the Council of the European Union, 2013, §§ 431–451) enables collecting data regarding risk- weighted assets and capital ratios starting from the financial year 2007. Second, the Law on Electronic Commercial and Company Registers (EHUG, 2006, § 21) obliges companies to disclose their financial reports, and thus the other necessary variables, in the electronic Federal Gazette starting with the financial year 2007. Accordingly, the timeframe was set between 2007 and 2018 and also applied to the selected stock-listed banks to ensure data comparability. This time period corresponds with a historically low key interest rate environment (cf. Appendix A), meeting the precondition requirement for hypothesis H1A.

Three major challenges were uncovered in the process of compiling data. First, it is common practice for banks in Germany to disclose a reference to the disclosure report in the Federal Gazette, leading interested third parties to the bank’s website. The report on the website, 84 however, is in most cases only available for the previous financial year and is removed by the bank afterwards. Additionally, the response ratio on data requests was low at 16.6%, making inquiries not a viable alternative for gathering the necessary information. Consequently, collecting data on the nominal amount of risk-weighted assets relied on disclosures in the financial report or, alternatively, on disclosure of the liable equity capital components and the respective capital ratios. These values enabled reverse calculating the nominal risk-weighted assets amount by rearranging the equation as demonstrated in Formula 6. In each case, the formula was adjusted according to the available data and the legal framework currently in place (for detailed composition of liable equity components and the calculation of equity ratios during applicability of the Solvency Regulation until 31 December 2013 cf. Fischer, 2011, 15–44; for calculation framework during the applicability of the Capital Requirements Regulation after 1 January 2014 cf. Fischer, 2014, pp. 13–33).

퐸푛표푚 with: = 퐸푟푎푡 푅푊퐴 퐸푛표푚 Nominal liable equity capital (6) 퐸 퐸 Respective equity capital ratio in relation to 푅푊퐴 = 푛표푚 푟푎푡 퐸푟푎푡 risk-weighted assets

The second encountered challenge concerned the transitional arrangement of the Solvency Regulation (Solvabilitätsverordnung, 2006, § 339). This allowed banks to implement the new regulation with a delay of one year if the respective bank strived to apply the IRBA. Consequently, in 2007, some of the test subjects applied the old risk-weighting approach (cf. Kreditwesengesetz, 1998, § 10) while others were already applying the Solvency Regulation. The effect of this discrepancy is analyzed in the following subchapter to decide whether exempting the year 2007 in the dataset would be appropriate.

The third and final data collection challenge related to the aforementioned disclosure issue. In case a specific bank did not provide the necessary data for analysis, the random selection process was executed repeatedly to draw substitute test subjects from the data pool. Since the data pool for the savings banks (i.e. 394) and the cooperative banks (i.e. 916) provides a sufficient capacity, the redraws did not trigger any complications. However, the data pool for the private banks eligible for the draws was comparably small (i.e. 157). This initial number, combined with the data track record constraint and the other limitations, led to a total of only 36 test subjects that met all requirements for subsequent statistical testing. The random draw was performed after identifying those test subjects. However, this approach alters the random selection process to a manual selection. Nevertheless, the sample contains a heterogeneous

85 collection of banks in terms of size, business model, and capital market access, which should thus meet the representativeness criteria.

In total, 1,260 annual and 319 disclosure reports have been collected from either the respective bank’s website or directly from the German Federal Gazette. Based on these sources, a total of 10,080 numerical data elements have been manually retrieved and systematized in Microsoft Excel for further analysis and calculation. Additionally, four categorical variables have been retrieved for each test subject, adding a total of 420 data elements to the data pool. The data pool for the test group features no missing values due to the applied data collection methodology.

In order to explore whether the specific effects are only valid for German banks or also feature implications for other banks in the Eurozone, supplemental data from a control group was included in the research design. Due to data availability in foreign jurisdictions, the control group consists exclusively of stock-listed banks that are constituents in the EMU-Datastream Banks Index provided by Thomson Reuters Datastream. The index represents a total of 77 banks, of which 47 banks met both criteria, possessing headquarters in the Eurozone but not in Germany and having a data track record including at least the year 2007. Six of the index constituents were excluded due to having German headquarters, 10 constituents were excluded due to an overly short data track record, and the remaining 14 constituents were excluded based on data errors and insufficient data availability. Table 21 below illustrates the geographical distribution of the selected control group. The individual banks are presented with their country of headquarters in Appendix F in alphabetical order.

The control group’s data elements processed in the empirical study originate from two data sources. First, the Thomson Reuters Datastream database was employed to retrieve the same numerical variables as in the test group. In the second step, annual and disclosure reports were utilized to replenish 73 missing data elements. During the sample inspection, it became apparent that the variables NCI, AE, and LLP exhibited different values than the actual financial reports due to a standardization adjustment by Thomson Reuters. Therefore, these values were collected manually from the respective bank’s financial reports. For data-retrieval purposes, 564 annual and 12 disclosure reports were collected from the respective bank’s website. In total, 4,512 numerical and 188 categorical data elements were collected and systematized in Microsoft Excel for further analysis.

Table 21: Composition of the Control Group by Country Country Constituents Geographical Coverage of EMU Austria 5 Belgium 1 Cyprus 2 Finland 2 France 5 Greece 4 Ireland 2 Italy 11 Lithuania 1 Malta 4 Netherlands 2 Portugal 1 Slovakia 3 Spain 4 Source: Own representation. 6.1.3 Descriptive Statistics and Elimination of Outliers The next preparatory step covered the descriptive analysis of the collected data elements. This analysis should mitigate the likelihood of biased model estimates resulting from outlier values. As described in the previous subchapter, the first decision to make concerned whether to in- or exclude the year 2007 in the model due to the transitional arrangement of the Solvency Regulation (Solvabilitätsverordnung, 2006, § 339). As this arrangement primarily affects the nominal volume of RWA—and accordingly, all ratios calculated on their basis—the impact measurement focused on the mean value difference for the years 2007 and 2008. Applying a 푡- test statistic on RWA in billion euros, the null hypothesis that the difference of means (휇2007 =

42.9869, 휎2007 = 103.167; 휇2008 = 42.8832, 휎2008 = 101.826) equaled zero was not rejected based on a two-tailed 푝 = 0.993. Consequently, the year 2007 was included in the statistical analysis. This and all subsequent calculations were performed using GNU Regression, Econometrics, and Time-Series Library (gretl) software.

The three determinants RWA, BST, and NOE are not expressed as a percentage, and are thus exposed to a strong positive skew. Since the skewness was considered substantial, the common practice of data transformation was applied to those three variables to resolve this skewness and present a more symmetric distribution (cf. e.g. Kuhn & Johnson, 2013, 31–33). Accordingly, the variables were converted by a natural logarithm transformation to LOG_RWA, LOG_BST, and LOG_NOE. The histograms and the density functions before and after the transformation are presented in Appendix G, while the numerical analysis is presented in Table 22. Since the log transformation simultaneously resolved the outlier issue, the number of data elements only decreases as a result of sample subject exclusions in the subsequent outlier analysis. Based on 87 the model specification, data from the year 2018 is used solely for year-over-year calculation of the RORWA ratio, and is thus excluded from all other calculations.

Hoaglin and Iglewicz’s (1987) resistant rules for outlier labeling were applied to the other four numerical variables. Accordingly, a specific value was labeled an outlier when its distance from the first or third quartile value exceeded the 2.4-fold interquartile range (Hoaglin & Iglewicz, 1987, 1149). This calculation was performed on an annual basis. Following this approach, 342 out of 6,688 values were identified as outliers (i.e. RORWAYOY: 79 values; AERWA: 99 values; LLPRWA: 72 values; OSIERWA: 92 values), equaling 5.11%. A total of 64.9% of those values were allocated to the test group of private banks, followed by 29.5% in the control group, 3.8% in the cooperative banks group, and 1.8% in the savings banks group.

The subsequent individual outlier analysis followed two rules. First, a bank was completely excluded from the data set when its outlier data element proportion exceeded 20%. Second, single outlier values were context reviewed to assess whether the outlier only resulted from the respective annual distribution or if it represented an actual one-time effect in the individual bank’s data series. The latter were removed from the data set. Based on this approach, 99 single outlier values and 10 banks containing a total of 194 outlier values were excluded from the data set (i.e. Baader Bank, Banca Generali, Bankhaus Ellwanger & Geiger, Joh. Berenberg Gossler & Co., BHF, comdirect, Hoerner-Bank, Lombard Bank, S-Broker, and Tradegate). In most cases, these banks followed a highly specific business approach and did not necessarily qualify as universal banks, which consequently led to the large number of outliers. Table 22 below presents the calculated descriptive statistics before and after the outlier elimination.

It can be seen that the outlier elimination led to an improvement in skewness and mitigation of the leptokurtic distribution in all four variables. The RORWAYOY presents a range between –2.35% and 2.52% with both mean and median values close to zero. The AERWA ratio varied between 0.40% and 9.75% with a mean value of 3.38%. The range represents an interesting finding, especially when examining the savings or the cooperative bank association sample subjects. The respective banks seem to operate their RWA at totally different administrative- cost ratios, although the business set-up should be comparable in most aspects. The LLPRWA ratio varied between -1.57% (i.e. reversal of loan loss provisions) and 5.73% with a median value of 0.69%, which corresponds to an overall portfolio-quality translation in a BB to BB+ rating by S&P (Krämer-Eis, 2001, 25). Finally, the OSIERWA ratio exhibits a range between –3.50% and 3.78% and a mean value of 0.74%. This indicates that banks are, on average, able to generate a positive return from the other sources of income or expenses. However, this 88 represents a highly volatile profit-and-loss position, as can be seen in the minimum and maximum values before the outlier elimination. The histograms and density functions of the four outlier-adjusted variables are presented in Appendix H. To assess the annual development of the variables, supplemental material is provided in Appendix I.

Table 22: Descriptive Statistics of Numerical Variables for the Test of H1A Dependent Excess 푴풊풏 푴풂풙 Median 흁 흈 Skewness 풏 Variables Kurtosis 1 RORWAYOY –0.1961 0.2365 –0.0003 –0.0003 0.0124 1.07 126.75 1,672 RORWAYOY –0.0235 0.0252 –0.0003 –0.0001 0.0057 0.13 1.81 1,541

Independet Excess 푴풊풏 푴풂풙 Median 흁 흈 Skewness 풏 Variables Kurtosis AERWA1 0.0040 0.3927 0.0336 0.0415 0.0369 4.78 29.19 1,672 AERWA 0.0040 0.0975 0.0325 0.0338 0.0129 1.04 2.36 1,555 LLPRWA1 –0.0544 0.1881 0.0068 0.0093 0.0134 5.27 48.98 1,672 LLPRWA –0.0157 0.0573 0.0069 0.0081 0.0069 1.39 4.36 1,511 OSIERWA1 –0.1743 0.5750 0.0026 0.0080 0.0370 10.04 124.05 1,672 OSIERWA –0.0350 0.0378 0.0023 0.0036 0.0074 0.41 2.82 1,542 RWA1, 2 0.0211 642.0698 1.7177 39.3955 100.1334 3.57 13.43 1,672 LOG_RWA 9.9566 20.2800 14.4690 14.9010 2.5355 0.30 –0.83 1,562 BST1, 2 0.0314 2,202.4230 3.6488 112.8966 320.4141 3.96 16.60 1,672 LOG_BST 10.3540 21.5130 15.1610 15.5790 2.6546 0.42 –0.69 1,562 NOE1, 3 0.0060 205.3480 0.6260 11.6201 32.0291 3.86 15.41 1,672 LOG_NOE 1.7918 12.2320 6.4401 6.8153 2.4050 0.36 –0.54 1,562 Notes: 1 before outlier elimination; 2 in billion euros; 3 in thousands. Source: Own calculations. The remaining five variables present a categorical scale level enabling only a limited analysis scope. Table 23 below presents the different characteristics of the categorical variables and their associated distributions.

Table 23: Descriptive Statistics of Categorical Variables for the Test of H1A Categorical Variable Description Number of Constituents Percentage Test group 97 68.3% GROUP Control group 45 31.7% Cooperative banks 35 24.6% BG Savings banks 35 24.6% Private banks 72 50.7% Regional scope 67 47.2% GS National scope 29 20.4% International scope 46 32.4% Yes 51 35.9% SEL No 91 64.1% German-GAAP 85 59.9% ACS IFRS 57 40.1% Source: Own calculations. The number and percentage of constituents for the test and control groups corresponds to the chosen approach in Chapter 6.1.2. The group of private banks is overweighted due to the control

89 group constituents, all of which are private banks. Cooperative banks and savings banks are mostly operating on a regional scope, leading to an overweight of the respective group in the GS variable. Finally, the SEL and ACS variables demonstrate the previously described mismatch between listing and application of the IFRS accounting standard. In total, six banks are voluntarily applying the IFRS accounting standard without being listed (i.e. umbrella organizations in the savings or the cooperative bank association and banks with stock-listed parent companies).

6.1.4 Research Design Based on the collected longitudinal data set (short and wide panel), a panel regression was applied to test hypothesis H1A. In general, panel regression designs offer three models for utilization. Herein, the pooled regression model, fixed effects model (FEM), or random effects model (REM) can be applied for the calculation. The central assumption of the pooled regression model concerns the absence of subject-specific influences, meaning that the model corresponds with an ordinary least squares (OLS) model. The FEM controls for subject-specific heterogeneity by assuming different regression intercepts. The REM follows the same approach as the FEM, but focuses on the between-variation effects rather than within-variation effects (Malitte & Schreiber, 2019, 389–405). The decision of which model to use can be based on statistical tests. To test whether the groups share a common intercept, the 퐹-test statistic can be applied. If the groups possess a common intercept, the OLS model should be preferred in favor of the FEM (Malitte & Schreiber, 2019, 398–399). Each time, running the full as well as several reduced models resulted in non-rejection of the null hypothesis, favoring the OLS model. The second diagnostic utilizes the Hausman (1978) test statistic to assess whether the generalized least squares estimates are consistent. If they are, the REM should be preferred in favor of the FEM (Malitte & Schreiber, 2019, 405). However, all test results were statistically significant at a 5% level, rejecting REM application. Consequently, the pooled regression model (i.e. OLS) was applied for further statistical testing. All calculated test statistics are listed in Appendix J. Formula 7 below describes the model equation.

with:

푦푙 Observed value 푙 of criterion 푦 훽0 Regression intercept 퐾 훽푘 Regression coefficient 푘 (7) 푦푙 = 훽0 + ∑ 훽푘 ∗ 푥푘푙 + 푢푙 푥푘푙 Observed value 푙 of determinant 푥푘 푘=1 푙 Index of observations (푙 = 1, . . . , 퐿) 푘 Index of determinants (푘 = 1, . . . , 퐾)

푢푙 Residual value of observation 푙

In the next step, regression diagnostics were conducted to assess model quality and determine possible model assumption violations. For efficient and unbiased estimates, no linear relationship should exist between the determinants (i.e. no multicollinearity), residuals should be uncorrelated (i.e. no autocorrelation), and variance of the residuals should be identical and finite for all observations (i.e. homoscedasticity) (cf. e.g. Auer & Rottmann, 2011, 445–452; Gujarati & Porter, 2009, 61–69). Running the model with all numerical and categorical variables, supplemented by time dummies, revealed two assumption violations.

The first violation was that several determinants in the data set exhibited multicollinearity. The initial variance inflation factor (VIF) calculation discovered that the variables LOG_NOE, LOG_RWA, LOG_BST, SEL(Yes), and ACC(IFRS) present problematic VIF values larger than 10 (cf. e.g. Freund, Wilson, & Sa, 2006, 190–192). The subsequent collinearity diagnostics revealed that all named variables possess variance proportions over 50% in condition indices, indicating a moderately strong (i.e. condition index ≥ 10) or strong (i.e. condition index ≥ 30) near-linear dependence (Belsley, Kuh, & Welsch, 2004, 105). Based on detailed investigations of the interdependencies, all variables in question were excluded from the model to counter biased estimate values (Auer & Rottmann, 2011, 513–514).

Losing all size approximations in the process was problematic regarding the model’s explanatory power. However, the 퐹-test statistic for the null hypothesis that the regression parameters equal zero for the excluded variables was not rejected at a 푝 = 0.143374. Moreover, all three information criteria were improved by excluding those variables (i.e. the Akaike information criterion (AIC) by Akaike (1974), the Bayesian information criterion (BIC) by Schwarz (1978), and the Hannan-Quinn information criterion (HQIC) by Hannan and Quinn (1979)). Those characteristics should partially alleviate the disadvantages from the exclusion.

