Business, Sauder School of
Accounting, Division of
DSpace
UBCV
Yang, Hsiang-Chieh
Doctor of Philosophy - PhD
University of British Columbia
This thesis examines the consequences of two post-2008 financial crisis bank reforms in two studies. The first study explores the consequences of a new accounting rule for recognizing loan losses, the current expected credit loss model (CECL). CECL requires banks to expense their expected credit losses when a loan is issued, rather than when an event occurs that could make the loan uncollectable, as required by CECL’s predecessor accounting standard. Requiring earlier recognition of loan losses is likely to require banks to prepare more regulatory capital, decreasing their willingness to lend. Empirically, I find that following the approval of CECL, more capital-constrained banks reduce their loan growth rates. The reduced loan growth is stronger for real estate loans, which typically have a longer term to maturity, and for banks operating under a more lenient regulator. Using county-level loan origination data, I also find that more capital-constrained banks originate fewer small business loans. One key takeaway of this study is that when evaluating the effects of a new accounting change, policymakers should also consider the changes in economic behaviors that occur before the effective date of the change, rather than only focusing on the time after it becomes effective. In the second study, we examine the consequences of the Volcker Rule, which prohibits banks from conducting proprietary trading. The Volcker Rule aims to reduce the likelihood of future financial crises by discouraging banks from taking on too much risk. However, prior research finds that corporate bond liquidity (i.e., the ease of trading public bonds) has decreased after the Volcker Rule. This study utilizes the Volcker Rule as a bond liquidity shock to investigate whether the loss of bond liquidity motivates bond issuing firms to increase their voluntary disclosures. Using a difference-in-difference design, we find that bond issuers are more likely to increase their management guidance after the Volcker Rule was implemented. Further, the increased management guidance is more pronounced for firms with credit ratings close to the investment-grade cutoff, rather than those with very high or very low ratings, where firms have less ability to change market perceptions by their disclosures.
POST-2008-CRISIS BANK REFORMS: STUDIES ON THE REAL EFFECTS OF LOAN LOSS ACCOUNTING AND THE ROLE OF BANKS AS INFORMATION INTERMEDIARIES by Hsiang-Chieh Yang A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Business Administration) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) November 2021 © Hsiang-Chieh Yang, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Post-2008-Crisis Bank Reforms: Studies on the Real Effects of Loan Loss Accounting and the Role of Banks as Information Intermediaries submitted by Hsiang-Chieh Yang in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Business Administration Examining Committee: Joy Begley, Associate Professor, Accounting and Information Systems, UBC Supervisor Sandra Chamberlain, Associate Professor, Accounting and Information Systems, UBC Supervisory Committee Member Ira Yeung, Assistant Professor, Accounting and Information Systems, UBC Supervisory Committee Member Giovanni Gallipoli, Professor, Economics, UBC University Examiner Hernan Ortiz-Molina, Associate Professor, Finance, UBC University Examiner Additional Supervisory Committee Members: Ralph Winter, Professor, Strategy and Business Economics, UBC Supervisory Committee Member iii Abstract This thesis examines the consequences of two post-2008 financial crisis bank reforms in two studies. The first study explores the consequences of a new accounting rule for recognizing loan losses, the current expected credit loss model (CECL). CECL requires banks to expense their expected credit losses when a loan is issued, rather than when an event occurs that could make the loan uncollectable, as required by CECL’s predecessor accounting standard. Requiring earlier recognition of loan losses is likely to require banks to prepare more regulatory capital, decreasing their willingness to lend. Empirically, I find that following the approval of CECL, more capital-constrained banks reduce their loan growth rates. The reduced loan growth is stronger for real estate loans, which typically have a longer term to maturity, and for banks operating under a more lenient regulator. Using county-level loan origination data, I also find that more capital-constrained banks originate fewer small business loans. One key takeaway of this study is that when evaluating the effects of a new accounting change, policymakers should also consider the changes in economic behaviors that occur before the effective date of the change, rather than only focusing on the time after it becomes effective. In the second study, we examine the consequences of the Volcker Rule, which prohibits banks from conducting proprietary trading. The Volcker Rule aims to reduce the likelihood of future financial crises by discouraging banks from taking on too much risk. However, prior research finds that corporate bond liquidity (i.e., the ease of trading public bonds) has decreased after the Volcker Rule. This study utilizes the Volcker Rule as a bond liquidity shock to investigate whether the loss of bond liquidity motivates bond issuing firms to increase their voluntary disclosures. Using a difference-in-difference design, we find that bond issuers are more likely to increase their management guidance after the Volcker Rule was implemented. iv Further, the increased management guidance is more pronounced for firms with credit ratings close to the investment-grade cutoff, rather than those with very high or very low ratings, where firms have less ability to change market perceptions by their disclosures. v Lay Summary This thesis examines the consequences of two post-2008 financial crisis bank reforms. The first study finds evidence consistent with a new accounting rule reform (the current expected credit loss model, known as CECL) reducing a bank’s willingness to lend. After CECL, banks are required to recognize the expected loan losses at the time of loan issuance rather than when borrowers are unable to repay the loan. This requirement forces banks to prepare for higher regulatory capital for each loan they issue, increasing their lending cost. The second study finds that after banks are prohibited from trading corporate bonds due to the Volcker Rule, bond issuers increase their voluntary disclosures. This finding is consistent with the notion that banks’ bond trading activities can provide information to bond traders. Once banks are no longer allowed to trade bonds, bond issuers are encouraged to increase their disclosures to compensate for this information vacuum. vi Preface Chapter 2 is an original and independent work by the author, Hsiang-Chieh Yang. Chapter 3 is co-authored with Professor Joy Begley and Professor Ira Yeung from the University of British Columbia. I initiated this project as my third-year paper. Since coauthors joined this project, I have been responsible mainly for data analysis, and I have contributed to research design decisions and editing. My coauthors are mainly responsible for the formal writing of the manuscript. All authors contributed equally to the conceptual foundation, idea development, and empirical analysis. vii Table of Contents Abstract ................................................................................................................................... iii Lay Summary ............................................................................................................................ v Preface ...................................................................................................................................... vi Table of Contents ....................................................................................................................vii List of Tables ...........................................................................................................................xii List of Figures......................................................................................................................... xiv Acknowledgements .................................................................................................................. xv Dedication ............................................................................................................................... xvi Chapter 1: Introduction ............................................................................................................ 1 Chapter 2: How does accounting for loan losses influence bank lending? Evidence from the Current Expected Credit Loss Model (CECL) ........................................................................ 