The second assumption violation followed from White’s (1980) test for heteroscedasticity. Accordingly, the null hypothesis that the variance of residuals is identical (i.e. homoscedastic) is rejected at a 푝 < 0.001. The robust standard errors by Arellano (1987) are utilized for further testing to mitigate this assumption violation and increase the estimators’ efficiency.

The third assumption was tested using the Wooldridge (2002, 282–283) test for autocorrelation in panel data. The null hypothesis that there is no first order autocorrelation of residuals was not rejected at a 푝 = 0.599553. The VIFs for the full and reduced model, and a summary of all other regression diagnostic results, are presented in Appendix K.

The final step involved testing the robust standard error regression model for irrelevant determinants. For this purpose, the step-wise determinant exclusion effect is presented through 퐹-tests and the three information criteria in Appendix L. Only variables with a 푝 > 0.20 (i.e. fourfold 훼푒 level) were selected for this process. It can be seen that the coefficients and the test statistics of the dummy variables BG (private banks), GS (national banks), and BG (savings banks) present no significant effects in relation to the RORWA-ratio growth. Their exclusion from the final regression model also improved all three information criteria. The presented research design and the respective steps in this subchapter lean on the general-to-specific modeling approach, also known as the London School of Economics method (for an overview cf. Campos, Ericsson, & Hendry, 2005).

6.1.5 Result Discussion and Limitations Table 24 below presents the regression results, differentiating between the main measure, the full model of remaining determinants, and the full model including dummies for each year in the data set.

Table 24: Results of Regression Analyzing Determinants of Bank Profitability Growth (H1A) Full Model Main Measure Full Model incl. Time Dummies 휷풌 (흈) 휷풌 (흈) 휷풌 (흈) (Intercept) 0.001356*** (0.000518) 0.001198*** (0.000467) −0.001553*** (0.000561) AERWA −0.040707*** (0.016605) −0.034325*** (0.015863) −0.031346*** (0.016447) LLPRWA −0.088866*** (0.022678) −0.100098*** (0.023209) OSIERWA 0.034920*** (0.025065) 0.047423*** (0.024650) GS(International) 0.000662*** (0.000347) 0.000710*** (0.000330) GROUP(Control) 0.001076*** (0.000322) 0.000964*** (0.000307) Year(2007) 0.004959*** (0.000623) Year(2008) 0.005489*** (0.000730) Year(2009) 0.004177*** (0.000674) Year(2010) 0.002945*** (0.000568) Year(2011) 0.001185*** (0.000558) Year(2012) 0.002976*** (0.000600) Year(2013) 0.002520*** (0.000579) Year(2014) 0.002130*** (0.000630) Year(2015) 0.001645*** (0.000519) Year(2016) 0.001762*** (0.000552) No. observations 1535 1477 1477 No. units 142 142 142 푝-Value (퐹) 0.015451 < 0.000001 < 0.000001 Wooldridge test 0.110337 0.370211 0.674723 푅² 0.008605 0.034455 0.112016 Adj. 푅² 0.007958 0.031173 0.102899 Notes: *, **, *** indicates significance at the 90%, 95%, and 99% level, respectively. Source: Own calculations.

Examining the numerical variables first, the expected effects have largely been confirmed in the regression analysis. The AERWA ratio significantly negatively affects the RORWA-ratio growth. Depending on the model specification, each percentage point increase in the AERWA ratio results, ceteris paribus, in a 0.031–0.041 percentage point decrease in the RORWAYOY. Banks that are able to operate their business with a lower AERWA ratio are thus able to realize greater return growth in the following period. This links with the initial theoretical discussion regarding intermediation costs (cf. Chapter 3.2). The assumption that comparably lower intermediation costs bear higher competitive advantages, and thus greater potential for growth, was not rejected in the analysis. Furthermore, these results correspond with other empirical studies identifying that a bank’s operating expenses exert a significant inverse effect on its profitability (Athanasoglou et al., 2008, 133; Dietrich & Wanzenried, 2011, 319).

The LLPRWA ratio, which represents a bank’s overall asset quality, also features a negative coefficient sign, and thus a significant negative impact on profitability growth. A one percentage point increase in loan loss provisions determines a, ceteris paribus, 0.089–0.10 percent point lower RORWAYOY. One explanation found in the literature assumes that effective screening and monitoring instruments can better forecast the prospective risk level, enabling sustainable profit maximization. On the other hand, if the profit maximization is pursued by high-risk loans, the banks’ exposure to the growth of the accumulated amount of unpaid loans also increases (cf. e.g. Athanasoglou et al., 2008, 123, 132; Miller & Noulas, 1997, 511). These loans naturally provide lower returns, affect the bank’s overall profitability, and reduce management’s flexibility to react to market developments. Accordingly, the significantly negative impact on profitability growth is also in line with other empirical studies.

The OSIERWA ratio presents a non-significant impact on profitability growth in the full model and a significant impact at the 10% level in the full model, including time dummies. Its explanatory power regarding bank profitability growth is thus rather questionable. The calculated coefficient sign is positive, indicating the previously stated assumption that an increased OSIERWA ratio also increases action flexibility in the following period. Based on this flexibility, the bank’s management could be able to improve the RORWAYOY ratio.

Both of the remaining categorical variables present a positive coefficient sign. The business- model orientation on the international market significantly differs from the regional and the national market cultivation. Banks focusing on more than one specific country are able to generate a slightly higher return growth compared to their peer group. This could be explained with the portfolio management approach. If the management possesses more opportunities to 93 diversify the business on an international market, it should be able to generate greater profitability growth compared to a more restricted set-up. This explanation corresponds with the control group’s positive coefficient sign. The control group banks, in most cases (i.e. 70.2% of the control group subjects), pursue an international market cultivation approach. Furthermore, they are not exposed to German market competition in the same way as German market incumbents, which could be the second explanation for the significantly higher return growth.

An interesting finding was produced by including time dummies in the full model. All modelled years represent significant determinants of the RORWAYOY. Accordingly, the RORWAYOY is dependent on the overall market development in the respective year. Since the regression model assumes the year 2017 as a basis period, the regression coefficients indicate higher returns in each of the previous periods. Examining the nominal values of the coefficients, it is evident that the RORWA growth continuously decreased. Applying a 푡-test statistic on RORWAYOY to compare the first and final period, the null hypothesis that the difference of means (휇2007 =

0.0025, 휎2007 = 0.0061; 휇2017 = –0.0024, 휎2017 = 0.0045) equals zero was rejected based on a two-tailed 푝 < 0.001. One possible interpretation could be that banks optimized their RORWA ratio over the past decade and now gradually run into a recession scenario. If this assumption is applicable, the cyclical development would be comparable to the Kondratieff waves (Kondratieff & Stolper, 1935), and this finding should provoke further analysis, though exceeds the focus of the present study.

The assessment of overall model quality and its limitations was performed based on the coefficient of determination and the post-hoc power calculation in G*Power 3.1. In the first model, AERWA explains only 0.86% of the RORWAYOY variance, which, according to Cohen (1988, 413), is considered a model with a small effect size. The power calculation (푓² =

0.086797; 훼푒 = 0.05; total sample size = 1,535), however, presents a value of 0.9543, and thus only a 훽푒 error probability of 4.57%. The full model also accounts for only 3.45% of the

RORWAYOY variance. The model power (푓² = 0.0356845; 훼푒 = 0.05; total sample size = 1,477) increases to 0.99999. The final model presents an 푅² = 11.2%, which is considered a medium effect size (Cohen, 1988, 413). The power calculation (푓² = 0.1261464; 훼푒 = 0.05; total sample size = 1,477) additionally rules out any 훽푒 error probability (i.e. power = 1.000). The graphical analysis of the actual and fitted values (cf. Appendix M) indicates that the respective models account particularly well for lower growth or decrease values. Higher values in each direction, however, are not well fitted by the model. Summarizing the results, AERWA is not considered

94 the optimal determinant of RORWAYOY. However, combined with the other independent variables, the overall explanatory power of the full model, including time dummies, is considered moderate. Including more external factors in the model could be considered in subsequent research. This could help explore the annual decrease in profitability growth within the German banking industry. Nevertheless, hypothesis H1A, stating that internal bank costs significantly negatively influence its profit growth during periods of a low or zero key interest rate environment, has been confirmed.

6.2 Empirical Study: Significance of Direct Lending Industry The empirical study testing the significance of the market share of the direct lending industry in relation to the total corporate lending market and the total investment market in Germany comprises four subchapters. After selecting appropriate variables and their descriptive statistics, the research design is presented in detail. The fourth subchapter presents the results discussion and the description of limitations of the applied research approach.

The following empirical analysis requires an additional preceding disclaimer. Contrary to the other three empirical studies in this dissertation, it is in the nature of things that this analysis can only access highly limited data resources. First, the corporate direct lending market segment only came into existence in Germany in the year 2013 (cf. Figure 10). Second, the regulatory framework in place inhibits statistical deep dives, since market participants are not obliged to disclose their business data to the public. The application of an appropriate research design, as well as the limitations that follow from it, are discussed in the following subchapters.

6.2.1 Selection of Variables The first variable, net total wealth (NTW), was calculated on a quarterly basis to model Germany’s investment market. The necessary data was taken from the micro census reports provided by Statistisches Bundesamt (2014, 16; 2019, 16). The micro census is conducted periodically every five years and offers representative data concerning the number of private households, the average gross, and the average net total wealth per household in Germany. Accordingly, the statistics enable not only calculating the total wealth in Germany at the time the micro census was conducted, but also interpolating the missing values in the analysis. The compound annual growth rate (CAGR) between 2003 and 2018 was utilized for the latter. The years represent the period between the first available data set following the implementation of the euro and the newest available data set. The difference between the NTW and the gross total wealth are the average liabilities. It was assumed that the NTW is generally available for a reallocation between investment opportunities, while the additional amount covered by

95 liabilities could be subject to unknown limitations (e.g. mortgages, other collaterals). Therefore, only the NTW variable was taken into account in the subsequent calculations. One interesting finding during data collection was that the data provided by Statistisches Bundesamt (2014, 16; 2019, 16) did not correspond with the data on gross money wealth of private households provided by Deutsche Bundesbank (2020a). The cause for this deviation was that Deutsche Bundesbank (2020a) did not differentiate between wealth of German residents and wealth of foreigners with accounts in Germany. Accordingly, only the micro census data was utilized to model wealth in Germany due to its better fit.

The quarter-end outstanding amount of loans to domestic corporates and self-employed individuals (LCSI) was utilized to approximate Germany’s corporate lending market. The necessary data was taken from statistics provided by Deutsche Bundesbank (2020b). The public institution cumulates data feeds from all German banks that have to comply with the supervision mechanism. Accordingly, the data was considered resilient. The amount of total outstanding LCSI was not increased by outstanding loans provided through the direct lending channel (cf. Figure 11). This follows from the consideration that, although the direct lending market also provides loans to companies, its structure, regulatory framework, and market approach still strongly deviate from the classic banking environment (cf. Chapter 3.3.3 and Chapter 3.4).

The third and most challenging variable definition comprised the outstanding quarter-end volume of corporate direct lending loans (DLL) in Germany. Lack of transparency represents an inherent characteristic in the direct lending market. There is neither an obligation to disclose deal terms to the public nor a supervisory body that discloses market statistics on a cumulated basis. The only approach for gaining data on this market segment is to utilize information provided by private companies or advisors. Accordingly, the two primary sources were Deloitte’s (2020a) Alternative Lender Deal Tracker, which is available to the public, and GCA Altium’s (2020) MidCapMonitor, which is only available to subscribers. Neither those sources nor other subscription-based databases (i.e. Preqin, CEPRES) offered the outstanding quarter- end volume of DLL directly. Thus, calculation of said values was conducted by multiplying the respective number of outstanding loans by the average transaction volume.

Deloitte’s (2020a) database was utilized to determine the number of outstanding loans. This included, on average, 3.4 more deals per quarter than the database provided by GCA Altium in the period from fourth quarter 2012 to fourth quarter 2016. This gap decreased to an average of 0.7 deals per quarter afterwards, though Deloitte’s (2020a) database remained superior. The only data utilized from GCA Altium’s (2020) report comprised the number of transactions in 96 the fourth quarter of 2019, since this data was not available from Deloitte at the time the study was conducted.

Furthermore, it was assumed that the average loan maturity in this market segment equaled 5.2 years, corresponding with the average loan maturity of the S&P European Leveraged Loan Index (S&P Global, 2020). Accordingly, all loans concluded in the period 5.2 years prior to the respective quarter-end were considered to be still outstanding.

Finally, the average deal volume (i.e. €77.2m) was calculated based on all available estimations by GCA Altium (2020, 16) and Deloitte (2019, 5, 14). Based on data going back to the fourth quarter of 2012 and the given average maturity of 5.2 years, it was possible to model the quarter- end outstanding DLL between fourth quarter of 2017 and fourth quarter 2019 (푛 = 9). Only the disbursed DLL were considered in the following analysis. Committed and not yet disbursed amounts are situated in other asset classes at the respective point in time and would bias the ratio analysis if considered.

Subsequently, the three defined variables were utilized to calculate the DLL over NTW (DLL_NTW) ratio and the DLL over LCSI (DLL_LCSI) ratio, as was necessary for the hypothesis test.

6.2.2 Descriptive Statistics Table 25 below provides the descriptive statistics for the defined variables. Those variables neither exhibited critical skewness (Wegner, 2016, 86) nor was the platykurtic distribution considered problematic for the subsequent statistical testing. Additionally, the lack of strong variation over quarters also made the outlier elimination process dispensable. The data indicates that DLL were only able to reach an average market share of 0.2% in relation to the NTW in Germany. Although the ratio appears rather small, its relevance becomes more visible when relating it to the German alternative investment market alone (cf. distribution of wealth in Figure 13). In the second comparison, DLL reached an average market share of 0.9% compared to the LCSI amount. This also demonstrates the already solid position of the direct lending industry, considering the total number of banks covering the German corporate lending market (cf. Chapter 3.3.1).

Table 25: Descriptive Statistics of Variables for the Test of H1B Excess Variable 푴풊풏 푴풂풙 Median 흁 흈 Skewness 풏 Kurtosis DLL1 10.81 15.99 13.59 13.45 1.72 –0.06 –1.06 9 NTW1 6,458.88 6,764.59 6,609.97 6,610.70 104.65 0.02 –1.23 9 LCSI1 1,403.09 1,560.54 1,483.58 1,489.33 56.14 –0.15 –1.31 9 DLL_NTW 0.00167 0.00236 0.00206 0.00203 0.00023 –0.11 –1.03 9 DLL_LCSI 0.00771 0.01025 0.00916 0.00900 0.00082 –0.09 –0.94 9 Notes: 1 in billion euros. Source: Own calculations. The number of DLL grew over proportionally compared to NTW and LCSI in Germany. The market shares thus also increased during the observation period. The DLL_NTW ratio grew by 32.9% and the DLL_LCSI ratio by 41.2% between fourth quarter 2017 and fourth quarter 2019. Figure 14 presents the respective development of market shares.

Figure 14: Quarterly Development of Market Shares in Germany

Source: Own calculations. 6.2.3 Methodological Approach The one-sample 푡-test was applied to assess whether the respective DLL market shares significantly differed from zero. This test was utilized despite the calculated data corresponding more with the total population than with a sample from this population. However, application of a simple 푧-test was inappropriate due to the fact that the determined DLL values presented an approximation to reality while the true standard deviations of the population remained unknown.

The three assumptions for applying the one-sample 푡-test comprise the variable’s continuity (i.e. interval or ratio scale), random elicitation, and normal distribution in the population (Bortz & Schuster, 2010, 119–120). The first assumption was satisfied, since DLL_NTW and DLL_LCSI were represented by percentage values. The primary reason for the desired compliance with the second assumption concerns the elimination of possible data biases induced by samples that do not represent the population. Given the described research and

98 variable approximation approach, this assumption is not applicable in the context of this study. The test of normal distribution as well as the subsequent one-sample 푡-test was conducted with the program IBM SPSS Statistics 26. The Shapiro-Wilk test confirmed the normal distribution of both the DLL_NTW (푝 = 0.965) and the DLL_LCSI (푝 = 0.963) ratio. These results were anticipated based on the given quarterly data and its low variation. According to Bortz and Schuster (2010, 119–120), the one-sample 푡-test can be utilized independently from the sample size as long as the three assumptions are satisfied. However, larger samples generally benefit the test’s power. The latter was assessed using the G*Power tool after conducting the one- sample 푡-test (cf. Faul et al., 2009).