4 2.1 Introduction .................................................................................................................4 2.2 Background and related literature............................................................................... 11 2.2.1 Banks’ role in the economy .................................................................................... 11 2.2.2 Regulators’ role in bank supervision ...................................................................... 12 2.2.2.1 Types of banking entities in the U.S. and their regulators ............................... 12 2.2.2.2 On-site and off-site examinations ................................................................... 14 2.2.2.3 Inconsistency between banking regulators ...................................................... 15 2.2.2.4 The regulatory reporting system of banks ....................................................... 16 2.2.3 Bank capital regulation history ............................................................................... 17 2.2.3.1 Pre-Basel period (before 1992) ....................................................................... 17 viii 2.2.3.2 First Basel period – Basel I (1992 – 2008) ...................................................... 18 2.2.3.3 Second Basel period – Basel II (2008 – 2013) ................................................ 19 2.2.3.4 Third Basel period – Basel III (2013 – the current period) .............................. 20 2.2.3.5 Consequences of violating capital requirements – FDICIA prompt corrective action requirement ......................................................................................................... 21 2.2.4 Loan loss accounting.............................................................................................. 22 2.2.4.1 The incurred loss model (ILM) ....................................................................... 23 2.2.4.2 The current expected credit loss model (CECL) ............................................. 25 2.2.4.3 Criticism of CECL ......................................................................................... 28 2.2.4.4 Loan loss accounting, regulatory capital, and bank lending............................. 29 2.2.4.5 Prior research on how the accounting for loan losses influences bank lending through capital ratio requirements .................................................................................. 31 2.3 Hypothesis development ............................................................................................ 32 2.3.1 CECL and bank lending (H1) ................................................................................. 33 2.3.2 CECL and long-term loans (H2) ............................................................................ 36 2.3.3 CECL and loan types (H3) ..................................................................................... 37 2.3.4 Cross-sectional Analysis (H4 and H5) .................................................................... 40 2.3.4.1 Regulatory leniency (H4) ............................................................................... 40 2.3.4.2 Public banks versus private banks (H5) .......................................................... 42 2.4 Research design ......................................................................................................... 44 2.4.1 Timeline of the difference-in-differences design .................................................... 44 2.4.2 CECL’s impact on bank lending (H1, H2, and H3) ................................................ 47 2.4.2.1 Main independent variable ............................................................................. 48 ix 2.4.2.2 Control variables and fixed effects ................................................................. 51 2.4.3 Cross-sectional analysis (H4 and H5) ..................................................................... 52 2.4.3.1 Regulatory leniency (H4) ............................................................................... 52 2.4.3.2 Public banks versus private banks (H5) .......................................................... 53 2.5 Sample selection process and univariate analysis ....................................................... 54 2.5.1 Sample selection process........................................................................................ 54 2.5.2 Descriptive statistics .............................................................................................. 57 2.5.3 Correlations ........................................................................................................... 59 2.5.4 Difference-in-differences univariate analysis ......................................................... 60 2.6 Main results ............................................................................................................... 62 2.6.1 CECL and bank lending (H1) ................................................................................. 62 2.6.2 CECL and long-term loans (H2) ............................................................................ 63 2.6.3 CECL and loan types (H3) ..................................................................................... 64 2.6.4 Cross-sectional analyses (H4 and H5) .................................................................... 66 2.6.4.1 Regulatory leniency (H4) ............................................................................... 66 2.6.4.2 Public banks versus private banks (H5) .......................................................... 68 2.7 Additional analysis .................................................................................................... 69 2.7.1 CECL and small business lending .......................................................................... 69 2.7.1.1 Motivation ..................................................................................................... 69 2.7.1.2 Data and research design ................................................................................ 71 2.7.1.3 Results ........................................................................................................... 72 2.7.2 Placebo tests .......................................................................................................... 73 2.7.3 Are banks that increase their equity less affected by CECL? .................................. 74 x 2.8 Conclusion................................................................................................................. 77 Chapter 3: Bond Liquidity and Voluntary Disclosures: Evidence from the Volcker Rule 129 3.1 Introduction ............................................................................................................. 129 3.2 Background and hypothesis development................................................................. 134 3.2.1 Reasons why firms issue bonds ............................................................................ 134 3.2.2 The role of dealers in corporate bond markets ...................................................... 137 3.2.3 How the Volcker Rule affects corporate bond markets ......................................... 138 3.2.4 How firms that issue bonds respond to the Volcker Rule ...................................... 139 3.2.5 Do firms react to the Volcker Rule differently? .................................................... 141 3.3 Research design ....................................................................................................... 144 3.3.1 Identifying treatment firms and post-treatment period .......................................... 144 3.3.2 Regression models ............................................................................................... 145 3.3.2.1 The regression model for H1 ........................................................................ 145 3.3.2.2 The regression model for H2 ........................................................................ 147 3.4 Sample, Descriptive Statistics and Main Result ........................................................ 148 3.4.1 Data Source and Sample ...................................................................................... 148 3.4.2 Summary Statistics .............................................................................................. 149 3.4.3 Results on Testing H1 .......................................................................................... 150 3.4.4 Results on Testing H2 .......................................................................................... 152 3.5 Additional Tests: Instrumental variables for whether a firm issues public debt ......... 154 3.6 Conclusion............................................................................................................... 156 Chapter 4: Conclusion .......................................................................................................... 166 Bibliography .......................................................................................................................... 169 xi Appendices ............................................................................................................................ 