6.2.4 Result Discussion and Limitations Table 26 below presents the results of the one-sample 푡-tests, including the power values calculated for each test statistic. The results indicated that the DLL market share in both cases significantly differs from zero at a 1% level. Additionally, both tests exhibit strong power values, ruling out any 훽푒 error probability. This follows primarily from the small standard deviations of both variables.

Table 26: Results of One-Sample 풕-Tests Analyzing Market Shares (H1B)

Variable 흁 흈 푪푰ퟗퟓ% 푻 풅풇 풑 Power DLL_NTW 0.00203 0.00023 [0.001856; 0.002206] 26.70 8 < 0.001 1.000 DLL_LCSI 0.00900 0.00082 [0.008369; 0.009636] 32.75 8 < 0.001 1.000 Source: Own calculations.

Hypothesis H1B was derived from the impossibility of directly testing how key interest rate alterations influence the growth of the intermediation-light market niche (cf. Chapter 3.5). The test indicated that direct lending can no longer be considered a peripheral phenomenon. Instead, private debt funds and other direct lending vehicles present, on the one hand, an important source of funds for companies being purchased in leveraged transactions, and on the other hand, an investment opportunity for investors seeking adequate alternatives in the zero key interest rate environment.

The analysis is exposed to two major limitations. First, the data quantity is generally improvable. This limitation is based on the short existence of the direct lending market in Germany and can be repealed by subsequent research in the future. The second limitation concerns the data quality. As initially described, one of the upsides of the direct lending intermediation-light environment is that the regulatory obligations are negligible compared to the classical banking industry. The downside for research is that data concerning the market niche is not only hardly available, but is also currently not verifiable through official statistics.

The presented approximation approach should accordingly be recognized as such. An increase of supervision would generally benefit further research, though this would come at the industry’s expense. Nevertheless, from a logical perspective, the results should be reliable. If the one-sample 푡-tests confirm significant deviations from zero based on an approximated and probably incomplete data set, then the full data set should significantly differ from zero as well. However, this hypothesis can only be tested in subsequent studies with broader access to data.

Summarizing the discussion, the test of hypothesis H1B presented a first insight into the significant market relevance of Germany’s direct lending industry. The methodological limitations should encourage further research on the topic.

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7. Empirical Study: Corporate Finance in Times of Changing Key Interest Rates The empirical study exploring how altering key interest rates influences corporate finance comprises five subchapters. The first subchapter describes the respective model variables selected for testing the four sub-hypotheses. The second subchapter defines the sample composition as well as the control group and reviews the data collection process. Subsequently, the third subchapter presents the descriptive statistics and outlier analysis. The fourth subchapter introduces the statistical methodology and the model quality assessment. The final subchapter presents the results discussion, limitations of the empirical approach, and an outlook to further related fields of research.

7.1 Selection of Variables The percentage ratio of the cash flow from investing activity over balance sheet total

(CFI_BST) was utilized as a dependent variable for testing hypothesis H2A. The cash flow from investing activity comprises not only the net investments in tangible assets, but also the cash in- and outflows from merger and acquisition transactions. The value should thus present a useful approximation of the company’s overall investment activity. Relating capital expenditures to the balance sheet total comprises a common approach in empirical research for approximating a company’s growth (cf. e.g. Anderson & Garcia-Feijóo, 2006, 177; Titman & Wessels, 1988, 4). It should be noted that capital expenditures only account for investments in tangible assets. Excluding the merger and acquisition activities in the present empirical study, however, did not seem appropriate, since the derived hypothesis also applies for investments in other companies. The assumption was that the lower the key interest rate is, the lower the investment return threshold becomes (cf. Chapter 4.1). Companies can therefore increase their investment activity and realize more projects with lower returns. The characteristics of the project itself are irrelevant; this could be the purchase of a new machine or the purchase of a new company. Therefore, the cash flow from investing activity related to the BST to assess the company’s overall investment activity in relation to its current asset portfolio.

Hypothesis H2B assumes that a company’s increased investment activity should lead to an overall lower return on assets. This follows from the lower return threshold, which enables executing more projects with lower profitability. The ratio of earnings before interest and taxes over balance sheet total (EBIT_BST) was employed to test this assumption. Applying the EBIT as a profitability proxy should mitigate the impact of heterogeneous tax effects and financing structures (cf. Chapter 4.3.2). Utilizing the ratio itself is common practice in scientific literature. Especially in the research of capital structure determinants, most authors relate either the EBIT 101 or the EBITDA to the amount of a company’s total assets (cf. e.g. Huang & Song, 2006, 18; Saurabh & Sharma, 2015, 7; Schneider, 2010, 209–210).

Following Keynes’ explanation of the speculative reserve (cf. Chapter 2.3.4), hypothesis H2C assumes an inverse relationship between key interest rates and the cash holdings of a company. The ratio of cash and cash equivalents over balance sheet total (CASH_BST) was defined as dependent variable to test this hypothesis. This ratio also represents a common operationalization approach in scientific literature for assessing a company’s cash holdings position (cf. e.g. Opler, Pinkowitz, Stulz, & Williamson, 1999, 15; Pinkowitz, Stulz, & Williamson, 2015, 346). The rational calculation mitigates currency and size issues, and thus enables a reliable sample subject comparison.

Finally, the fourth dependent variable was defined as the ratio of total financial liabilities over balance sheet total (TFL_BST). Utilizing the total financial liabilities rather than the total liabilities resulted from the different initial and subsequent measurements of these positions under IFRS (cf. Chapter 4.3.1). Accordingly, this approach should minimize the risk of accounting-implied distortions following from the alteration of the main determinant. In contrast to this, empirical studies focusing on capital structure determinants have largely utilized the ratio of the book or market value of the total liabilities over balance sheet total (cf. e.g. Bevan & Danbolt, 2002, 162–163; Schneider, 2010, 172–173).

The main determinant for testing all four hypotheses was defined as the period-weighted average key interest rate (KIR). The monthly weighting of the key interest rate accounts for its intra-year alterations. This approach ensures that the collected annual values from the company’s financial report also relate to an annual key interest rate instead of a reference date value. As described in the previous chapters, the KIR value should, in most cases, inversely affect the dependent variables. The relation to the EBIT_BST ratio presents an exception. Here, the decrease of the key interest rate should also result in a decrease of the EBIT_BST ratio.

The first group of independent control variables considers the company’s profitability. Herein, the EBIT_BST ratio was employed as an independent variable in the three models emphasizing different dependent variables. The ratio of earnings before interest and taxes over revenue (EBIT_R) was added as an additional determinant (cf. e.g. Ammann, Oesch, & Schmid, 2011, 42). It was expected that including more ratios from different levels of the profit and loss statement (e.g. EBITDA, EBT, or EAT) would result in a multicollinearity issue. Therefore, the number of profitability-related dependent variables was limited to both above-mentioned ratios.

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The expected effect on the dependent variables differs between hypotheses. It was expected that the profitability ratios would positively influence both the CFI_BST and the CASH_BST ratio. The higher the company’s profitability, the greater its cash-generating power and the greater its possibility to invest should be. As already described in Chapter 4.5, profitability’s influence on a company’s lending strategy is considered to be indeterminate. According to the pecking order theory, the TFL_BST ratio should decrease with rising profitability, as the company would be able to fund new projects from internal sources. The trade-off and the signaling theory, on the other hand, expect a rising TFL_BST ratio due to the company’s increased creditworthiness. The profitability-related independent variables were not applicable

(N/A) in the H2B model.

The second group of independent control variables focuses on the balance sheet relations of the respective company. Examining the assets side first, the ratio of fixed assets over balance sheet total (FA_BST) was included within the model to control for the company’s capital intensity (cf. Schneider, 2010, 222). It was expected that companies with higher FA_BST ratios have to invest more to maintain their capital-intensive business model, that their assets generate comparably higher profits, and that the cash holdings should be lower compared to other companies. It was also expected that these companies relate more on debt financing, which should result in a higher TFL_BST ratio. The ratio of total liabilities over balance sheet total (TL_BST) was used to control for the company’s financing structure (cf. e.g. Bevan & Danbolt, 2002, 162–163). It was expected that companies with higher TL_BST ratios tend to have lower CFI_BST ratios due to a more restricted scope of financing opportunities (cf. e.g. Aivazian, Ge, & Qiu, 2005, 281–284). The expected effect with regards to the EBIT_BST ratio remains indeterminate. It could be argued that either the return threshold is reduced by an increasing liabilities proportion, since debt capital is cheaper than equity (cf. e.g. the results provided by Pattitoni, Petracci, & Spisni, 2014, 768), or that the return on equity comprises an imputed value and that the company is forced to generate higher returns for settling its increased payment obligations (cf. e.g. the number of reported positive relationships in Capon, Farley, & Hoenig, 1990, 1149). The expected effect on the CASH_BST ratio is inverse. The higher the company’s indebtedness, the lower its cash holdings should be (cf. e.g. Pinkowitz et al., 2015, 322). The

TL_BST ratio was not included within the H2D model due to the expected similarity between both ratios.

The third group of independent control variables emphasizes two cash-related ratios. The first determinant was defined as the ratio of cash flow from operating activities over revenue

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(CFO_R). The ratio represents the company’s internal financing capacity by calculating the proportion of cash remaining after operational disbursements (cf. e.g. Giacomino & Mielke, 1993, 56). It was expected that the higher a company’s CFO_R ratio, the greater its capacity to invest, its profitability, and its cash reserves should be. A negative relationship was expected between the CFO_R and the TFL_BST ratio. If a company is able to generate higher amounts of cash from its revenues, it should also possess a greater capacity to pay back its debt. This expectation corresponds with the above-mentioned relationship between the TL_BST and the CASH_BST ratio (cf. e.g. Pinkowitz et al., 2015, 322). The CFI_BST ratio was included within this group as a second control variable. To maintain consistent assumptions, the expected effect on the EBIT_BST and the TFL_BST ratio is positive. Accordingly, a higher investment ratio should result in greater profitability, but also in higher company indebtedness. The impact on the CASH_BST ratio, on the other hand, was expected to be inverse. Companies were expected to utilize the free liquidity for their investment projects.

The fourth group of determinants contains size proxies. The company revenue and the BST were converted by a natural logarithm transformation and utilized as independent variables (i.e. LOG_R and LOG_BST). The effects on the dependent variables were expected to share the same coefficient sign, and these are thus not discussed separately. It was expected for the size proxies to positively influence the TFL_BST ratio, since larger companies tend to carry more debt (cf. e.g. Barclay, Marx, & Smith, 2003, 158, 160). The impact on the EBIT_BST ratio was expected to be negative, as larger companies tend to have less flexible organizations and to favor asset expansions rather than profitability goals (cf. e.g. Goddard, Tavakoli, & Wilson, 2005, 1277; Pattitoni et al., 2014, 767–769). Corresponding with this explanation, the impact on the CFI_BST ratio was expected to be positive. Following the results of Opler et al. (1999, 24), the impact of size on the CASH_BST ratio was expected to be negative.

One categorical variable was added to the model to control for market effects. This variable represents the company’s country of headquarters (COH). Since all members of the Eurozone are exposed to the ECB’s monetary policy, a differentiation is only made between Germany and all other Eurozone countries. All other countries of the control group were included with their respective country code. The a priori assessment of the coefficient sign is exposed to a manifold of macro- and microeconomic factors. The expected effect of the COH variable was thus considered indeterminate. Table 27 below presents a summary of the model variables, their respective descriptions, and their expected effect on the dependent variables in each sub-model.

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Table 27: Definition and Description of Variables for the Test of H2 Dependent Reference Description Variables Hypothesis CFI_BST Cash flow from investing activity over balance sheet H2A total (%) EBIT_BST Earnings before interest and taxes over balance sheet H2B total (%) CASH_BST Cash and cash equivalents over balance sheet total (%) H2C

TFL_BST Total financial liabilities over balance sheet total (%) H2D

Independet Expected Effect in Description Variables H2A H2B H2C H2D KIR Period-weighted average key interest rate (%) – + – – EBIT_BST Earnings before interest and taxes over balance sheet + N/A + +/– total (%) EBIT_R Earnings before interest and taxes over revenue (%) + N/A + +/– FA_BST Fixed assets over balance sheet total (%) + + – + TL_BST Total liabilities over balance sheet total (%) – +/– – N/A CFO_R Cash flow from operating activity over revenue (%) + + + – CFI_BST Cash flow from investing activity over balance sheet N/A + – + total (%) LOG_R Log transformation of the revenue + – – + LOG_BST Log transformation of the balance sheet total + – – + COH Dummy variable for country of headquarters +/– +/– +/– +/– Source: Own representation. 7.2 Composition of Sample and Control Group The test of the respective corporation-related hypotheses necessitated a representative composition of the sample group. As previously described in Chapter 4.3.4, the trade-off to consider first concerned whether to utilize stock-listed or private companies in the sample. While private companies offer a larger data pool and should be less exposed to accounting- implied distortions of balance sheet positions, stock-listed companies present greater transparency based on regulatory disclosure requirements (cf. e.g. transparency requirements of Prime Standard segment in Deutsche Börse AG, 2020, 3). The disclosure of a cash flow statement, for instance, is only obligatory for either capital-market-oriented private companies or private companies that have to prepare a consolidated financial report (cf. Handelsgesetzbuch, 2019, §§ 267, 294). Since this study required values from the cash flow statement, this regulation narrows down the available population. Additionally, this population is not systematized in any public directory. The possibility of random draws is thus strongly limited. These two challenges, combined with the overall data accessibility and the already considered impacts of key interest rate alterations on the respective financials (cf. Chapter 4.3 and Chapter 4.4), led to the decision to utilize stock-listed companies in the present empirical study. The determination of the necessary sample size was performed with the G*Power 3.1

105 tool (cf. Faul et al., 2009). Given a medium effect size of 푓2 = 0.15 (Cohen, 1988, 412–414), a conventional 훼푒 error probability of 5%, a power of 80% (fourfold 훽푒 error probability of 20%), and a total of 10 predictor variables, the a priori sample size should include at least 118 test subjects.

The Prime All Share Index (Deutsche Börse AG, 2019) was utilized to select the sample subjects. The index includes a total of 318 companies listed in the Prime Standard, the German stock segment with the highest transparency requirements (Deutsche Börse AG, 2020, 1). The data collection started with the year 1999, which marks the beginning of the mutual monetary policy in the Eurozone (Scheller, 2006, 27). Due to deviations between the calendar and fiscal year, some companies already presented their annual reports for 2019 before the data collection. Therefore, the last available period of the data set was the year 2019, though this did not apply to all sample subjects.

The analysis of transnational implications of key interest rate alterations required defining a control group outside Germany. This control group should cover regions in which the companies were also exposed to a zero interest rate environment. Accordingly, the U.S., Japan, and the other Eurozone countries excluding Germany (cf. Figure 3) were selected due to their large data pools. The control group consists exclusively of stock-listed corporations, since the data collection would otherwise be restricted by language and data-accessibility barriers. The S&P 500® index, which covers approximately 80% of the overall market capitalization (S&P Dow Jones Indices, 2020, 1), was utilized to select the sample subjects for the U.S. The index includes a total of 505 rather than 500 companies, because five constituents have two share classes of stock. The Japanese premier index Nikkei 225 was utilized to select sample subjects for the Japanese market (Nikkei Indexes, 2020). Finally, the EURO STOXX® index was selected as a data source for the Eurozone selection. The index covers a total of 305 constituents that, however, also include German companies (STOXX, 2020). Excluding those companies, the index presents 231 sample subjects for the Eurozone (excluding Germany) market.