177 Appendix A Appendix for Chapter 2 ................................................................................... 177 A.1 History of changes in CECL effective dates ......................................................... 177 A.2 Variable Definitions ............................................................................................. 182 Appendix B Appendix for Chapter 3 ................................................................................... 189 B.1 Variable definitions .............................................................................................. 189 B.2 Disclosure frequency of different quarterly management forecast items for treatment and control firms in pre-Volcker and post-Volcker eras ................................................... 191 B.3 Summary Statistics of disclosure variables for firms in each rating category ........ 193 xii List of Tables Table 2.1 Composition of the FR Y-9C sample ......................................................................... 81 Table 2.2 Descriptive Statistics and Correlations - Y9C Sample (bank-quarter) ......................... 84 Table 2.3 Difference-in-difference descriptive statistics ............................................................ 92 Table 2.4 CECL and bank lending (H1) .................................................................................... 96 Table 2.5 CECL and long-term loans (H2) ................................................................................ 98 Table 2.6 CECL and loan growth of different loan types (H3) ................................................. 100 Table 2.7 CECL's impact on lending and regulatory leniency - using subsample tests (H4) ..... 102 Table 2.8 CECL's impact on lending and regulatory leniency - using continuous measure (H4) ............................................................................................................................................... 105 Table 2.9 CECL's impact on lending and bank share listing status (H5) ................................... 107 Table 2.10 Composition of the Small Business Loan Sample .................................................. 110 Table 2.11 Descriptive Statistics - Small business lending (SBL) sample ................................ 112 Table 2.12 CECL and small business lending .......................................................................... 113 Table 2.13 Cross-sectional analysis of CECL's impact on small business lending .................... 115 Table 2.14 CECL's impact on small business lending and regulatory leniency - using a continuous measure ................................................................................................................. 118 Table 2.15 Summary of placebo test results ............................................................................. 120 Table 2.16 Does increasing equity mitigate the impact of CECL on lending? .......................... 121 Table 2.17 Does increasing equity mitigate the impact of CECL on long-term lending? .......... 124 Table 2.18 Does increasing equity mitigate the impact of CECL on different types of lending? ............................................................................................................................................... 127 Table 3.1 Summary Statistics .................................................................................................. 158 xiii Table 3.2 The effect of Volcker Rule on management guidance .............................................. 161 Table 3.3 Examining the Volcker Rule's impact on disclosures of firms with different credit ratings ..................................................................................................................................... 163 Table 3.4 The effect of Volcker Rule on management guidance: Instrumental Variable Approach ............................................................................................................................................... 165 xiv List of Figures Figure 2.1 The structure of the U.S. bank charter system ........................................................... 13 Figure 2.2 Timeline of the Basel III implementation .................................................................. 20 Figure 2.3 A summary of FDICIA bank regulatory capital requirements ................................... 22 Figure 2.4 The journal entry when CECL is first adopted (day-1) .............................................. 26 Figure 2.5 Timeline of my research design ................................................................................ 46 xv Acknowledgements My survival in the Ph.D. program would not have been possible without the support from many individuals. In particular, I would like to express my special gratitude to my advisor Joy Begley. She helped me to become a better researcher by pushing my boundaries to think deeper. She also offered me tremendous support whenever I faced difficulties. I am also grateful to my committee members, Sandra Chamberlain, Ralph Winter, and Ira Yeung, for their insightful feedback and encouragement. I would also like to thank other accounting division faculty members as well as my fellow Ph.D. students for useful feedback. I am also grateful for the support of our divisional assistant, Debra Harris, and Ph.D. program administrator, Elaine Cho. My Ph.D. life would not have been as smooth without them. Last but not least, this thesis is dedicated to my wife Linda Luo, who is my great companion and supports me through all of the most challenging moments in my Ph.D. life. xvi Dedication To my parents. 1 Chapter 1: Introduction The 2008 financial crisis stimulated many reforms in the U.S. banking sector. This thesis consists of two studies that examine the economic consequences of two of the post-crisis reforms. The first study focuses on the economic consequences of an accounting rule reform, the current expected credit loss model (ASU 2016-13, also known as CECL). CECL was approved on November 18, 2015, and it requires banks to disclose more information on the credit risk of their loan portfolios. Specifically, CECL requires banks to expense a loan's lifetime expected credit losses whenever a new loan is issued, reducing the size of their equity capital. In contrast, CECL’s predecessor accounting rule (the incurred loss model) does not require banks to recognize the expected credit losses of a loan until an event occurs that could make the loan uncollectable. If banks effectively and efficiently apply CECL, CECL is expected to benefit the financial sector by promoting bank transparency. However, recognizing credit losses at the inception of loans can hurt a bank’s regulatory capital by reducing their retained earnings, increasing a bank’s cost of issuing new loans. In the first study, I explore the possibility that CECL might hurt the availability of bank lending even before the new standard takes effect in 2020 and beyond. I find that following the approval of CECL, more capital-constrained banks reduce their loan growth. In addition, the reduced loan growth is more pronounced for real estate loans. These loans are expected to become costlier after CECL because they typically have a longer maturity than other loan products. The above findings are shown to be stronger for banks operating under a more lenient regulator, suggesting that these types of banks expect to experience increased regulatory enforcement after CECL. I also find that relative to privately-held banks, publicly-listed banks are more likely to reduce their growth in long-term loans during the CECL transition period. This 2 finding is consistent with the notion that public banks acted more quickly to mitigate CECL’s impact because they must adopt CECL at an earlier date. Using the Community Reinvestment Act (CRA) data to sharpen the identification, I also find that more capital-constrained banks originate fewer small business loans as they prepare for CECL. These loans are essential to the local economy but riskier for banks. This study has two important policy implications. First, policymakers should consider CECL’s impact on the availability of loans to individuals and small businesses. Second, when evaluating the effects of a new accounting change, policymakers should consider changes in economic behaviors that occur both before and after the effective date of the accounting change. In the second study, I examine the consequences of the Volcker Rule, which prohibits banks from doing proprietary trading. This rule aims to reduce the likelihood of future financial crises by preventing banks from trading financial assets that could increase their asset risk. However, studies have found that following the Volcker Rule, corporate bond liquidity (i.e., the ease of trading public bonds) has decreased. In this study, I use the Volcker Rule as a bond liquidity shock to investigate whether the loss of bond liquidity encourages bond issuing firms to increase their management disclosures to replace the loss of information flowing from banks. Using a difference-in-difference design, I find that bond issuing firms are more likely to increase their management guidance after the Volcker Rule. Further, the increased management guidance is more pronounced for firms with credit ratings close to the investment-grade cutoff rather than those with very high or very low ratings. Firms with very high credit ratings are likely to have good bond liquidity, even without banks trading and holding their bonds, while banks with low ratings face inherent uncertainties that are unlikely to be resolved by increasing their management disclosures. 3 Although the topics of the two studies focus on two different economic decisions (i.e., bank loan originations and firms' voluntary disclosures), both studies touch on the consequences of post-crisis reforms in the banking sector. The first one investigates banks as a source of lending in the economy to see whether banks contract their lending in anticipation of CECL requiring them to prepare more regulatory capital for each loan they issue. The second one explores the information role banks have played in the corporate bond market. The study finds that bond issuers increase their voluntary disclosures after banks are prohibited from trading their bonds, consistent with banks no longer impounding their private information through their bond trading activities. Because each study examines a different topic, chapters are designed to be self-contained. I thus leave a more thorough discussion of the research question and contribution to the introduction sections of each chapter. 4 Chapter 2: How does accounting for loan losses influence bank lending? Evidence from the Current Expected Credit Loss Model (CECL) 2.1 Introduction CECL, the current expected credit loss model, is considered by the American Bankers Association to be the most sweeping change to bank accounting ever.1 CECL requires the life of loan estimates of expected future losses to be recorded for unimpaired loans, posing significant compliance and operational challenges for banks.2 While practitioners, lawmakers, and scholars worry about CECL’s consequences for bank loan availability, there is no direct empirical evidence on how the transition to CECL has affected bank lending. To fill this gap, I investigate how banks adjusted their lending in response to the announcement of CECL. This research question is important because bank lending plays a crucial role in a well-functioning economy, and loans make up the largest portion of a bank’s assets. The relevance of loan loss accounting is also highlighted by the post-crisis debate over whether to include loan loss allowances in calculating regulatory capital, negating its charge to retained earnings.3 Prior to the adoption of CECL in 2020, loan loss accounting in the U.S. was governed by the incurred loss model (ILM) for over four decades. ILM requires banks to recognize loan losses only when it becomes probable that a loss has occurred as of the balance sheet date. If no event has occurred that would make a loss probable, then no provision can be recognized. Critics of ILM and the probable threshold have argued that the ILM model delays loan loss recognition 1 The full official name of CECL is Accounting Standard Update (ASU) 2016-13, Financial Instruments—Credit Losses (Topic 326): Measurement of Credit Losses on Financial Instruments. 2 See the article written by American Banks Association, available at https://www.aba.com/advocacy/our-issues/cecl-implementation-challenges 3 See Ng and Roychowdhury (2014) for a detailed discussion on this debate. 5 and makes loan loss allowance too little, too late (e.g., Laeven and Majnoni 2003; FSF 2009). Many bank stakeholders contend that the absence of adequate loan loss provisions due to having to meet the probable condition was one of the major causes of the 2008 financial crisis (Beatty and Liao 2011; Kim 2021). Responding to the criticism of ILM, the Financial Accounting Standards Board (FASB) developed a new accounting standard on loan loss provisioning, announcing the current expected credit loss (CECL) model on November 18, 2015. CECL significantly changes how banks determine their loan loss provisions from 2020 onwards4. Unlike ILM’s backward-looking incurred-loss approach, CECL is forward-looking. It requires banks to maintain a loan loss allowance for all expected future credit losses throughout a loan’s contractual life from the day the loan is made. Although CECL requires banks to disclose more information about their loan quality, practitioners are worried that CECL can distort banks’ incentives to make new loans by mismatching expenses and revenues. When a loan is made, CECL requires banks to recognize expected losses due to the loan’s expected probability of default over its entire contractual life. However, CECL does not correspondingly allow banks to recognize the loan’s lifetime revenues that compensate for its default risk. While there is much ongoing debate, we have little evidence on the impact of CECL on bank lending policies. As a result, in June 2019, a group of lawmakers 4 CECL was originally set to be effective for all publicly traded banks in 2020 and for all other banks in 2021 and onwards. However, on July 17, 2019, the FASB proposed an extension to CECL’s effective date (https://www.fasb.org/jsp/FASB/FASBContent_C/ActionAlertPage&cid=1176172970471&rss=1). This proposal extended the beginning effective dates from 2020 to 2023 for small public banks and from 2021 to 2023 for non-public banks. FASB approved this proposal on November 15, 2019. Subsequently, due to the COVID-19 pandemic, in March 2020 President Donald Trump signed into the law the CAREs Act, which grants banks the option to postpone their adoption of CECL until the pandemic is over. However, these changes do not influence the analyses in this study because the sample period ends in the fourth quarter of 2018. See appendix A.1 for the history on the changes in CECL adoption dates for different types of banks. 6 proposed a bill that delays CECL until the government performs a quantitative study to investigate CECL’s impact on bank loan availability.5 CECL is likely to influence bank lending policy not only after its effective date but also when banks are preparing for it during the transition period. Many of the loans made during the CECL transition period6 will remain on bank balance sheets and be measured by CECL once CECL is adopted. The anticipated change in how these loans will be accounted for under CECL is likely to induce banks to revise their lending strategy during the CECL transition period. On day-1 of CECL adoption, bank balance sheets are expected to shrink as additional expected credit losses are subtracted from loans, reducing retained earnings, a major component of regulatory capital7. Consistent with this expectation of additional loan losses, the loan loss allowance for the 200 largest U.S. banks (which adopted CECL in the first quarter of 2020) increased by nearly 60% due to their day-1 CECL adjustment.8 In this study, I predict that banks with greater capital adequacy concerns during the CECL transition period are more likely to reduce their supply of loans to reduce their risk of violating minimum regulatory capital requirements upon CECL adoption.9 To examine CECL’s anticipatory impact on bank lending, I use a sample of bank holding companies filing the FR Y-9C regulatory reports. Using the four quarters of bank data before 5 To see the section 3 of the proposed bill on the following website: https://www.congress.gov/bill/116th-congress/house-bill/3182/text?r=1571&s=5. 6 The full CECL transition period starts from when CECL is approved by the FASB and ends when CECL becomes effective. For publicly listed banks, this was from 2016 to 2020. And for other types of banks, it was from 2016 to 2023. 7 In the sample used in this study, banks’ retained earnings make up about 58% of their Tier 1 regulatory capital. 8 The figure is obtained from an article by the American Banker Association, which is available on https://bankingjournal.aba.com/2020/06/bipartisan-group-of-senators-call-for-fsoc-study-on-cecl/. 9 For the more capital constrained banks examined in this study, a 60% increase in their loan loss allowance equates to a 4.2% decline in their equity to asset ratio – from 9.60% to 9.20%. 