From the total of 1,279 sample subjects, 244 banks and financial service, insurance, and real estate investment trust companies were excluded due to their industry affiliation. This procedure is common in scientific literature, because those companies typically present industry-specific financing structures and investment behaviors (cf. e.g. the research results and approaches of Diamond & Rajan, 2000, 2431; Feng, Ghosh, & Sirmans, 2007, 81; Psillaki & Daskalakis, 2009, 326). A further 153 companies were excluded due to an overly short data track record. The threshold for this drop out was set at the reference year 2006. If the company 106 performed its initial public offering after 2006, it is missing more than 40% of the necessary data elements. This fact triggered the respective exclusion. Companies that exited the indices before 2019 were not taken into account. This approach was considered uncritical due to a limited relevance of the survivorship bias in the present empirical study (cf. e.g. Elton, Gruber, & Blake, 1996). Based on those preliminary works, the Thomson Reuters Datastream database was employed in the following step to retrieve the numerical variables for the remaining sample subjects. The data query included the condition of retrieving only originally reported values and the request of exchanging all foreign currencies into euros. The latter was necessary for transnational comparability of the logarithm-transformed variables.

The subsequent data inspection revealed two major problems. First, companies that changed their reporting period between 1999 and 2019 exhibited incomplete data sets. The data mostly included one shortened fiscal year and several years without any values at all. The second problem was that the Thomson Reuters Datastream outputted distorted data for several companies, presenting negative revenue or cash values. The companies affected by the described issues, double entries resulting from different share classes, and companies with a secondary listing in the respective region were excluded from the study. This drop-out of 85 companies led to a remaining data set of 797 sample subjects. Table 28 below presents an overview of the utilized indices, the number of exclusions, and the final number of sample subjects per region. The individual companies are listed in Appendix N in alphabetical order for each index.

Table 28: Composition of the Sample Subjects Eurozone Region Germany USA Japan (ex Germany) Prime All EURO Index S&P 500® Nikkei 225 Share STOXX® Constituents 318 231 505 225 – Industry 49 59 109 27 – Insufficient Timeframe 69 25 50 9 – Insufficient / Improper Data 45 7 30 3 = Sample Subjects 155 140 316 186 Source: Own representation. A total of 417 missing data elements were replenished in the second data inspection step. These data elements were taken from annual reports collected from the respective company’s website. The secondary objective of this report collection was to assess the overall quality of the data retrieved from the Thomson Reuters Datastream. For this purpose, the financials from the reported and the previous year were compared to the data-retrieval results. This approach

107 covered approximately 5.3% of the overall dataset and led to the conclusion that the data delivered by the Thomson Reuters Datastream was of high quality, since it almost entirely corresponded with the actually reported values (i.e. 99.52% of the assessed data elements). Consequently, no further data adjustments were performed. Figure 15 below presents an overview of the annual distribution of the data elements. It can be seen that, based on the threshold approach, none of the 797 companies featured missing values starting from 2007. The strong discrepancy in complete data sets between the year 1999 and 2000 comes from the Japanese sample. The disclosure of the cash flow statement became obligatory in Japan only in the financial year 2000 (cf. Mizuno, 2004, 359). Accordingly, the data pool does not include Japanese companies in the financial year 1999, leading to lower quantity.

Figure 15: Annual Distribution of Complete Data Sets

Source: Own representation. In total, 158,020 numerical data elements have been calculated based on the data retrieved from the Thomson Reuters Datastream. Those values were supplemented by 797 categorical data elements (i.e. COH) and systematized in Microsoft Excel for further analysis. Finally, the KIR was added to the data pool. For Germany and the rest of the Eurozone, the ECB’s main refinancing facility was employed as a reference rate. The BoJ’s basic loan and discount rate was utilized as a key interest rate reference for Japanese companies. Finally, the federal funds target rate of the Fed was utilized as a reference key interest rate for the U.S. companies. All key interest rate values were retrieved from the Thomson Reuters Datastream.

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7.3 Descriptive Statistics and Elimination of Outliers The following preparatory step covered the descriptive analysis of the collected data elements and the elimination of outliers. Neither size proxy—revenue and BST—was represented by a percentage value, and thus exposed to a positive skew. To counter this substantial skewness, the variables were converted by a natural logarithm transformation (cf. e.g. Kuhn & Johnson, 2013, 31–33) to LOG_R and LOG_BST. In contrast to the empirical study about determinants of bank profitability growth (cf. Chapter 6.1), the natural logarithm transformation did not completely resolve the outlier issue, and both variables were accordingly included in the subsequent outlier analysis. Appendix O presents the histograms and the density functions of the original size proxies as well as their logarithm-transformed and outlier-adjusted counterparts. The numerical analysis is presented in Table 29.

According to the resistant rules for outlier labeling by Hoaglin and Iglewicz (1987), specific values should be labeled as outliers when their distance from the first or third quartile value exceeds the 2.4-fold interquartile range (Hoaglin & Iglewicz, 1987, 1149). This follows from the present sample size and the desired 훼푒 error probability. The outlier calculation was performed on an annual basis for all 10 numerical variables. Based on this approach, 2,492 out of 158,020 values were identified as outliers (i.e. EBIT_BST: 376 values; EBIT_R: 396 values; CASH_BST: 475 values; FA_BST: 0 values; TL_BST: 42 values; TFL_BST: 39 values; CFO_R: 421 values; CFI_BST: 642 values; LOG_R: 52 values; LOG_BST: 49 values), equaling 1.58%. Based on this low overall percentage, the decision was made to exclude the majority of outlier values from the data set to mitigate result distortions. An exception was made for the CASH_BST variable, since high cash hoardings of a company present one of the particular topics of interest. After excluding the data elements, all companies exposed to a data loss of more than 20% were completely removed from the data pool. This exclusion pertained to only three companies (i.e. Artnet, Gilead Sciences, and Teles).

Table 29 below illustrates the descriptive statistics before and after outlier elimination. The first statement to make focuses the skewness and kurtosis statistics. All skewness values changed to a moderate (i.e. skewness coefficient > –1 and < +1) level after the outlier elimination, presenting more symmetrical distributions than before (cf. Wegner, 2016, 86). The elimination also resolved or at least mitigated the fat tail issue in most cases. One exception was the variable CASH_BST, which, based on the applied outlier procedure, exhibited only slightly modified statistics. The second spotlight can be directed at the range between the minimum and maximum values of each variable. The ranges and standard deviations decreased expectedly without strongly affecting the distribution’s mean and median values. 109

Table 29: Descriptive Statistics of Numerical Variables for the Test of H2 Dependent Excess 푴풊풏 푴풂풙 Median 흁 흈 Skewness 풏 Variables Kurtosis CFI_BST1 –0.9660 1.5961 –0.0552 –0.0673 0.0851 –0.16 25.27 15,802 CFI_BST –0.4111 0.1631 –0.0543 –0.0624 0.0574 –0.92 2.87 15,113 EBIT_BST1 –1.9179 0.6500 0.0760 0.0859 0.0913 –3.79 65.09 15,802 EBIT_BST –0.1779 0.3501 0.0757 0.0867 0.0633 0.58 1.19 15,395 CASH_BST1 0.0000 0.8961 0.0720 0.1016 0.1023 2.35 7.91 15,802 CASH_BST 0.0000 0.8961 0.0718 0.1011 0.1017 2.35 7.99 15,742 TFL_BST1 0.0000 6.7622 0.2409 0.2514 0.1886 5.79 149.64 15,802 TFL_BST 0.0000 1.0042 0.2407 0.2481 0.1620 0.41 –0.13 15,710

Independet Excess 푴풊풏 푴풂풙 Median 흁 흈 Skewness 풏 Variables Kurtosis KIR 0.0000 0.0621 0.0070 0.0149 0.0162 1.16 0.27 15,742 EBIT_R1 –35.7786 0.7015 0.0968 0.0983 0.4675 –45.33 2748.6 15,802 EBIT_R –0.2316 0.4564 0.0966 0.1125 0.0868 0.69 1.10 15,376 FA_BST1 0.0047 0.9756 0.5646 0.5664 0.1980 –0.07 –0.61 15,802 FA_BST 0.0047 0.9756 0.5654 0.5672 0.1975 –0.06 –0.62 15,742 TL_BST1 0.0378 11.2184 0.6064 0.5968 0.2413 11.50 419.16 15,802 TL_BST 0.0378 1.3709 0.6060 0.5917 0.1882 –0.23 –0.05 15,711 CFO_R1 –28.3041 5.8759 0.1098 0.1252 0.3213 –51.51 4,108.00 15,802 CFO_R –0.2081 0.5157 0.1087 0.1269 0.0937 0.76 0.93 15,338 Revenue1, 2 0.0004 450.0885 5.0202 13.3709 26.3548 5.80 53.34 15,802 LOG_R 14.2822 26.8327 22.3487 22.1438 1.7791 –0.70 0.72 15,711 BST1, 2 0.0012 570.7319 6.4039 18.4009 38.1318 6.02 54.96 15,802 LOG_BST 15.3734 27.0702 22.5859 22.3697 1.8650 –0.68 0.48 15,718 Notes: 1 before outlier elimination; 2 in billion euros. Source: Own calculations. Through their mean values and standard deviations, the profitability-related variables EBIT_R and EBIT_BST demonstrated that negative returns on revenue or balance sheet total present more exceptional than regular cases. The profitability in relation to revenues illustrates a mean of 11.25%, and is thus greater than the profitability in relation to the balance sheet total, which only has a mean value of 8.67%. The unadjusted CASH_BST ratio variates between 0.00% and 89.61% with a mean value of 10.11%. The TFL_BST ratio demonstrates a range between 0.00% and 100.42%. The latter follows from a negative equity position in the balance sheet. The average financial indebtedness, however, represents 24.81% of the balance sheet total. The FA_BST ratio’s mean of 56.54% indicates that companies predominantly invest in fixed assets. These investments are, on average, not fully covered by equity capital, since the TL_BST ratio exhibits a mean value of 60.60%. However, the minimum values of the TFL_BST (i.e. 0.00%, cf. histogram in Appendix P) and the TL_BST (i.e. 3.78%) ratios also reveal that certain companies in the sample are predominantly financed by equity capital, and vice versa (cf. maximum values). The CFI_BST ratio indicates that companies, on average, invest 6.24% of their balance sheet total in assets or target companies. The range between –41.11% and 16.31% also displays that disinvestments can lead to a positive cash flow development. The CFO_R ratio presents a range between -20.81% and 51.57% with a mean value of 12.69%. Finally, the 110

KIR ratio reveals a range between 0.00% and 6.21% for all three central banks. The average key interest rate for the period between 1999 and 2019 equals 1.49%, though this is influenced by the Japanese rates, which have been low for the total period of analysis (cf. Figure 3). The histograms and the density functions of the described variables are presented in Appendix P. Supplemental material is provided in Appendix Q to assess the year-over-year development of the variables.

7.4 Research Design A panel regression was applied to analyze the longitudinal data set. This data set represents an unbalanced, short, and wide panel. Whether to apply the FEM, which controls for subject- specific heterogeneity (i.e. within variation effects); the REM, which additionally controls for an inter-subject residual variable (i.e. between variation effects); or the pooled regression model, which assumes the absence of subject-specific influences was decided by applying statistical tests (cf. Malitte & Schreiber, 2019, 389–405). The 퐹-test statistic reveals whether the sample subjects share a common intercept and if the pooled regression model should be preferred in favor of the FEM. The Hausman (1978) test statistic evaluates the consistency of the generalized least squares estimates. If the estimates are consistent, the REM should be preferred in favor of the FEM (cf. Malitte & Schreiber, 2019, 398–399, 405).

Since the four sub-hypotheses consider different dependent variables, the tests were performed separately for each set-up. Starting with hypothesis H2A and running the full as well as several reduced models led to the conclusion to apply the FEM. Both the null hypothesis in favor of the pooled regression model and the null hypothesis in favor of the REM were rejected at a 5% level. The same conclusion in favor of the FEM was derived from the calculations related to hypothesis H2B. The model preference for hypotheses H2C and H2D, on the other hand, was dependent on the chosen variables. In both cases, the simplest model, which contained only KIR as the explanatory variable, presented results in favor of the REM. With the addition of further control variables within the respective model, the Hausman (1978) test began rejecting the null hypothesis in favor of the FEM. Consequently, the FEM is applied to test all four sub- hypotheses. This decision, however, led to the exclusion of the COH dummy variable. Its time invariance resulted in a perfect collinearity with the unobserved individual effect of the respective sample subject which is modelled through a corresponding dummy (cf. Gujarati & Porter, 2009, 600–601). To resolve this issue, gretl automatically drops out all categorical variables. All calculated test statistics are listed in Appendix R.

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Formula 8 below illustrates the applicable fixed effects model equation. It should be noted that the mean of subject-specific intercepts results from a software-related issue and would normally not be included in the equation. The standard output of gretl does not include subject-specific intercepts based on their large numbers. Instead, the results presentation includes the mean of those values and offers the possibility for retrieving the individual intercepts if required (cf. Cottrell & Lucchetti, 2020, 185, 189; Malitte & Schreiber, 2019, 399). The variable had been included within the formula for traceability of the subsequent result presentation and discussion.

with:

푦̃푚푡∗ Mean-corrected observed criterion 푦 of subject 푚 in period 푡∗

(푦̃푚푡∗ = 푦푚푡∗ − 푦̅푚) ̅̅̅̅ 푎̂푚 Mean of subject-specific intercepts 퐾 훽푘 Regression coefficient 푘 ∗ ̅̅̅̅ ∗ ∗ (8) 푦̃푚푡 = 푎̂푚 + ∑ 훽푘 ∗ 푥̃푘푚푡 + 푢푚푡 푥̃푘푚푡∗ Mean-corrected observed determinant 푘=1 ∗ 푥푘 of subject 푚 in period 푡 (푥̃푘푚푡∗ = 푥푘푚푡∗ − 푥̅푘푚) 푚 Index of subjects (푚 = 1, . . . , 푀) 푘 Index of determinants (푘 = 1, . . . , 퐾) 푡∗ Index of periods (푡∗ = 1, … , 푇∗) ∗ 푢푚푡∗ Residual value of subject 푚 in period 푡

The regression diagnostics for assessing possible assumption violations were conducted separately for each model and included all relevant numerical determinants. The assessments analyzed whether a linear relationship exists between the determinants (i.e. multicollinearity), whether the residual values are correlated (i.e. autocorrelation), and whether the variance of the residuals is identical and finite for all observations (i.e. homoscedasticity) (cf. e.g. Auer & Rottmann, 2011, 445–452; Gujarati & Porter, 2009, 61–69). The calculation methodology of the FEM, however, required applying different testing methods than in the pooled regression model (cf. Chapter 6.1.4). The condition index methodology by Belsley et al. (2004, 105) was utilized for the multicollinearity test. As proposed by Greene (2003, 323–324), the distribution- free Wald test for heteroscedasticity was employed to analyze the variance of residuals. The Wooldridge (2002, 282–283) test for autocorrelation in panel data was utilized for the third diagnostic.

Starting with the multicollinearity analysis and calculating the respective models with all numerical variables, supplemented by time dummies, revealed a common problem. The size dummies LOG_R and LOG_BST are, again (cf. Chapter 6.1.4), exposed to a multicollinearity issue. They highly correlate with not only each other (푟 = 0.95), as expected, but also with the 112 subject-specific effect dummy calculated when utilizing the FEM. This follows from the circumstance in which the logarithm-transformed values present only a small variation in the analyzed period of time. This leads to high correlation between the size proxy and the subject- specific effect dummy, which is stable in all periods. For each model, the collinearity diagnostics reveal that both size proxies possess variance proportions over 50% in condition indices, indicating strong (i.e. condition index ≥ 30) near-linear dependencies (Belsley et al., 2004, 105). Since the subject-specific effect dummy represents an indispensable element of the FEM, both size proxies were omitted from the respective model to countermeasure biased estimates (Auer & Rottmann, 2011, 513–514).

The second multicollinearity issue resulted from the strong correlation between CFO_R and

EBIT_R (푟 = 0.71). Consequently, the variable EBIT_R was excluded from the H2A, the H2B and the H2D model. Only the BIC in the H2A model improved based on excluding the three determinants. All other information criteria revealed increased values afterwards. Although losing the size proxies and one of the profitability-related determinants as control variables is problematic regarding the model’s overall explanatory power, the main measure and numerous other control variables remained unaffected, and were thus utilized for the model estimation.

Analyzing the other model assumptions next led to the result that each model is exposed to heteroscedasticity and autocorrelation. Consequently, the robust standard errors by Arellano (1987) were utilized in the FEM estimation to mitigate these assumption violations and increase estimator efficiency (for a reasoning in favor of Arellano’s robust estimator cf. Cameron & Trivedi, 2005, 705–708). A summary of all regression diagnostics is presented in Appendix S.