7 CECL was approved (i.e., 2015Q1–2015Q4), I classify banks as more or less likely to be impacted by CECL based on their regulatory capital ratios. Banks with capital ratios closer to their capital adequacy constraints are considered to be more affected by CECL, while those with healthier capital ratios are assumed to be less affected. For the first hypothesis, I predict and find that higher closeness to capital adequacy constraints is associated with a lower loan growth rate during the CECL transition period (2016Q1–2018Q4). In this study, I also examine which types of lending these banks are more likely to reduce in anticipation of adopting CECL. Longer-term loans have higher and more volatile CECL expected lifetime loss rates than shorter-term loans when the default risk is held constant. As mentioned earlier, CECL requires banks to recognize expected lifetime credit losses when a loan is made, reducing the bank’s equity capital. Thus, CECL is expected to increase a bank’s marginal cost on offering one unit of loan maturity and decrease a bank’s willingness to hold longer-term loans relative to other assets. Although I do not find a general result on the relation between the issuance of long-term loans and the capital adequacy concern during the CECL transition, I do find support for this hypothesis in two subsets of banks, which will be described more fully later in this section. A follow-up question is how banks reduce their holding of longer-term loans to mitigate CECL’s impact. Do banks lower all their existing loan products’ loan maturity but hold constant the mixture of different loan types (i.e., intensive margin)? Or do banks increase (decrease) their holdings of loan types with shorter (longer) terms (i.e., extensive margin)? To answer this question, I examine how the loan growth rates of different loan types change during the CECL transition period. I find that banks that are closer to their capital adequacy constraints decrease their lending of total real estate loans during the CECL transition period. However, these banks 8 do not have significant changes in the growth rates for other loan types (i.e., consumer and non-real estate commercial loans). When I divide real estate loans into residential real estate and commercial real estate loans, only the growth in residential real estate loans declines in the CECL transition period. The reduced growth rate only for the residential loans is consistent with banks decreasing these relatively longer-term loans to reduce their loan remaining life and mitigate CECL’s impact. To further investigate CECL’s impact on banks, I conduct two sets of cross-sectional analyses. First, I analyze whether CECL’s impact on bank lending through regulatory capital becomes stronger when banks are monitored by a more lenient regulator. I rely on the index developed by Agarwal et al. (2014) to measure regulatory leniency. I find that capital-constrained banks monitored by a more lenient regulator are more likely to contract their total loans, long-term loans, and total real estate loans during the CECL transition period. This finding suggests that these banks change their lending policy to mitigate CECL’s negative impact on their regulatory capital. This is consistent with Agarwal et al.’s finding that banks monitored by a more lenient regulator are financially weaker. Second, I also perform a cross-sectional analysis looking at public and private banks separately. Public banks could be more affected because they have to adopt CECL sooner than private banks. However, it is also possible that the private banks could be more affected by CECL if it is more costly for them to raise new equity to mitigate the reduction in regulatory capital caused by CECL. I find both public and private capital-constrained banks experience an overall reduction in loan growth rates during the CECL transition period. In addition, I find that CECL’s negative impact on long-term loans through regulatory capital is concentrated in publicly traded banks but not in their privately-held banks. This finding is consistent with public 9 banks, which must adopt CECL earlier than private banks, responding to CECL’s anticipatory negative impact on capital by reducing their issuance of long-term loans. To corroborate my interpretation with sharper empirical identification, I perform several additional analyses. In particular, I use the Community Reinvestment Act (CRA) small business lending data at the bank-county level10 to control for local economic conditions. Consistent with the main results, I find that during the CECL transition period, banks that are closer to their capital adequacy constraints are more likely to reduce their small business lending. I also find that CECL’s impact on small business lending through the regulatory capital channel is more concentrated in more-leniently-regulated banks and in public banks, consistent with the results obtained from the bank-level data. This study contributes to the policy debates on CECL by responding to the lawmakers’ call for quantitative studies on CECL. Notably, it suggests that banks with weaker capital adequacy when CECL was announced are more likely to contract their supply of loans, especially the longer-term loans, real estate loans, and small business loans. These findings help inform regulators and lawmakers about which sectors of the economy are likely to be impacted by CECL. This study also contributes to the accounting literature in two ways. First, it contributes to the research on CECL’s impact on banks. Wheeler (2021) develops a measure of lifetime expected credit losses using a method intended to mimic one approach acceptable under CECL. He finds that recorded allowances during the pre-CECL period were, on average, less than this estimate of lifetime expected credit losses, consistent with concerns that implementing the 10 See section 7 for more details on the CRA data. 10 expected loss model will adversely impact regulatory capital adequacy. My study also speaks to this concern by showing evidence consistent with CECL negatively affecting banks’ willingness to lend when their regulatory capital constraints are more severe. In a recent working paper related to CECL’s impact, Lu and Nikolaev (2020) suggest that under-recognition of expected losses (which amounts to overstating real capital) gives banks incentives and opportunities to take more risks. The findings in my paper are consistent with Lu and Nikolaev (2020). Because CECL exposes the information about under-recognition of expected losses, banks reduce riskier long-term loans and small business loans. However, the approach in this paper is different from Lu and Nikolaev. While Lu and Nikolaev create their proxy for under-recognition of expected losses to gauge CECL’s impact, I examine how banks change their lending strategy during the transition period of CECL. This study also contributes to the literature on the real effects of accounting changes. The findings in this paper are related to a fundamental question in accounting – whether and to what extent financial reporting influences firms’ investment behavior.11 This study documents the real effects of CECL on bank lending and shows that the real effects can occur even before the standard’s effective date. The results are consistent with affected firms altering their investment decisions during a new standard’s transition period (i.e., the period between the standard’s approval date and its effective date) in preparation for implementing the new standard. The results also provide an important message to policymakers that in order to estimate the standard’s full impact on the economy, they should consider a new accounting standard’s anticipatory effects as well as its effect after implementation. 11 Roychowdhury, Shroff, and Verdi (2019) provide a comprehensive review on this literature. 11 The remainder of the study is organized as follows: Section 2 discusses the institutional background and literature related to this study. Section 3 develops hypotheses. Section 4 describes the research design. Section 5 describes the sample construction process and descriptive statistics. Section 6 discusses the main results. Section 7 discusses the additional analyses. Section 8 concludes. 2.2 Background and related literature This study investigates CECL’s anticipatory impact on bank lending. To provide the institutional background relevant to the research question, I begin section 2 by providing a simple theoretical description of the role of banks in the economy as aggregators and distributors of capital (section 2.1). In section 2.2, I discuss the role that regulatory capital plays in bank supervision. In section 2.3, I discuss the evolution of U.S. bank capital regulations. Finally, I discuss loan loss accounting for banks and the recent move in bank financial reporting toward timelier accounting for expected credit losses, CECL (section 2.4). 2.2.1 Banks’ role in the economy Banks serve as financial intermediaries: collecting capital from bank depositors, debt holders, and equity holders and distributing it to various types of borrowers in the form of residential mortgages, commercial loans, consumer loans, etc. While getting access to these loans is critical to a well-functioning economy, loans are not all equal. Some loans have a lower risk and pay relatively lower interest rates because they are unlikely to default. Other loans have higher risk and pay higher interest rates to compensate for their higher risk of default. The greater the risk, the larger the risk premium that a borrower can expect to pay for getting access to funds. As risk increases, at some point the bank will decide the loan is too risky and will reject the loan. In this instance, the borrower may have to rely on some other form of risky borrowing 12 (e.g., convertible debt or high yield bonds), equity financing, or they may have to cancel the investment project. Banks do not always make lending decisions in the best interests of their stakeholders (equity holders, depositors, debt holders, banking regulators, etc.) because information asymmetry exists between bank managers and their outside stakeholders. Moreover, the banks’ high leverage and the default risk protection provided to depositors by deposit insurance offer bank managers an opportunity to take on additional risk for a higher upside profit but at the expense of debt holders. To prevent banks from taking on excessive risk, bank regulators monitor the risk exposure of banks by conducting on-site visits and by setting minimum capital ratio requirements that banks must adhere to. 2.2.2 Regulators’ role in bank supervision 2.2.2.1 Types of banking entities in the U.S. and their regulators In the U.S., most banks operate as subsidiaries, governed by a bank holding company (BHC). 12 BHCs are defined as parent companies that control one or more bank subsidiaries. The Federal Reserve is the regulator of BHCs, but much of their work is shared with state and other regulators, who monitor the bank subsidiaries. Each bank subsidiary’s bank charter type determines its banking regulators (no matter whether a BHC controls them or not). See Figure 2.1. Nationally chartered banks are supervised by the Office of the Comptroller of the Currency (OCC). State-chartered banks are supervised 12 In my sample period, 79.93% of banks in the Call Report data are governed by a BHC, and 81% of BHC have only one bank subsidiary. 13 simultaneously by their state banking department and a federal banking regulator. The structure of the U.S. bank charter system is summarized in Figure 2.1 as below. Figure 2.1 The structure of the U.S. bank charter system Which federal regulator regulates state-chartered banks depends on their membership in the Federal Reserve System. The Federal Reserve regulates banks that are members of the Federal Reserve System. The Federal Deposit Insurance Corporation (FDIC) regulates non-member banks. State-member banks and state-non-member banks are examined on an alternating annual basis by their state regulators and their respective federal regulators.13 Despite being supervised by different regulators with different strictness on regulatory enforcement, state and national banks face the same bank capital regulations (including regulatory capital requirements) and have similar banking business models (Blair and Kushmeider 2006; Nicoletti 2018). 13 This statement is based on the Riegle Community Development and Regulatory Improvement Act of 1994. 14 2.2.2.2 On-site and off-site examinations Banking regulators in the U.S. supervise the financial condition of each bank through both on-site and off-site examinations. The periodic on-site examinations typically occur on an annual basis14. During such examinations, bank examiners review various aspects of a bank’s operations to assess the bank’s overall safety and soundness. After their examinations, a written report is prepared, and a CAMELS rating is assigned to the bank. The report and the CAMELS rating are confidential and are only disclosed to the bank management. The CAMELS rating is a comprehensive assessment of a bank’s capital adequacy (C), asset quality (A), management (M), earnings (E), liquidity (L), and sensitivity to market risk (S). Each rating component is assessed on a scale of from one to five, with one representing the highest quality and five the lowest. Banking regulators provide individual ratings for each of the six CAMELS components and a composite CAMELS rating for the bank as a whole. Banking regulators also conduct off-site examinations during the time between two consecutive on-site examinations. Specifically, banking regulators analyze the periodic reports filed by banks15 to remotely monitor bank financial health. Federal Reserve Bank of New York (2020) specifies that their off-site examinations aim to help banking regulators understand the strategic business developments and changes in risk profiles of the banks they monitor. They adjust the scope of their off-site examinations based on specific events, such as (a) significant changes in a bank’s inherent risk, control processes, or key personnel; (b) increased concerns 14 As an exception to this rule, banks can be examined once every 18 months if they meet certain criteria: (1) assets below $3 billion, (2) well-capitalized, (3) CAMELS ratings of 1 or 2, (4) well-managed, (5) not operating under a formal enforcement action and (6) has not experienced a change in control in the previous 12 months. 15 Banks file quarterly Call Reports with their regulators. These are discussed further in section 2.2.3. 15 about the adequacy of internal controls; (c) the absence of sufficiently recent exams; or (d) market events. 2.2.2.3 Inconsistency between banking regulators As previously mentioned, state-chartered banks are examined alternatively by their federal and state banking regulators. Using these alternating visits and private data on the CAMELS ratings from the Federal Reserve Board, Agarwal et al. (2014) find that federal and state banking regulators differ in how they implement the same supervisory rules. In particular, Agarwal et al. (2014) find that the federal regulators systematically operate to a higher standard, downgrading CAMELS ratings almost twice as frequently as their state counterparts. State regulators are also more likely to upgrade a bank’s CAMEL rating, implying that state regulators are more lenient. The implication of this leniency is manifest in their finding that the leniency of state regulators is related to costly outcomes, such as higher bank failure rates and lower repayment rates of government assistance funds amongst state-regulated banks. Agarwal et al. also find that the leniency of state banking regulators varies in different states, and they develop a state-level index to measure this leniency. I utilize the Agarwal et al. regulatory leniency index in some of my hypothesis tests. In this study, I use the level of a bank’s regulatory capital as an indicator of when a bank is likely to be more affected by a new accounting rule that reduces shareholders’ equity. Two accounting studies apply the Agarwal et al. (2014) leniency index. Their findings imply that regulatory leniency is likely to lead to an overstatement of a bank’s net income, retained earnings, and regulatory capital. First, Costello et al. (2019) find that banks that are more leniently regulated are less likely to restate their net income downward. Second, Nicoletti (2019) 16 finds that higher regulatory leniency is associated with greater delays in loan loss recognition, which overstates retained earnings and equity capital. 2.2.2.4 The regulatory reporting system of banks In the U.S., all bank subsidiaries (regardless of their chartered status) and BHCs must file financial statement-based reports to banking regulators each quarter. Bank subsidiaries file Call Reports and BHCs file FR Y-9C reports.16 The details that must be disclosed in Call Reports are set by the Federal Financial Institutions Examination Council (FFIEC), which primarily consists of high-ranking representatives from the three federal banking regulators (i.e., the Federal Reserve, FDIC, and OCC). The details disclosed in FR Y-9C reports are set by the Federal Reserve, the designated regulator of BHCs. However, in practice, the Call Report and the FR Y-9C contain very similar reporting items, reported on almost identical schedules. The Call Report and the FR Y-9C include an income statement and a balance sheet prepared using U.S. GAAP. The two filings also include various supporting schedules that show the breakdowns of particular income statement items and balance sheet items. One area of key concern for regulators is the adequacy of a bank’s regulatory capital levels in relation to assets. Regulatory capital ratios are expressed as a capital amount over an asset amount. These will be explained more in the next section. Regulatory capital ratios are reported for bank subsidiaries in their Call Reports and for BHCs in their FR Y-9C reports. 16 This study relies on data from Y-9C reports. Therefore, this study examines banks at the BHC level, rather than at the bank subsidiary level. 