Irrelevant determinants were identified based on the respective robust standard error FEM. The variables were excluded step-wise, starting with the coefficient with the highest 푝 value and step-by-step progressing to the threshold of 푝 > 0.20 (i.e. fourfold 훼푒 level). The detailed process with 퐹-test statistics and the development of the three information criteria is presented in Appendix T. It can be seen that, in most cases, the omitted variables comprised specific years, which did not significantly affect the dependent variable. However, two exceptions were found.

The first exception concerned the KIR’s irrelevance in model H2B. With KIR as the model’s main measure, the subsequent result discussion was already partially anticipated at this point. The coefficient presents a 푝 = 0.8929 in the full model including the adjusted time dummies, and thus no value added for the model. Consequently, it was included in the reduced and full, but not in the third, model estimation. The second exception was variable CFI_BST in model

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H2D. Since, in this case, the variable represented only a control measure, it was completely excluded from the following model estimations. This final step concludes the general-to- specific modeling approach (for an overview cf. Campos, Ericsson, & Hendry, 2005).

7.5 Result Discussion and Limitations This chapter presents the regression results in four subchapters corresponding to the four corporation-related research hypotheses.

7.5.1 Impact of Key Interest Rates on Investment Activity (H2A) Table 30 below comprises the regression results, differentiating between the main measure, the full model containing the remaining determinants, and this full model supplemented by time dummies not removed in the step-wise optimization process (cf. Chapter 7.4).

Table 30: Results of Regression Analyzing Determinants of Investment Activity (H2A) Full Model Main Measure Full Model incl. Time Dummies 휷풌 (흈) 휷풌 (흈) 휷풌 (흈) Global mean –0.054381*** (0.000628) –0.014837*** (0.007361) −0.010045*** (0.007339) KIR –0.543951*** (0.042463) –0.602055*** (0.042878) −0.322248*** (0.057589) EBIT_BST –0.112198*** (0.016336) −0.114548*** (0.016238) FA_BST –0.040572*** (0.009178) −0.048211*** (0.009125) TL_BST 0.006789*** (0.006959) 0.013529*** (0.006881) CFO_R –0.073856*** (0.013451) −0.083680*** (0.013076) Year(1999) −0.045083*** (0.004668) Year(2000) −0.028206*** (0.003212) Year(2001) −0.016190*** (0.002663) Year(2002) −0.010176*** (0.002055) Year(2006) −0.003441*** (0.002134) Year(2007) −0.006306*** (0.002280) Year(2008) −0.013091*** (0.002117) Year(2009) −0.007358*** (0.001757) Year(2010) −0.002956*** (0.001579) Year(2011) −0.005571*** (0.001729) Year(2012) −0.009218*** (0.001648) Year(2013) −0.006112*** (0.001527) Year(2014) −0.004531*** (0.001572) Year(2015) −0.006043*** (0.001666) Year(2016) −0.002811*** (0.001714) No. observations 15,113 14,516 14,516 No. units 794 791 791 퐿푆퐷푉 푅² 0.273002 0.291095 0.310555

푅²푤푖푡ℎ푖푛 0.026042 0.049951 0.076030 Notes: *, **, *** indicates significance at the 90%, 95%, and 99% level, respectively. Source: Own calculations. The main determinant’s expected effect was confirmed in none of the three FEMs. The CFI_BST ratio was coded from the IFRS cash flow statement, meaning that higher negative

114 values represent higher cash outflows for investments in relation to the balance sheet total. The negative sign of the KIR coefficient accordingly means that increasing the key interest rate leads to a corresponding increase in investment activity. To be precise, each percentage point KIR increase results, dependent on the model specification and under ceteris paribus conditions, in a 0.32–0.60 percentage point increase of the CFI_BST ratio. This finding empirically rejects the previously discussed theories, which assume an inverse relationship between interest rates and investment activity (cf. e.g. Clark, 1908, 186; Keynes, 2018, 124; Smith, 1776a, 426, 434–436). The negative sign of the coefficient remains stable in lagged model set-ups (i.e. assuming an up to a 10-year delayed impact of the key interest rate alteration on investment activity). Keynes’ (2018, 177–180) concept of the speculative reserve could offer a possible explanation for this finding. The assumption is that companies are less willing to surrender their liquidity in times of low key interest rates because they cannot anticipate future market developments (e.g. rising or further falling interest rates). Instead, they could reduce their investment activities and build up a speculative reserve to be prepared for exploiting appropriate market opportunities. This explanation approach is analyzed further in Chapter 7.5.3, which focuses on cash holdings.

The expected effect of determinant EBIT_BST has been confirmed in each model specification. The profitability proxy reveals that the higher the company’s profitability, the greater its investment activity becomes. Each percent point increase in the EBIT_BST ratio leads, ceteris paribus, to an approximate 0.11 percent point increase in the CFI_BST ratio. The proposed explanation for this relationship is that profitable companies should possess a larger financial scope to invest in new assets, and that they utilize this accordingly.

The assumption that companies with a higher FA_BST ratio have to invest more to maintain their capital intensive business model has been confirmed. According to the FEM results, each percentage point increase in the fixed assets proportion of the balance sheet total leads to a 0.041 or 0.048 percent point increase in investment activity. This investment approach is, for example, necessary to mitigate an investment backlog. Companies that switch their business model from capital-intensive to asset-light approaches could be considered exceptions from this rule. The data set, however, represents on average the aforementioned assumption.

The TL_BST ratio illustrates an insignificant result in the full model and a significant result at the 5% level in the full model, including time dummies. The positive sign of the coefficient corresponds with the expected effect. Companies with higher TL_BST ratios tend to feature lower CFI_BST ratios. A possible explanation for this more restricted investment activity could 115 comprise the lower scope of financing opportunities. Comparable results can be found in other empirical studies (cf. e.g. Aivazian et al., 2005, 281–284).

The regression analysis also confirms the expected effect of the last numerical value—namely, the CFO_R ratio. Accordingly, each percentage point increase in the CFO_R ratio leads to a 0.07 or 0.08 percentage point increase in the CFI_BST ratio. The explanation for the EBIT_BST ratio effect can also be applied at this point. Companies possessing higher operative cash flows should, ceteris paribus, also possess a larger financial scope to invest in new assets.

The remaining time dummies illustrate an interesting finding. Companies seem to have invested significantly more in the years shortly before and during times of crisis (cf. e.g. Hartmann & Smets, 2018, 8). The years 1999–2002 (i.e. dot-com crisis), 2007–2009 (i.e. financial crisis), and 2011–2015 (i.e. European debt crisis) present significant results at the 1% level. A possible explanation could be that companies exploit decreasing prices in times of crisis to purchase new assets for their businesses. This anticyclical approach, however, is only possible with an adequate amount of financial means.

The model quality assessment was performed based on the least square dummy variable coefficient of determination (i.e. 퐿푆퐷푉 푅²) and the within coefficient of determination (i.e.

푅²푤푖푡ℎ푖푛). The first assumes that the model includes a full set of subject-specific effect dummies and provides correlation between the actual and fitted dependent variables based on this assumption (Cottrell & Lucchetti, 2020, 190). Accordingly, the value should be rather high, since the model should explain the between-variance comprehensively (Andreß, Golsch, & Schmidt, 2013, 138–139). The latter demonstrates the goodness of fit for the demeaned variables, disregarding the between-variation (Cottrell & Lucchetti, 2020, 190). Thus, it can be seen as goodness of fit coefficient, which can be compared to the regular 푅² and interpreted accordingly (Cottrell & Lucchetti, 2020, 190; StataCorp, 2013, 368). In addition to the aforementioned, the post-hoc power calculation was conducted using the G*Power 3.1 tool. All three models provide 푅²푤푖푡ℎ푖푛 values, implying small effect sizes, according to Cohen (1988, 413). The 퐿푆퐷푉 푅² values demonstrate that the determinants are only able to explain approximately a third of the between-variation, rather low considering the model set-up. The power calculation for all three models (i.e. main measure: 푓² = 0.0267383; 훼푒 = 0.05; total sample size = 794; full model: 푓² = 0.0525773; 훼푒 = 0.05; total sample size = 791; full model including time dummies: 푓² = 0.0822862; 훼푒 = 0.05; total sample size = 791) returns power values > 0.995, almost ruling out any 훽푒 error probability. The graphical analysis of the actual and fitted values (cf. Appendix U) indicates that the respective models are only able to explain 116 a moderate portion of the overall variation. However, the significance of the respective determinants combined with the test power provides a useful foundation for further research.

Summarizing the results, KIR is not considered an optimal determinant of the CFI_BST ratio. Moreover, the coefficient’s negative sign contradicts the initial theoretical assumptions and leads to rejecting hypothesis H2A. Nevertheless, the regression analysis provides valuable insights into how key interest rate alterations influence corporations’ investment activity. The discovered effects of the main measure and the control variables can be utilized for further model optimizations.

7.5.2 Impact of Key Interest Rates on Return on Assets (H2B) Table 31 below present the regression results for the three model specifications analyzing hypothesis H2B (i.e. main measure, full model, and full model incl. time dummies).

Table 31: Results of Regression Analyzing Determinants of the Return on Assets (H2B) Full Model Main Measure Full Model incl. Time Dummies 휷풌 (흈) 휷풌 (흈) 휷풌 (흈) Global mean 0.084505*** (0.000687) 0.125188*** (0.008467) 0.129353*** (0.008371) KIR 0.145690*** (0.046448) 0.159692*** (0.039771) FA_BST –0.111958*** (0.010979) –0.115475*** (0.011045) TL_BST –0.020413*** (0.008387) –0.015744*** (0.008277) CFO_R 0.244047*** (0.017015) 0.238117*** (0.016674) CFI_BST –0.070846*** (0.010553) –0.071580*** (0.010268) Year(2001) –0.007975*** (0.001637) Year(2002) –0.015524*** (0.001806) Year(2003) –0.012792*** (0.001714) Year(2005) 0.003980*** (0.001466) Year(2006) 0.007542*** (0.001397) Year(2007) 0.010814*** (0.001471) Year(2008) 0.009951*** (0.001685) Year(2009) –0.016654*** (0.001788) Year(2010) –0.004314*** (0.001547) Year(2011) 0.004229*** (0.001309) Year(2013) –0.004597*** (0.001352) Year(2014) –0.003259*** (0.001374) Year(2015) –0.003680*** (0.001521) Year(2016) –0.004443*** (0.001356) Year(2017) –0.003295*** (0.001270) No. observations 15,395 14,516 14,516 No. units 793 791 791 퐿푆퐷푉 푅² 0.558595 0.640896 0.653129

푅²푤푖푡ℎ푖푛 0.002597 0.147863 0.176892 Notes: *, **, *** indicates significance at the 90%, 95%, and 99% level, respectively. Source: Own calculations.

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The main determinant’s expected effect was confirmed in the first and second model specification. Accordingly, each percentage point decrease of the KIR leads, ceteris paribus, to a 0.15 or 0.16 percentage point decrease of the EBIT_BST ratio. The initial assumption followed from the market participant’s capacity to invest in more assets with lower profitability in times of low or zero key interest rates, which should then cause overall decreasing profitability (cf. e.g. Fisher, 1930, 104–112). The rejection of hypothesis H2A, however, calls for adjusting this assumption, since the overall investment activity actually decreases in times of decreasing key interest rates (cf. regression results in Chapter 7.5.1). The adjusted explanation could be that lower market interest rates reduce pressure on the management to earn adequate returns on assets. This, in turn, could result in an increasing number of project realizations providing a comparably low profitability, and subsequently leading to a decreased EBIT_BST ratio. This assumption is not necessarily tied to the company’s overall investment activity. The main measure’s overall explanatory power is considered limited, since the reduced model exhibits only a rather small effect size (Cohen, 1988, 413), and the determinant is omitted in the third model during the step-wise optimization process (푝 = 0.874546).

The FA_BST ratio’s expected effect was confirmed in none of the two model specifications. Instead, the EBIT_BST ratio decreases by 0.11 or 0.12 percentage points with each percentage point increase of the FA_BST ratio. A possible explanation could be that companies are generating higher profits predominantly from their current rather than non-current assets. Additionally, high capital intensity could also be accompanied by high fixed costs, which, based on asset utilization, could result in lower profitability.

The TL_BST ratio’s effect was considered indeterminate. The FEM reveals that the TL_BST ratio negatively influences the EBIT_BST ratio (for comparable results cf. e.g. Pattitoni et al., 2014, 768). Each percentage point increase in the TL_BST ratio leads to a 0.16 or 0.20 percent point decrease in the EBIT_BST ratio. As previously discussed, the inverse relationship could follow from a lower return threshold. Debt capital is typically cheaper than equity capital, and management thus needs to generate lower returns to satisfy all capital providers. However, the determinant is only significant at a 5% level in the full model and only at a 10% level in the extended model, indicating limited explanatory power.

The assumption that companies with a higher CFO_R ratio also possess higher EBIT_BST ratios has been confirmed. Specifically, each percent point increase in the CFO_R ratio leads, ceteris paribus, to a 0.24 percent point increase in the EBIT_BST ratio. As expected, a

118 company’s cash-generating power in relation to its revenues represents an effective indicator of its profitability.

The expected effect for the CFI_BST ratio is based on the assumption that a higher investment ratio should produce higher profitability. Since the CFI_BST ratio coding was derived from the cash flow statement, higher negative values represent higher cash outflows for investments. Accordingly, the coefficient’s negative sign confirms the assumed effect. Each percentage point increase in the CFI_BST ratio leads to a corresponding 0.07 percentage point increase in the EBIT_BST ratio.

The remaining time dummies do not illustrate a clear pattern, thus leaving room for interpretations. In total, 10 years significantly negatively differ, and five years significantly positively differ, from the six excluded time dummies (i.e. 1999, 2000, 2004, 2012, 2018, and 2019). One possible explanation could be that the negative deviations are crisis-implied (i.e. dot-com crisis 2001–2003, financial crisis 2009–2010, and European debt crisis 2013–2017), while the positive deviations represent the following boom years according to the methodology of the Kondratieff waves (Kondratieff & Stolper, 1935). The excluded time dummies could be related with each other, representing transition periods between the boom and the crisis scenarios. These interpretations, however, require further empirical analysis.

While the reduced model provides a 푅²푤푖푡ℎ푖푛 value that implies a small effect size, both other models are able to present 푅²푤푖푡ℎ푖푛 values corresponding with medium effect sizes (Cohen, 1988, 413). The 퐿푆퐷푉 푅² values demonstrate that the variables can explain 55–65% of the between-variation, still a low proportion considering the model set-up (for a detailed discussion of both coefficients of determination cf. Chapter 7.5.1). The power calculation of the reduced model (푓² = 0.0026038; 훼푒 = 0.05; total sample size = 793) presents an expectedly high 훽푒 error probability of 0.6997829. The power calculations for the full model (푓² = 0.1735202; 훼푒

= 0.05; total sample size = 791) and the extended model (푓² = 0.2149074; 훼푒 = 0.05; total sample size = 791), on the other hand, return a value of 1.00, thus ruling out any 훽푒 error probability (each calculation was conducted in G*Power 3.1). Combining these results with the graphical analysis of the actual and fitted values (cf. Appendix U) leads to the conclusion that the third model especially can explain a medium proportion of the variation. However, further research is necessary to further optimize the 푅²푤푖푡ℎ푖푛 value.

Summarizing the results, hypothesis H2B has been confirmed. Although the KIR represents only a weak explanatory variable, the expected impact on the EBIT_BST ratio has been confirmed

119 in the reduced and full models. Following empirical research should tackle the annual variance to explain the varying effect of the included time dummies.

7.5.3 Impact of Key Interest Rates on Cash Holdings (H2C) Table 32 below present the regression results for the three model specifications analyzing the determinants of the CASH_BST ratio (i.e. main measure, full model, and full model incl. time dummies).