17 2.2.3 Bank capital regulation history 2.2.3.1 Pre-Basel period (before 1992) Before the 1980s, banking regulators in the U.S. did not impose specific numerical capital ratio standards. Instead, banking regulators applied informal and subjective measures tailored to the circumstances of individual institutions. In 1981, triggered by the convergence of macroeconomic weakness, bank failures, and diminishing bank capital, the three primary U.S. banking regulators (i.e., the Federal Reserve, OCC, and FDIC) implemented explicit numerical capital requirements for all but the largest multinational banks. However, the numerical requirements differed by bank size, and the three regulators did not use the same definition of capital adequacy. In 1985, the International Lending Supervision Act was passed. The act introduced a common definition of regulatory capital across all three regulators and set a uniform capital requirement that all banks must adhere to. Capital adequacy was mainly evaluated using the ratio of primary capital17 to total assets. Intuitively, the capital ratio measures how much equity capital a bank has on hand to support each dollar of its assets. A higher capital ratio demonstrates a bank has a better ability to absorb a decrease in the value of its assets. A higher capital ratio, therefore, reflects a healthier bank. These requirements remained in place in the U.S. until the first Basel Accord (referred to as Basel I) was implemented in 1992.18 17 The primary capital components include (1) common equity, (2) perpetual preferred stock (i.e., preferred stock that does not have a maturity), (3) minority interests, (4) mandatory convertible instruments, and (5) the loan loss allowance. 18 This history is obtained from FDIC (2003). 18 2.2.3.2 First Basel period – Basel I (1992 – 2008) In 1992, the U.S. implemented Basel I. The most significant change introduced by Basel I was to replace total assets in the denominator of the capital adequacy ratio with risk-weighted assets. Risk-weighted assets assign higher weights to riskier assets. The intuition behind risk-weighting is that banks must hold more regulatory capital to support the riskier assets they hold. Assets considered risk-free (e.g., U.S. Treasury Securities) are assigned a zero weight and therefore do not require any capital. Assets assumed not to be risk-free but considered relatively safe (e.g., short-term claims guaranteed by other U.S. banks) are assigned a 20% weight. Assets that are risky where the loss of default is limited by collateral (e.g., residential mortgages) are assigned a 50% weight. The remaining assets are assigned a 100% weight (Beatty and Liao 2011). The numerator of the capital adequacy ratio is regulatory capital. Basel I defines two measures of regulatory capital, Tier 1 capital and total capital. Tier 1 capital is a narrower definition of capital, and it only includes the following equity accounts: common stock, retained earnings, noncumulative perpetual preferred stock19, and minority interests. Total capital includes Tier 1 capital plus other accounts (referred to as Tier 2 capital) that can absorb losses, providing additional protection for depositors. Tier 2 capital accounts include some portion of the loan loss allowance20, cumulative perpetual preferred stock, limited-life preferred stock, and subordinated debt21. Under Basel I, banking regulators used the Tier 1 capital ratio (i.e., Tier 1 19 Noncumulative perpetual preferred stock is a type of preferred stock that (1) has no fixed date on which invested capital will be returned to the shareholder and (2) does not accumulate a fixed dividend to the investor in the future if the dividend is not currently paid. 20 The maximum amount of loan loss allowance that banks can include in Tier 2 capital is equal to 1.25% of the risk-weighted assets. 21 Subordinated debt is any type of debt that will not be paid until all other senior debt is paid in full. 19 capital divided by risk-weighted assets) and the total risk-based capital ratio (i.e., the sum of Tier 1 and Tier 2 capital divided by risk-weighted assets) to monitor the financial conditions of banks. In addition to the Tier 1 capital ratio and the total risk-based capital ratio, U.S. banking regulators also monitor a bank’s Tier 1 leverage ratio, which is defined as Tier 1 capital over total assets (i.e., not risk-weighted). By limiting the Tier 1 leverage ratio, regulators aim to (1) protect against risks beyond credit risk and (2) discourage banks from becoming highly leveraged by concentrating on assets with lower risk weightings (Haubrich 2020). 2.2.3.3 Second Basel period – Basel II (2008 – 2013) One of the problems with Basel I regulatory capital was the divergence between the Basel risk weights and economic risks, such as securitizations and credit derivatives that increase risk exposure but lower the Basel I risk weights (Beatty and Liao 2014). To alleviate this problem, the 2008 Basel II included more granular risk-weighted asset measures and off-balance sheet exposures in risk-weighted assets. The numerators of the capital ratios (i.e., Tier 1 capital and Tier 2 capital) were left unchanged from Basel I. Due to the concerns about Basel II’s high implementation cost, U.S. banking regulators only required Basel II for large, internationally active banks (i.e., banks with assets over $250 billion or foreign exposures greater than $10 billion). These larger banks had to apply a new formula to calculate the risk-weighted assets, referred to as the advanced approaches. The advanced approach involves calculating a complicated formula based on expected asset losses produced by a bank’s own internal risk model. However, smaller banks could continue to adhere to the Basel I rules for calculating risk-weighted assets, referred to as the standardized approaches. 20 2.2.3.4 Third Basel period – Basel III (2013 – the current period) The 2008 financial crisis revealed weaknesses in Basel II as a bank monitoring mechanism. Basel II’s inability to prevent bank failures and the resultant taxpayer bailouts led global banking regulators to reform Basel II. Basel III was released in November 2010. Basel III strengthened the capital requirements by (1) tightening the elements that count as capital (the numerator of capital ratios), (2) improving the ways that bank risks are measured (the denominator of capital ratios), and (3) requiring that higher minimum ratios be met (Walter 2019). In July 2013, US supervisors established the capital requirements in conformance with Basel III. Banks with assets over $250 billion or foreign exposures greater than $10 billion (a.k.a. advance approaches banks) were required to comply with the Basel III requirements by January 2014. Non-advanced approaches banks were required to comply with those requirements by January 2015. Figure 2.2 below summarizes the timeline of the Basel III implementation for U.S. banks. These requirements are still in effect. Figure 2.2 Timeline of the Basel III implementation Under Basel III, U.S. banking regulators monitor two additional bank capital ratios: (1) the common equity Tier 1 capital ratio and (2) the supplementary leverage ratio. The common equity Tier 1 capital ratio is similar to the Tier 1 common equity capital ratio. The only difference is that the numerator of the ratio (the common equity Tier 1 capital) only contains a subset of what is included in Tier 1 capital. The common equity Tier 1 capital is designed to be 21 the highest quality of regulatory capital, as it absorbs losses immediately when they occur. It includes only the items that are directly related to common shareholders’ equity, such as common stock, retained earnings, accumulated other comprehensive income, and certain minority interests22. The supplementary leverage ratio is similar to the Tier 1 leverage ratio discussed previously. However, it only applies to the advanced approaches banks. It equals the Tier 1 capital divided by total leverage exposure, which is a comprehensive measure of asset risks, reflecting both on- and off-balance sheet risk exposures. 2.2.3.5 Consequences of violating capital requirements – FDICIA prompt corrective action requirement The Federal Deposit Insurance Corporation Improvement Act of 1991 (FDICIA) is an essential part of the U.S. capital regulation system. FDICIA specifies the consequences of violating bank capital requirements by establishing tripwires below which supervisors must take prompt corrective actions. These tripwires specify the minimum regulatory capital requirements that all banks must adhere to. Minimum regulatory capital requirements of the five regulatory capital measures are as follows: (1) 4.5 percent for the common equity Tier 1 capital ratio, (2) 6 percent for the Tier 1 capital ratio, (3) 8 percent for the total risk-based capital ratio, (4) 4 percent for the Tier 1 leverage ratio, and (5) 3 percent for the supplementary leverage ratio, with the last ratio only applying to the advanced approaches banks. 22 Certain minority interests are defined as the minority interests held by consolidated depository institutions or foreign bank subsidiaries. 