Table 32: Results of Regression Analyzing Determinants of Cash Holdings (H2C) Full Model Main Measure Full Model incl. Time Dummies 휷풌 (흈) 휷풌 (흈) 휷풌 (흈) Global mean 0.106392*** (0.000954) 0.307040*** (0.013881) 0.309075*** (0.014094) KIR –0.353754*** (0.064077) –0.521285*** (0.052542) –0.355547*** (0.054014) EBIT_BST –0.026089*** (0.023076) –0.035106*** (0.023674) FA_BST –0.316253*** (0.021734) –0.324442*** (0.022253) TL_BST –0.054008*** (0.009943) –0.047957*** (0.009850) CFO_R 0.163138*** (0.016603) 0.154882*** (0.016885) CFI_BST 0.137749*** (0.012295) 0.131227*** (0.012213) Year(1999) –0.011249*** (0.003409) Year(2000) –0.011507*** (0.002736) Year(2001) –0.010224*** (0.002493) Year(2002) –0.013263*** (0.002345) Year(2003) –0.009997*** (0.002329) Year(2004) –0.006973*** (0.002343) Year(2005) –0.002674*** (0.001933) Year(2008) –0.003923*** (0.001546) Year(2009) 0.005968*** (0.001824) Year(2010) 0.005229*** (0.001776) Year(2011) 0.001907*** (0.001444) Year(2017) 0.004769*** (0.001490) Year(2019) 0.006953*** (0.002551) No. observations 15,742 14,516 14,516 No. units 794 791 791 퐿푆퐷푉 푅² 0.648442 0.770486 0.773182

푅²푤푖푡ℎ푖푛 0.007488 0.256855 0.265585 Notes: *, **, *** indicates significance at the 90%, 95%, and 99% level, respectively. Source: Own calculations. The main measure’s expected effect has been confirmed in all three model specifications. Companies tend to hoard significantly more cash and equivalents in times of low or zero key interest rate environments compared to times featuring a higher key interest rate environment. Each percentage point decrease in the KIR leads, ceteris paribus, to a 0.35, 0.36, or 0.52 percentage point increase in the CASH_BST ratio. First of all, these results correspond with the findings in Chapter 7.5.1. Companies do not increase their investment activity in periods of low key interest rates; rather, they tend to hoard the excess cash to exploit future market 120 opportunities. Consequently, Keynes’ (2018, 172–173, 177–180) theory regarding the speculative reserve and market participants’ behavior was confirmed by these two hypothesis tests. Moreover, the findings contribute to the Chapter 4.2.1’s discussion concerning the pecking order theory’s empirical dominance. Companies do not necessarily utilize their increasing cash to reduce their dependence on external capital sources (for the theoretically assumed pecking order cf. Myers, 1984, 581; for an annual development of the CASH_BST, the TFL_BST, and the TL_BST ratio cf. Appendix Q). In times of decreasing key interest rates, the hoarding behavior thus appears incompatible with the pecking order theory. Instead, the smaller coefficient of the TL_BST ratio indicates the relevance of the trade-off or signaling theories.

The EBIT_BST ratio does not significantly influence the CASH_BST ratio. Moreover, the coefficient’s sign differs from the expected relationship. This empirical finding could be interpreted as meaning that companies manage their cash and equivalents position predominantly without considering the actual return on assets. The other significant determinants in the respective model could be utilized as possible control parameters instead.

The FA_BST ratio’s expected effect was confirmed in both model specifications. With each percentage point increase in the FA_BST ratio, the CASH_BST ratio decreases by 0.32 percentage points. Since the cash and equivalents represent part of a company’s current assets, the increasing FA_BST ratio should automatically limit the level of the highest possible CASH_BST ratio. The empirical results thus confirm the expected impact of this control variable.

The assumption that companies with higher indebtedness tend to possess lower CASH_BST ratios was confirmed in both model specifications (for comparable results cf. e.g. Pinkowitz et al., 2015, 322). Even though lower key interest rates lead to increasing cash holdings, it remains possible to differentiate between companies with high and low TL_BST ratios. Accordingly, each percentage point increase in the TL_BST ratio leads to a 0.05 percentage point decrease in the CASH_BST ratio. Compared with the KIR coefficient, however, the TL_BST ratio alteration has to be stronger to affect the CASH_BST ratio to the same extent.

The CFO_R ratio significantly positively influences the CASH_BST ratio. This effect was expected, since a higher cash conversion in the operative business should primarily manifest itself in the company’s cash position. The coefficients illustrate that each percentage point

121 increase in the CFO_R ratio leads, ceteris paribus, to a corresponding 0.15 or 0.16 percentage point increase in the CASH_BST ratio.

Taking the coding of the CFI_BST ratio into account, the effect with regards to the CASH_BST ratio is significantly negative. Each percent point increase in the CFI_BST ratio leads to a 0.13 or 0.14 percent point decrease in the CASH_BST ratio. This finding corresponds with the initial assumption that companies would utilize parts of their free liquidity for increasing investment activity. This would generally support the pecking order theory. However, the company’s financial and total liabilities remain stable at the same time (cf. Appendix Q), impeding a consistent interpretation.

The time dummies in the third model specification do not illustrate a clear pattern, but still allow an interpretation. Seven years significantly negatively differ, and four years significantly positively differ, from the basis and the insignificant years (i.e. 2005, 2006, 2007, 2011, 2012, 2013, 2014, 2015, 2016, and 2018). The negative deviations appear predominantly in the periods before 2009, implying that companies began to increase their cash reserves shortly after the financial crisis. This implies a lessons-learned effect from a possibly overly low precautionary reserve during the financial crisis. Based on the regression model set-up, the effect manifests itself on both a local and global level.

The first regression model provides a 푅²푤푖푡ℎ푖푛 value, implying a small effect size according to

Cohen (1988, 413). The full and the extended model, on the other hand, present a 푅²푤푖푡ℎ푖푛 > 0.26, and thus a large effect size. The 퐿푆퐷푉 푅² values indicate that the determinants can explain 64–77% of the between-variation, considered a medium proportion in the given model set-up (for a detailed discussion of both coefficients of determination cf. Chapter 7.5.1). The post-hoc power calculation was conducted in the G*Power 3.1 tool. The calculation for the main measure

(i.e.푓² = 0.0075445; 훼푒 = 0.05; total sample size = 794) returns a power value of 0.69, implying that utilizing KIR as a sole determinant bears an approximately 31% risk of conducting a 훽푒 error. The calculations for the full (i.e. 푓² = 0.3456324; 훼푒 = 0.05; total sample size = 791) and extended models (푓² = 0.3616280; 훼푒 = 0.05; total sample size = 791) return a power value of

1.00 and rule out any 훽푒 error probability. Combining these results with the graphical analysis of the actual and fitted values (cf. Appendix U) leads to the conclusion that the third model especially can explain a medium to large proportion of the variation.

Summarizing the results, the KIR represents a significant but weak explanatory variable. This finding corresponds with previous subchapter results. Nevertheless, hypothesis H2C was

122 rejected in none of the three model specifications, confirming the KIR’s expected inverse impact on the CASH_BST ratio at a 1% level. In total, the regression analysis provided valuable insights into how the main measure and the control variables influence the CASH_BST ratio, which can be utilized for further research and model optimizations.

7.5.4 Impact of Key Interest Rates on the Financial Indebtedness (H2D) Table 33 below comprises the regression results for the three model specifications analyzing the determinants of the TFL_BST ratio (i.e. main measure, full model, and full model incl. time dummies).

Table 33: Results of Regression Analyzing Determinants of Financial Indebtedness (H2D) Full Model Main Measure Full Model incl. Time Dummies 휷풌 (흈) 휷풌 (흈) 휷풌 (흈) Global mean 0.250817*** (0.001512) 0.167311*** (0.018466) 0.159842*** (0.019324) KIR –0.182158*** (0.101540) 0.023631*** (0.102477) –0.399389*** (0.126353) EBIT_BST −0.277140*** (0.042030) –0.238124*** (0.042179) FA_BST 0.201881*** (0.030873) 0.215830*** (0.031656) CFO_R −0.064560*** (0.025587) –0.054026*** (0.025698) Year(1999) 0.026046*** (0.007067) Year(2000) 0.044613*** (0.006668) Year(2001) 0.034763*** (0.005645) Year(2002) 0.023131*** (0.004814) Year(2003) 0.011096*** (0.004557) Year(2005) –0.014109*** (0.003637) Year(2006) –0.017041*** (0.003817) Year(2007) –0.011092*** (0.003863) Year(2010) –0.011098*** (0.002886) Year(2011) –0.009945*** (0.002826) Year(2012) –0.010219*** (0.002749) Year(2013) –0.008642*** (0.002658) Year(2014) –0.009280*** (0.002419) Year(2015) –0.005030*** (0.002383) No. observations 15,710 15,036 15,036 No. units 794 791 791 퐿푆퐷푉 푅² 0.700046 0.723412 0.731370

푅²푤푖푡ℎ푖푛 0.000922 0.063693 0.090630 Notes: *, **, *** indicates significance at the 90%, 95%, and 99% level, respectively. Source: Own calculations. Examining the main measure first, the results reveal an inconsistent picture. The KIR exerts a significantly negative impact on the TFL_BST ratio at a 10% level in the first model, an insignificant positive impact in the second, and a significantly negative impact at a 1% level in the third model. Taking into account the notably low 푅²푤푖푡ℎ푖푛 value in the first model, the KIR exerts a weak to no influence on the ratio of a company’s financial indebtedness. Although this result cannot provide reliable insight into the discussed dissent between the trade-off, pecking 123 order, and signaling theories (cf. Chapter 4.2.1), the negative sign of the two significant coefficients at least offers an indication in favor of the trade-off and signaling theories. These assume that decreasing market interest rates should lead to an increasing debt position in a company. However, neither these results nor the results in Chapter 7.5.3 can fully resolve the given dissent. Regardless of this discussion, at least companies seem to avoid decreasing their TFL_BST ratio in periods of low key interest rates. This subsequently stable or slightly increasing financing demand benefits the growth opportunities of Germany’s direct lending industry.

The effect of the EBIT_BST ratio has been considered indeterminate. The negative sign of the significant coefficient thus favors the pecking order theory. With each percent point increase of the EBIT_BST ratio, the TFL_BST ratio, ceteris paribus, decreases by 0.24 or 0.28 percent points. A possible explanation for the lower financial indebtedness of more profitable companies could be that these companies can utilize a larger proportion of internal funds for their business operations or investment activities, and accordingly do not need to fund these by external debt.

The FA_BST ratio’s expected effect has been confirmed in both model specifications. Each percentage point increase in the FA_BST ratio leads, ceteris paribus, to a 0.20 or 0.22 percentage points increase in the TFL_BST ratio. This expectation was based on the assumption that companies with a large proportion of fixed assets tend to utilize more debt capital for financing. A possible explanation for this relationship could be that the management attempts to match maturities between financing and asset lifetime. Accordingly, the proportion of fixed assets that are not covered by equity capital would be financed via long-term debt.

The CFO_R ratio’s expected effect was confirmed in both model specifications at the 5% level. Accordingly, each percent point increase in the CFO_R ratio leads to a 0.05 or 0.06 percent point decrease in the TFL_BST ratio. The proposed explanation for this relationship was that companies possessing a higher cash conversion in relation to their revenues should also feature a greater capacity to pay back the financial indebtedness. However, the significance level indicates that this variable represents a comparably weak determinant of the TFL_BST ratio.

The remaining time dummies do not exhibit a clear pattern in the third model specification. Nine years significantly negatively differ, and five years significantly positively differ, from the basis years (i.e. 2004, 2008, 2009, 2016, 2017, 2018, and 2019). The only recognizable pattern is that all significantly positive deviations (i.e. higher TFL_BST ratios) occur in periods

124 before 2004, while all negative deviations (i.e. lower TFL_BST ratios) occur thereafter. Since the model includes KIR as determinant, this variation must possess a different explanation beyond market interest rate. Subsequent empirical studies could tackle this unresolved matter.

All three regression models provide 푅²푤푖푡ℎ푖푛 values, implying a small effect size according to Cohen (1988, 413). The 퐿푆퐷푉 푅² values, on the other hand, reveal that the determinants can explain more than 70% of the between-variation, considered a medium proportion reflecting the inclusion of the unit-specific dummy variables (for a detailed discussion of both coefficients of determination cf. Chapter 7.5.1). The post-hoc power calculation was conducted in the

G*Power 3.1 tool. The calculation for the main measure (i.e.푓² = 0.0009229; 훼푒 = 0.05; total sample size = 794) returned only a power value of 0.14, implying a 훽푒 error probability of approximately 86%. Utilizing KIR as sole determinant is thus not recommendable. The calculations for the full (i.e. 푓² = 0.0680258; 훼푒 = 0.05; total sample size = 791) and extended models (푓² = 0.0996624; 훼푒 = 0.05; total sample size = 791) present power values > 0.999 and almost rule out any 훽푒 error probability. The graphical analysis (cf. Appendix U) indicates a low congruity between actual and fitted values of the dependent variable, leading to the conclusion that the model can only explain a rather low proportion of the overall variation.

Reviewing the regression analysis results, hypothesis H2D must be rejected. The three model specifications present inconsistent coefficients for determinant KIR, impeding a clear result derivation. The rejection of the hypothesis, however, does not favor a positive relationship between key interest rates and financial indebtedness of a company, but rather favors no relationship at all. This is especially indicated by the reduced model specification with its

푅²푤푖푡ℎ푖푛 value. Even the extended model’s explanatory power must be critically questioned at this point. The results indicate that a company’s TFL_BST ratio is predominantly determined by other factors beyond the given explanatory variables. This presents an opportunity for further research.

An important closing point is that the KIR’s overall impact on the different dependent variables was quite limited. Nevertheless, two of four corporation-related hypotheses were not rejected in the analysis, thus confirming the previously discussed assumptions. However, it must also be noted that the two regression calculations leading to rejecting hypotheses H2A and H2D provided valuable empirical insights. These insights can especially be utilized in the cause analysis of Germany’s ascending direct lending industry, as follows in Chapter 9. However, they can also be seen as a starting point for executing future scientific research projects.

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8. Empirical Study: Zero Yield Bias The concluding empirical study focuses the impact of key interest rate alterations on investment behavior. The first subchapter describes the experimental design applied in the survey to test both sub-hypotheses. The second subchapter presents sociodemographic statistics regarding the survey participants, their assignment to the respective social strata, and the applied procedure to prepare the data for further analysis. The third subchapter presents the descriptive statistics of the collected data and continues into the methodological data analysis approach. Finally, the fifth subchapter discusses the results, the limitations of the applied approach, and the outlook for further research on this topic.

8.1 Experimental Design The experimental design utilized in this survey was developed and presented by Schaab et al. (2019). Compared to Lian et al. (2019) and Ganzach and Wohl (2018), the authors proposed a design that not only differentiates between one risky and one risk-free investment opportunity, but also presents a greater degree of detail by taking Germany’s primary asset classes into account (cf. Figure 13).

The basic idea of the design is that the survey participant first allocates proportions of his fictitious wealth to different asset classes in a normal interest rate environment, and afterwards completes the same allocation task in a zero interest rate environment. The respective asset classes are presented without labels in the survey so as to mitigate data biases from the participant’s prejudices or individual experiences (Hensher, Rose, & Greene, 2005, 371; Schaab et al., 2019, 482). Instead, the participant sees different characteristics of the respective asset class, including the average yield per year (i.e. return criterion), details on the asset’s average duration and premature availability (i.e. liquidity criteria), and information on the redemption value at maturity as well as the default probability in the coming 12 months (i.e. risk criteria). The two experimental set-ups only diverge from each other in their respective asset class yield, necessary for simulating normal and zero interest rate environments. All other characteristics are kept stable to avoid an additional influence on the allocation decision. Schaab et al. (2019, 483) proposed a total of six underlying asset classes based on Germany’s wealth allocation. These asset classes included real estate investments, overnight deposits and saving accounts, company shares, and bonds. The latter were subdivided into three bond categories: governmental, investment grade, and non-investment grade (i.e. high-yield). Alternative investments were not considered based on their low proportion in the wealth allocation.

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Based on the general concept, the authors next derived proxy values for each characteristic in each asset class. Table 34 below presents the utilized return values per year, including the underlying assumptions. Regarding the interest-bearing investment opportunities, the period of the normal interest rate environment was set between 1999, the implementation year of the common currency, and 2006, the year before the beginning of the financial crisis. The period of the zero interest rate environment, on the other hand, began in 2016 in accordance with the ECB’s policy implementation. The average returns for the non-interest-bearing investment opportunities were calculated based on the period between 1999 and 2018.

Table 34: Utilized Average Returns per Year for the Defined Asset Classes Normal Interest Rate Zero Interest Rate Utilized Sources for Asset Class Environment Environment Value Calculations Assumption Value Assumption Value Overnight Gesamtverband der Deutschen Deposits and 1.9% 0.2% Versicherungswirtschaft e.V. Saving (2018, 117) Accounts Governmental Average returns 4.4% 0.3% Deutsche Bundesbank (2019b) Bonds Average returns between 1999 Investment- since 2016 LBBW Research (2019, 96); and 2006 4.8% 0.9% Grade Bonds Thomson Reuters, (2019b) Bank of America Merrill Non- Lynch (2019); LBBW Investment- 9.3% 5.4% Research (2019, 96); Grade Bonds Thomson Reuters (2019b) Shareholding in Average returns Average returns 8.4% 8.4% Thomson Reuters (2019a) Companies between 1999 between 1999 Real Estate and 2018 3.9% and 2018 3.9% Thomas & Piazolo (2009) Source: Own representation based on Schaab et al. (2019, 483). For overnight deposits and saving accounts, Schaab et al. (2019, 484) defined daily maturities with redemption values equaling the initial deposit. Based on the deposit protection scheme in place, the counterparty risk was set at 0.0% (European Parliament and the Council of the European Union, 2014, 160). This default probability also applied to governmental bonds, based on Germany’s AAA rating. The duration was set at 10 years with premature availability, which, however, comes with market-based price risks. Examining the investment-grade and non-investment-grade bonds, Schaab et al. (2019, 484) defined an average maturity of five years and the same premature availability mechanism. The probability of default for investment- grade bonds was set at 0.1%, on average corresponding with rating categories from AAA to BBB–. For non-investment-grade bonds, the default probability was set at 2.5%, resembling an average default rate in rating categories from BB to B– (S&P Ratings Global, 2018, 60). The redemption value at maturity equaled the initial investment in all three bond categories. The CDAX was utilized as a proxy index for calculating average returns and average default

127 probabilities (i.e. 0.7%) in the shareholding category. The average duration to maturity was set at 0.7 years (Bundeszentrale für politische Bildung, 2018), and the repayment mechanism was considered to be exposed to share market price developments. Finally, the average duration of a real estate investment was set at 33 years, corresponding with its depreciation period (Einkommensteuergesetz, 2019, §7). The repayment was also considered to be exposed to price developments on the market. Due to the lack of corresponding market data, a sample of seven open real estate funds was utilized for calculating average return. The default probability was set at 0.1% based on the average model implied rating of the Europe Real Estate Index. For more details concerning the derivation of the respective characteristics, cf. the original publication by Schaab et al. (2019).

The final result of the developed survey question for the normal interest rate environment is presented in Figure 16. Herein, due to technical restrictions, the total available funds are presented as a numerical value and not as a slide control, deviating slightly from the concept of Schaab et al. (2019, 485). One additional issue was encountered in the survey’s technical realization: By implementing the proposed slide control mechanism, it was technically no longer possible to control for the total allocated amount of funds. Thus, survey participants were able to allocate an investment amount between 0% and 600%, which consequentially led to a high dropout ratio after the survey. This topic is addressed in the following subchapter.

Figure 16: Survey Question for Normal Interest Rate Environment

Source: Own representation based on experimental design by Schaab et al. (2019, 485). 128

The utilized survey question relating to the zero interest rate environment is presented in Figure 17. The feedback from the 16th Conference on European Financial Systems occurring in Brno was assimilated in the presented question design. The recommendation was to highlight the alterations in relation to the first question to enable a quick comparison by the survey participant. The accentuation was implemented through a blue font in the first column of the characteristics.

Figure 17: Survey Question for Zero Interest Rate Environment

Source: Own representation based on experimental design by Schaab et al. (2019, 485) and feedback from the 16th Conference on European Financial Systems which took place 24–25 June 2019 in Brno, Czech Republic. As proposed by Schaab et al. (2019, 482), the two allocation questions were followed by a validation question focusing on the motives for the respective wealth allocation. The survey participant had to think about the primary and secondary motives and select one of the four characteristics accordingly. Figure 18 below presents the design utilized for this question. The experimental design was embedded within a comprehensive survey regarding the future of banking, conducted by the FOM University of Applied Sciences between September and November 2019. One of the preceding survey questions also focused on investment decisions (“Which of the following investment aspects is most important for you when making investment decisions?”), and thus enabled an additional cross-validation of the participant’s investment motives.

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Figure 18: Survey Question about Motives for Wealth Allocation

Source: Own representation based on experimental design by Schaab et al. (2019, 482).

Participant recruitment was primarily conducted in the virtual campus of the FOM University of Applied Sciences, which features approximately 50,000 enrolled students (FOM Hochschule, 2019, 16). The two professional social media networks LinkedIn and Xing were utilized as a secondary channel for participant recruitment. On those platforms, the study was promoted with an according informative posting.

8.2 Sample and Data Preparation The different survey promotions attracted a total of 564 clicks from the virtual campus and 164 additional clicks from the professional social media networks. However, 30.4% of those potential survey participants dropped out immediately on the introduction page. An additional 19.1% of participants dropped out during the survey. The experimental design was located on page four, which illustrates the survey’s lowest dropout ratio. Thus, the complexity of the allocation task appeared generally acceptable for the respective survey participant. Unfortunately, the sociodemographic questions were located on page seven of the survey. Accordingly, the participant had to fully conclude the survey to make the data available for further analysis. This point of the questionnaire was only reached by 50.5% of the survey participants. As the sociodemographic data is essential for assessing the sample group, the following analysis exclusively focuses on the 368 completed questionnaires. Table 35 below presents an overview of completion ratios and the respective dropout positions within the survey.

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Table 35: Overview of Survey Completion Ratios Recruitment Number of Dropout Position Completed Channel Clicks Intro Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Surveys Virtual Campus 564 180 41 17 22 4 7 9 284 LinkedIn / Xing 164 41 21 4 8 3 2 1 84 Total 728 221 62 21 30 7 9 10 368 Source: Own representation. Due to the missing feasibility of a technical plausibility check, not all of the 368 completed surveys could be utilized in the subsequent analysis. Examining the data set led to concluding that only 232 participants answered the allocation questions precisely and distributed a total amount of wealth in the acceptable deviation range between 95% and 105%. Table 36 below presents the descriptive statistics of the categorically scaled sociodemographic variables for the total and the selected set of participants.

Table 36: Descriptive Statistics of Categorically Scaled Sociodemographic Variables Categorical Description 풏 in % 풏 in % Variable 푷푨 Male 244 66.3% 169 72.8% Gender Female 117 32.3% 61 26.3% Divers 1 0.3% 0 0.0% Single 130 35.3% 73 31.5% Marital status In permanent relationship 166 45.1% 115 49.6% Married 68 18.5% 44 19.0% No degree 97 26.4% 60 25.9% Bachelor 118 32.1% 79 34.1% Master 44 12.0% 26 11.2% Academic degree Doctorate 7 1.9% 7 3.0% Diploma 19 5.2% 15 6.5% Other degree 79 21.5% 45 19.4% Full-time 301 81.8% 200 86.2% Part-time 44 12.0% 22 9.5% Employment Unemployed 2 0.5% 1 0.4% Freelancer / independent work 11 3.0% 7 3.0% Other employment status 6 1.6% 2 0.9% Service industry 294 79.9% 190 81.9% Industry Production industry 50 13.6% 33 14.2% Commerce 19 5.2% 9 3.9% Rented flat 239 64.9% 159 68.5% Rented house 7 1.9% 6 2.6% Living conditions Owned flat 36 9.8% 19 8.2% Owned house 50 13.6% 29 12.5% Other living condition 28 7.6% 17 7.3% Migration Yes 73 19.8% 38 16.4% background No 288 78.3% 193 83.2% Atheist 147 39.9% 103 44.4% Christianity 186 50.5% 114 49.1% Religion Islam 8 2.2% 5 2.2% Other religion 3 0.8% 1 0.4% Notes: 푛푃𝐴 = Number of sample subjects from subpopulation of the 232 survey participants with precise allocation answer. Survey participants who declined to answer a specific question are not disclosed separately. Instead, the number of subjects and the respective percentage can be derived as difference value from the other disclosures. Source: Own calculations.

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Two findings in particular can be pointed out based on the descriptive table. First, the category ratios deviate, at most, 6.5% from each other between the two sample groups. In 67.7% of the cases, on the other hand, the deviation is equal or even below 2.0%. Thus, reducing the sample due to the technical issue should not heavily affect sociodemographic distributions. However, the second topic to point out concerns the sample’s overall representativeness. Even though the reduction does not heavily affect distributions within the sample, the distribution compared to Germany’s general population appears inadequate. The sample exhibits overrepresentation of male participants (Statistisches Bundesamt, 2020a), of participants with an academic degree (Statistisches Bundesamt, 2020b, 22), and of atheistic participants (Forschungsgruppe Weltanschauungen in Deutschland, 2019). Moreover, examining the industry distribution, the survey was primarily answered by participants working in the service industry. An explanation for these distribution characteristics could be the selected recruitment strategy. Although the overall representativeness is diminished by the given distribution, the sample’s financial literacy favors substantiated results. The FOM University of Applied Sciences included three knowledge questions in the survey to assess the participant’s financial literacy (for research design cf. Bucher-Koenen & Lusardi, 2011, 568). Within the subsample of survey participants who conducted the wealth allocation task precisely, 90.9% answered all three knowledge questions correctly. Compared to 85.3% in the complete sample and the lower proportion in Germany’s general population (Bucher-Koenen & Lusardi, 2011, 569), the financial literacy can be considered high level. Therefore, the discussed overrepresentations may be offset by this characteristic’s positive effect. If participants with high financial literacy are exposed to a zero yield bias, the effect could also be relevant for people with lower financial literacy, especially since the latter have to base their financial investment decisions more on intuition than on actual knowledge.

Table 37 below presents the descriptive statistics of the metrically scaled sociodemographic variables for the total and selected set of participants. The low average age of participants reflects the target audience of the FOM University of Applied Sciences. The median of the number of children also indicates that most survey participants had no children yet. Despite the low average age, the average net income was above Germany’s average income (Statistisches Bundesamt, 2020d, 11). This finding also corresponds with the net household income, which, on average, outperformed statistical values (Statistisches Bundesamt, 2020c). The net wealth was affected by several outlier values, as can also be seen in the variable’s positive skew and leptokurtic distribution. Finally, the years in professional employment revealed an average

132 value of approximately nine years. Since these variables only served the secondary purpose of sample assessment, no outlier elimination was performed at this point.

Table 37: Descriptive Statistics of Metrically Scaled Sociodemographic Variables Excess Metrical Variables 푴풊풏 푴풂풙 Median 흁 흈 Skewness 풏 Kurtosis Age1 18.0 56.0 26.0 28.49 7.07 1.20 1.32 365 Age 18.0 55.0 26.5 28.65 6.97 1.05 0.74 232 Number of Children1 0.0 3.0 0.0 0.24 0.62 2.54 5.41 345 Number of Children 0.0 3.0 0.0 0.29 0.67 2.28 4.19 224 Net Income1, 2 0.3 14.0 2.2 2.44 1.32 3.55 23.00 302 Net Income2 0.7 9.5 2.3 2.53 1.23 2.31 8.68 192 Net Household Income1, 2 0.3 22.0 3.5 3.97 2.51 2.68 13.88 271 Net Household Income2 0.3 12.7 3.8 4.01 2.01 1.17 2.67 172 Net Wealth1, 2 –40.0 2,300.0 25.0 92.53 232.92 5.49 38.32 267 Net Wealth2 0.0 2,300.0 25.0 93.04 229.47 6.17 50.04 175 Years in Job1 0.0 40.0 6.0 8.80 7.43 1.62 2.68 354 Years in Job 0.0 39.0 6.0 8.91 7.40 1.49 2.16 225 Notes: 1 data set before selection of survey participants with precise allocation answer; 2 in thousands of euros. Source: Own calculations. Summarizing the sample’s descriptive statistics, most participants were male possessing an above-average net income and an academic degree. The sample’s financial literacy was above average in level. Combining these characteristics, the capacity to answer the allocation question based on a substantiated assessment was considered present. Therefore, the reduced data set was utilized to test the two investor-related hypotheses.

8.3 Descriptive Statistics The initial data analysis step covers the experiment’s descriptive statistics. Figure 19 presents the survey participants’ average allocation decision, first in a normal and then in a zero interest rate environment. The corresponding bar chart is clustered by the six asset classes introduced in the experimental design. Even without applying inferential statistics, it can easily be seen that the survey participants extensively reallocated their wealth between the two set-ups. In particular, the asset classes of overnight deposits and saving accounts (–29.1%), governmental bonds (–69.5%), and investment-grade bonds (–42.9%) were reduced in favor of stockholdings (+108.8%) and real estate investments (+96.9%). The category of non-investment-grade bonds also revealed an alteration (–3.3%), though this proved rather small compared to the other asset classes. A noteworthy allocation decision concerns the decrease in governmental bonds compared to the decrease in overnight deposits and savings accounts, which possessed approximately the same average allocation amount in a normal interest rate environment. Survey participants seemed to prefer the asset’s liquidity, and thus reallocated a smaller proportion in the zero interest rate environment, which would support Keynes’ (2018, 177–180) speculative reserve theory. 133

Figure 19: Changes in Average Investment Behavior

Source: Own calculations. Figure 20 below presents the primary and secondary motives for the allocation decision. The return of an asset was chosen by 66.4% of the survey participants as the primary allocation motive. This result generally corresponds with the reaching-for-yield debate (Bernanke, 2013, 12) since the safety (16.4%) and the liquidity answers (15.9%) followed with a large gap. The secondary motive did not present such a clear preference. Safety accounted for 41.4%, liquidity for 34.5%, and return for 23.3% of the answers.

Figure 20: Primary and Secondary Motives for Asset Allocation

Source: Own calculations.

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Since the FOM University of Applied Sciences included one question about primary investment motive in the survey prior to the experiment, it was also possible to analyze the investment motive consistency. Figure 21 below presents the results of this analysis. Accordingly, only 65.8% of survey participants answered both questions with the same preference (i.e. yield, liquidity, or safety). The other 34.2% of participants changed their primary investment motive after concluding the experiment.

Figure 21: Investment Motive Consistency

Source: Own calculations. The following subchapter presents the methodological approach for validating the descriptive and graphical analysis through inferential statistics.

8.4 Methodological Approach The collected data set presented repeated measures within one sample, leading to the decision to utilize the paired-sample 푡-test to test both hypotheses. Generally, the paired-sample 푡-test calculates difference values between the two measures of survey participants and examines whether those difference values significantly differ from zero. The approach thus resembles the calculation of a one-sample 푡-test (Bortz & Schuster, 2010, 124).

The three assumptions for applying the paired-sample 푡-test concern the dependent variable’s continuity (i.e. interval or ratio scale), its random elicitation, and normal distribution of the difference value variable (Bortz & Schuster, 2010, 125). Since random survey participants provided percentage ratios for each asset class, the first two assumptions were satisfied. The third assumption examination and subsequent statistical analysis were conducted with the

135 program IBM SPSS Statistics 26. In each case, applying the Shapiro-Wilk test revealed, with a 푝 < .01, that none of the difference value variables complied with the normal distribution assumption. However, several simulation studies indicated that the paired-sample 푡-test is robust given a sample size of at least 30 participants, even if the normal distribution assumption is violated (cf. e.g. Boneau, 1960, 63; Sawilowsky & Blair, 1992, 359). Since the sample included a total of 232 participants, no variable transformation was applied in the subsequent analysis. An additional recommendation for applying the paired-sample 푡-test includes a positive correlation between the repeated measures. A negative correlation, on the other hand, could negatively influence the test’s power (Bortz & Schuster, 2010, 125). Calculating the Bravais-Pearson correlation coefficient revealed that all measurement pairs possessed a 푟 ≥ 0.17 with a 푝 < .05, which in general should benefit the test’s power. The post-hoc power calculation itself was conducted with the G*Power 3.1 tool (cf. Faul et al., 2009). The 36 identified outlier values (i.e. 2.6% of the total data set) were considered true outliers, and thus not removed from the final data set.

Although not covered by the research hypotheses, one additional analysis was conducted following the described test procedure. The emphasis of this analysis concerned the question of whether or not sociodemographic variables (e.g. gender) significantly affected the allocation decision. The applied statistical method comprised a general linear model for repeated measures, which utilized the respective sociodemographic variable as a between-subject factor. The results of this analysis are included in Appendix V for informational purposes only.

8.5 Result Discussion and Limitations Table 38 below presents the results of the paired-sample 푡-tests, including the means and standard deviations of the difference value variables as well as the power values calculated for each paired difference.

Table 38: Results of Paired-Sample 풕-Tests Analyzing Investment Behavior (H3A, H3B)

Paired Asset Class 흁 흈 푪푰ퟗퟓ% 푻 풅풇 풑 Power 1 0.0579 0.1434 [–0.0393; –0.0764] 6.148 231 < 0.001 > 0.999 2 0.1213 0.1848 [–0.0974; –0.1452] 9.998 231 < 0.001 1.000 3 0.0722 0.1978 [–0.0467; –0.0978] 5.564 231 < 0.001 > 0.999 4 0.0069 0.2959 [–0.0313; –0.0452] 0.357 231 0.721 0.064 5 –0.1973 0.2843 [–0.2341; –0.1605] –10.570 231 < 0.001 1.000 6 –0.0620 0.1470 [–0.0810; –0.0430] –6.425 231 < 0.001 > 0.999 Notes: 1 = Overnight deposits and savings accounts; 2 = Governmental bonds; 3 = Investment-grade bonds; 4 = Non-investment-grade bonds; 5 = Shareholding in companies; 6 = Real estate. Source: Own Calculations.

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Summarizing the results, both investor-related hypotheses have been confirmed in the paired- sample 푡-tests. First, investors significantly changed their investment behavior (i.e. their asset- class allocation) in times of a zero key interest rate environment (H3A). Second, they also allocated significantly more capital to riskier investments (i.e. higher default probabilities) than before (H3B).

The foundation for the first conclusion can be found in five out of six asset classes demonstrating with their 푝 < .01 that the difference between measurements significantly differs from zero. The asset category of non-investment-grade bonds presents the only exception with no significant alteration. This might be explained by the comparably high average return per year, making the asset class more robust against withdrawals. Confirmation of the second hypothesis is based on the given experimental design. The two asset classes that attracted significantly more funds in the zero interest rate set-up featured an overall higher default probability and increased exposure to price risks (i.e. redemption value at maturity). To be precise, the survey participants increased their average weighted default probability from 0.69% in the normal to 0.81% in the zero interest rate set-up, and they increased their price risk exposure at maturity from 24.4% to 50.3% of total funds.

These findings contradict the assumption that an investor’s risk-preference is generally stable (cf. e.g. Sharpe, 1966, 122), thus strongly supporting further research on the topic (cf. e.g. Baucells & Villasís, 2010, 209; Schildberg-Hörisch, 2018, 148–150), as well as advancement of the current understanding of the portfolio theory (for a comparable proposal based on irrational investor behavior cf. Reuse, 2011, 222). Reviewing the presented Figure 5 in this context, the real-world investor strives for a new combination of risk-free investment opportunity and market portfolio along the capital allocation line in order to restore his utility level, which was diminished by altering the capital allocation line’s slope. The debate about reaching for yield (cf. e.g. Bernanke, 2013; Neubauer, 2018) thus seems to be fully justified. However, as presented in Figure 22 below, even if the investor is able to restore his expected portfolio return level, the risk level still increases, which should result in diminished utility. Accordingly, the yield perception appears to irrationally overweigh the perception of risk, corresponding with the survey results presented in Figure 20. The study results also correspond with the findings of Lian et al. (2019) and Ganzach and Wohl (2018), which suggested individual reference dependence and salience of returns as possible explanations for the tendency towards a higher risk exposure.

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Figure 22: Empirically Implied Investment Behavior along Capital Allocation Line

Source: Own representation based on Schaab et al. (2019, 481). The post-hoc test power was > 0.999 for each asset class whose mean significantly differed between the two experimental set-ups. These results almost ruled out any 훽푒 error probability. The test of the non-investment-grade bonds, on the other hand, presented only a corresponding power value of 0.064, implying a 훽푒 error probability of approximately 94%. This follows from a mean value close to zero combined with a comparably high standard deviation and limits the potential for generalizing the results. Discussing further limitations of the empirical approach, two more topics are worth pointing out: The first topic concerns the previously discussed representativeness of the sample. The additionally conducted analysis regarding sociodemographic variables and their forecasting power did not present any significant results (cf. Appendix V). Therefore, the sample’s representativeness is considered to not be heavily influenced by the overrepresented factors. The second topic to point out concerns the possibly divergent behavior of institutional investors, which were not taken into account in the empirical study. Therefore, one approach to further research on this topic would be to increase the sample’s general representativeness and include institutional investors in the survey.

Nevertheless, the confirmation of hypotheses H3A and H3B presents a solid foundation to further expand the scientific knowledge on the topic of risk stability and asset allocation strategy.

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9. Interrelation Discussion The interrelation discussion focuses on this dissertation’s final sub-objective and answers the question of whether some of the empirical findings can be considered to explain Germany’s strongly growing direct lending sector. The first subchapter presents a brief overview concerning this work’s empirical findings, followed by an assessment of these findings in relation to the German direct lending industry in the second subchapter.

9.1 Summary of Empirical Results Table 39 below summarizes the empirical results in relation to the refined research hypotheses. Only two out of those eight sub-hypotheses were rejected in empirical testing. The other six hypotheses withstood scrutiny and can offer answers to the questions initially formulated in Chapter 1.2.

Table 39: Summary of Empirical Results Number Hypothesis Result Internal costs of a bank significantly negatively influence

H1A growth (i.e. growth of return on risk-weighted assets) during Confirmed periods of a low or zero key interest rate environment. Corporate direct lending in leverage transactions achieved a

Banks H1B significant market share compared to the total corporate Confirmed lending market and the total investment market in Germany. Key interest rates significantly negatively influence the H2A investment activity (i.e. cash flow from investing activity) of a Rejected

company. Key interest rates significantly positively influence the ratio of H Confirmed 2B average returns (i.e. EBIT) to total assets. Key interest rates significantly negatively influence the cash H Confirmed 2C and cash-equivalents position of a company. Corporations Key interest rates significantly negatively influence the H2D financial liabilities (i.e. current and non-current borrowings) of Rejected a company. Investors significantly change their investment behavior (i.e.

H3A their asset class allocation) in periods of a zero key interest rate Confirmed environment. Investors allocate significantly more capital to riskier

Investors H3B investments (i.e. higher default probabilities than before) in Confirmed periods of a zero key interest rate environment. Source: Own representation. Starting with the bank-related hypotheses, the guiding research question concerned whether low intermediation costs benefit the business development of financial intermediaries in Germany. The empirical study on determinants of bank profitability growth revealed that internal costs exert a significant inverse impact on profit growth in the following period (cf. Chapter 6.1). Banks capable of managing their business operations at a lower cost ratio manage 139 to subsequently grow stronger than their competitors in terms of return on risk-weighted assets.

Accordingly, hypothesis H1A holds true and answers the initial question. Hypothesis H1B focused on Germany’s corporate direct lending industry (cf. Chapter 6.2). Using an approximation approach, it was empirically proven that this specific type of lending already represents a significant share of Germany’s total corporate credit market. Additionally, this market segment was also reviewed from an investor’s perspective. Here, the market volume in relation to Germany’s net total wealth also significantly differed from zero. Consequently, hypothesis H1B also holds true and presents the necessary foundation for discussing the causes of this significance.

The second guiding research question considered how the zero key interest rate environment influences the financing and investment decisions of stock-listed companies in Germany.

Testing hypothesis H2A revealed no inverse relationship between key interest rates and the investment activity of a company. Instead, companies tend to invest less in times of low key interest rates. This finding contradicted numerous theoretical paradigms and led to rejecting hypothesis H2A (cf. Chapter 7.5.1). Testing hypothesis H2B revealed that the average returns on total assets significantly relate to key interest rate alterations. Companies tend to possess lower average returns on assets in times of low key interest rate environments. Accordingly, hypothesis H2B holds true (cf. Chapter 7.5.2). The third hypothesis test confirmed the Keynesian assumption that market participants tend to possess higher speculative reserves in times of low key interest rates. Hypothesis H2C holds true based on this empirically confirmed hoarding behavior (cf. Chapter 7.5.3). The concluding analysis indicated an inverse relationship between key interest rates and company indebtedness. However, the impact was not unambiguous, consequently leading to the rejection of hypothesis H2D (cf. Chapter 7.5.4). Nevertheless, the overall assessment of the results leads to the conclusion that key interest rates significantly influence the financing and investment decisions of corporations, which also answers the initial question.

The third guiding research question focused on investor behavior and risk tolerance in different key interest rate environments. The empirical study about the zero yield bias could confirm both investor-related hypotheses H3A and H3B (cf. Chapter 8). Investors not only adjust their investment behavior in different key interest rate environments, but they also adjust their risk tolerance accordingly. The conducted experiment revealed that investors allocated significantly more capital to riskier investments in periods of a zero key interest environment in order to mitigate their yield reduction. This finding contradicts numerous theories assuming stable risk

140 preferences, but it also provides valuable insights for behavioral finance research and the discussion of the ascending direct lending industry.

The following subchapter reviews the empirical results in connection with the fourth sub- objective of this dissertation, which focuses on the explorative development of possible explanations for Germany’s strongly growing direct lending industry.

9.2 Implications for the Direct Lending Industry To recall the previous discussions: A direct relationship between key interest rate alterations and any specific market development was difficult to establish from not only the ECB’s perspective (cf. Chapter 2.2), but from a scientific perspective as well (cf. Chapter 3.5). The results of the four conducted empirical studies present a strong foundation tying the three market participant groups’ behavior in a yet unseen interest rate environment with the development of Germany’s direct lending industry. However, there remains a preceding disclaimer, since the analysis of the results and their interrelations still follows common sense given the scientific limitations. This represents a central characteristic of explorative research, which can resort to neither unambiguous if-then relationships nor to exhaustive data sets. However, just because the first steps are challenging does not mean that they cannot provide valuable insights for subsequent research. Thus, the concluding question to answer concerns whether this dissertation project was able to uncover possible explanations of Germany’s strongly growing direct lending industry.

Starting with the bank-related hypotheses, both the study about determinants of bank profitability growth and the study about significance of market shares offer results supporting the growth and relevance of the direct lending industry. The first study confirmed that low intermediation costs benefit profit growth in the following period. Banks are able to exercise their competitive advantage on the one hand, while customers seem to choose a counterparty that either increases their cash inflows or reduces their cash outflows on the other hand (cf. Chapter 3.2). The conclusion for the direct lending industry is that low intermediation costs following from a rather moderately regulated environment and the lean business set-up (cf. Chapter 3.3.3) should benefit growth in a general sense and profit growth in a narrow sense. The second study confirmed than that the direct lending industry already represents a significant market niche compared to the total corporate lending market and the total investment market in Germany. This result not only makes the discussion concerning the necessity of empirical research in this segment superfluous, but can also be seen as an appeal to catch up the previous research shortcomings as quickly as possible (cf. Chapter 3.4.3).

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The corporation-related hypotheses tests did not provide such unambiguous implications for Germany’s direct lending industry. The first test revealed that companies tend to decrease their investment activity in periods of decreasing key interest rates. Operationalization of investment activity included merger-and-acquisition transactions, which currently represent the primary field for financing through direct lending channels (cf. Chapter 4.2.2). Accordingly, this finding implies that the decreasing investment activity would rather obstruct growth of the direct lending market in recent years. The second hypothesis test confirmed that the average return on assets tends to decrease with decreasing key interest rates. Assuming that this effect applies to not only stock-listed, but to the total population of companies in Germany, it would in most cases be opportune for the sponsor of a merger-and-acquisition transaction to increase the target company’s leverage by applying a direct lending unitranche (cf. Chapter 4.2.2). As long as the return on assets exceeds the debt costs, the sponsor would be able to stabilize his return on equity through this measure even though the return on assets is decreasing. The third hypothesis test indicated that companies tend to increase their cash holdings when key interest rates decrease. This finding can be related to the decreasing investment activity and could also be seen as an obstacle for the direct lending industry’s growth. The fourth hypothesis test, however, presented the most important result in this context. It revealed that, despite investment activity decreasing and cash holdings increasing, companies still possess a stable demand for financing (cf. Appendix Q), which generally benefits the direct lending industry’s growth. It is important to point out that investments are only executed at a lower level in a zero key interest rate environment, but they do not cease to exist. Therefore, the implications of the second and fourth hypothesis tests should hold true regarding their beneficial influence on the direct lending industry’s growth.

Finally, both investor-related hypotheses presented strong support for the growth of the direct lending market. The first hypothesis test confirmed that investors tend to reallocate their wealth between asset classes in different interest rate environments. The second hypothesis test additionally indicated that investors tend to allocate their wealth to riskier asset classes when key interest rates are low. This empirically confirmed zero yield bias implies that investors would also allocate more capital to an asset class such as direct lending in the current zero interest rate environment. The statistically insignificant variation in the asset class of non- investment-grade bonds, which present the closest approximation to the asset class of direct lending, strongly supports this implication (cf. Chapter 8.3).

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Summarizing the results, over the last decade, the market environment has presented a rich breeding ground for the direct lending industry. Universal banks being unable to compete at eye-level with intermediation-light contestants, corporations maintaining a stable demand for financing, and investors seeking alternatives for diminishing yields in established asset classes all present generally positive conditions for growth of the direct lending market niche. The market participants in this niche were able to exploit those conditions to establish significant market shares in Germany following the US example (cf. Chapter 3.4.1). Accordingly, the answer to the initial question would be yes, the dissertation project was able to uncover several explanation approaches for Germany’s strongly growing direct lending industry, as are collectively delineated in Figure 23. As previously stated, the proposed explanations based on the explorative approach make no claim to be exhaustive. Rather, they should trigger further research and especially further development of the theoretical framework, which, aside from a few exceptions, possesses only a limited capacity to explain today’s economic environment.

Figure 23: Possible Causes of the Growing Direct Lending Industry in Germany

Source: Own representation based on data retrieved from Deloitte (2020b).

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10. Conclusion and Outlook Curiosity drives scientific exploration. The historically low key interest rate combined with the strong growth of Germany’s direct lending industry represented starting points for the exploration project described in this dissertation. Three of the four formulated objectives were achieved by means of statistical analysis of comprehensive data sets. The influence of the zero key interest rate policy on banks, companies, and investors is no longer simply evident according to general understanding, but could also be empirically proven in several sub- hypothesis tests. The final objective comprised an exploratory discussion of the interrelationships between these three interest groups in order to derive possible causes for Germany’s rapidly growing direct lending industry. This qualitative method presented a number of explanatory approaches based on the previously established statistical relationships, and thus led to achieving the final objective.

Two research perspectives require accentuation here due to their outstanding relevance for practitioners. On the one hand, the direct lending industry should be brought into the focus of empirical research, and the current knowledge gap regarding the European market should be closed as quickly as possible. A continuously increasing database on the topic would answer questions concerning not only market relevance, but also specific characteristics of this financing source in the future (e.g. number of lenders, structuring of covenants, margins). The influence of the COVID-19 crisis on the market development in this niche also presents a particularly interesting question for further research. The second spotlight is directed towards the zero yield bias. The results revealed that this topic is not only relevant in relation to the current zero interest rate environment, but also possesses far-reaching implications for the assumption of risk stability, the portfolio theory framework, and the concepts of behavioral finance. However, the results also raise the question of whether investors should be better protected by regulation in times of a zero key interest rate environment so that they only take risks according to their expertise, and not according to a biased perception.

As far as the author is concerned, this dissertation project provides one of the first contributions to reducing the described research gap. However, the first scientific steps are rarely able to fully grasp a new research topic. It would thus be desirable for curiosity to also drive future exploration of the direct lending industry in Germany.

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Appendix Appendix A: Long-Term Development of Key Interest Rates and Inflation ...... 146 Appendix B: Impacts of Key Interest Rate Alterations on Inflation Rates ...... 147 Appendix C: Calculation of Gross Total Wealth Allocation in Germany ...... 150 Appendix D: Calculation of Gross Money Wealth Allocation in Germany ...... 151 Appendix E: Constituents of the Test Group ...... 152 Appendix F: Constituents of the Control Group ...... 153