22 The FDICIA-mandated prompt corrective actions include (1) heightened supervisory monitoring, (2) requiring the bank to submit a capital restoration plan, (3) restrictions on executive pay, and (4) requiring prior supervisory approval on any acquisitions or new branch formations. Figure 2.3 below summarizes the definitions of the five regulatory capital ratios and their corresponding thresholds for adequate capitalization. Capital ratio Numerator Denominator The minimum threshold for adequate capitalization Common equity Tier 1 capital ratio Common equity Tier 1 capital = common stock + retained earnings + accumulated other comprehensive income + certain minority interests** Risk-weighted assets 4.5% Tier 1 capital ratio Tier 1 capital = Common equity Tier 1 capital + noncumulative perpetual preferred stock + non-qualifying minority interests Risk-weighted assets 6% Total risk-based capital ratio Total capital = Tier 1 capital + Tier 2 capital where, Tier 2 capital = cumulative perpetual preferred stock + limited life preferred stock + subordinated debt + loan loss allowance* Risk-weighted assets 8% Tier 1 leverage ratio Tier 1 capital Total assets 4% Supplementary leverage ratio Tier 1 capital Total leverage exposure*** 3% * The loan loss allowance that banks can include in Tier 2 capital is limited to a maximum of 1.25% of risk-weighted assets. ** Minority interests held by consolidated depository institutions or foreign bank subsidiaries. *** A comprehensive risk exposure measure, reflecting both on- and off-balance sheet exposures. Figure 2.3 A summary of FDICIA bank regulatory capital requirements 2.2.4 Loan loss accounting The accrual accounting principle requires banks to record loan losses using the allowance method. Theoretically, banks record the loan loss allowance for the expected future losses that 23 will occur if a borrower does not repay according to the loan contract. The word expected is ambiguous in the sense that it can be defined in two ways. First, expected can be defined as the expected value of future losses, i.e., the sum of loss given default in different states of the world multiplied by the corresponding probability. Alternatively, the expected can be defined as the losses that are expected to happen. The expected-to-happen losses are recognized in the period in which the loss events occur, and the events are very likely to make the loans uncollectible. For example, suppose that a car factory, the primary employer in a rural town, closed on December 1, 2018. Even though on December 31, 2018, no loans made by a bank in that town have defaulted, the bank knows that a major portion of the loans will not be repaid due to the factory closure. Under the incurred loss approach, the bank must recognize the loan loss allowance and bad debt expenses connected to the factory closure for the year 2018. The ambiguity in defining expected future loan losses reflects the notion that different financial statement users have different demands for loan loss-related information. Thus, there is no perfect, one-size-fits-all loan loss accounting standard. Investors of bank stocks likely prefer the loan loss accounting that generates expected values of future loan losses, which are more value-relevant. However, auditors and banking regulators likely prefer the loan loss accounting that generates the incurred losses, which are more reliable, objective, and easier to monitor. After discussing the theoretical aspects of loan loss accounting, the following subsections discuss loan loss accounting practices in the U.S. 2.2.4.1 The incurred loss model (ILM) Before the introduction of CECL, the incurred loss model (ILM) governed accounting for loan losses. Under ILM, there are two types of loans: loans that are impaired and loans that are 24 not. For impaired loans23, banks use ASC 310-10-35 (formerly known as FAS 114) and analyze the loans individually using observable market value or the discounted cash flow method. Even though impaired loans are individually large and heterogeneous, they account for only around 1.5% of a median bank’s loan portfolio (Table 1, Wheeler 2017). For nonimpaired loans, banks use ASC 450-20 (formerly known as FAS 5) to analyze their nonimpaired loans in aggregate. Nonimpaired loans cover most loans, including pools of homogeneous loans (e.g., credit card loans, residential real estate loans). ASC 450-20 is also called the incurred loss model (ILM) because it requires banks to recognize credit losses only when (1) the credit losses are estimable and (2) it is probable24 that the credit losses have been incurred on the balance sheet date. Grant Thornton, a large international accounting firm, explains the probable condition25: “A loss, for it to be accruable, must have already occurred by the date of the financial statements. In other words, expected future losses are not accruable, no matter how likely. This is what is meant by references to the incurred loss model. For example, consider a hurricane bearing down on the Gulf Coast and a property-casualty insurance company with policies outstanding in the soon-to-be-affected area. No accrual for losses can be made until the hurricane hits because, until then, no losses have been incurred by the insurer.” 23 According to ASC 310-10-35, a loan is classified as impaired when, based on current information and events, it is probable that the company will be unable to collect all amounts due according to the terms of the contractual loan agreement. 24 ASC 450-20-20 defines probable as likely to occur. While the assessment of these terms is subject to an entity’s judgment, likely under U.S. GAAP typically is considered a much higher threshold (i.e., approximately 80 percent) than more likely than not under IFRS (i.e., greater than 50 percent). See an article written by Deloitte (one of the biggest U.S. accounting firms): https://web.archive.org/web/20190921021222/www.iasplus.com/en-us/standards/ifrs-usgaap/contingencies). 25See https://www.grantthornton.com/~/media/content-page-files/financial-services/pdfs/2016/Allowance-for-loan-and-lease-lossess-ALLL.ashx 25 2.2.4.2 The current expected credit loss model (CECL) Regulators, scholars, and investors have at least two major concerns with using ILM to account for a bank’s loan losses. First, they blame the delays in loan loss recognition under ILM for exacerbating the severity of economic downturns. Their argument runs as follows. ILM’s probable threshold delays loan loss recognition during good times and results in excessive loan loss recognition during bad times. In turn, the excessive loan losses recorded during bad times lead to a depletion of retained earnings, increasing the risk that banks will have insufficient regulatory capital to support their risky assets during periods of economic distress. The risk of being undercapitalized forces affected banks to decrease their risky assets and/or increase their equity capital. However, since raising equity capital is costlier during downturns, banks are more likely to downsize their risky assets by reducing loan supply (Acharya and Ryan 2016; Beatty and Liao 2011; Laeven and Majnoni 2003). Second, regulators, scholars, and investors argue that the probable threshold results in an allowance estimate that does not provide investors with enough information about the full extent of expected credit losses over the entire contractual life of a bank’s loans. Ultimately, this leads to financial reports that are not transparent enough for making informed investment decisions.26 Responding to the two criticisms of ILM, the FASB replaced ILM with the current expected credit loss model (CECL) in Accounting Standards Update No. 2016-13.27 CECL 26 See the article by American Banker Association on the following website: https://www.aba.com/archive/Issue_Sites/Pages/issues_CreditImpairmentModel.aspx 27 The official name of the new CECL standard is “Financial Instruments – Credit Losses (ASC 326-20): Measurement of Credit Losses on Financial Instruments.” 26 became effective in 2020 for large public banks and in 2023 for all other banks.28 The American Bankers Association calls CECL the biggest change in bank accounting over the past 40 years. To provide a complete view of expected future credit losses, CECL removes the probable loss threshold and requires a lifetime loan loss allowance to be established whenever a loan is originated. Under CECL, banks must estimate their loan loss allowance based on (1) historical credit loss experience on financial assets with similar risk characteristics, (2) current conditions, and (3) reasonable and supportable forecasts that affect the collectability of the remaining cash flows over the contractual term of the financial assets (Federal Reserve System 2020). While CECL does not affect the aggregate lifetime amount of loan charge-offs, it significantly shifts the timing of recognizing loan losses by requiring banks to recognize all expected losses at loan origination. When CECL is first adopted (referred to as day-1), banks must estimate the lifetime expected credit losses of all the loans on their balance sheets. To adjust the ILM loan loss allowance to the CECL allowance, banks must post the following journal entry: Dr. Cr. Retained earnings Y = X − Z Deferred tax assets Z = X*effective tax rate Loan loss allowance X Figure 2.4 The journal entry when CECL is first adopted (day-1) 28 CECL was originally set to be effective for all public banks in 2020 and for all other banks in 2021 and onwards. However, on July 17, 2019, the FASB proposed an extension to CECL’s effective date (https://www.fasb.org/jsp/FASB/FASBContent_C/ActionAlertPage&cid=1176172970471&rss=1). This proposal extends the beginning effective dates from 2021 to 2023 for small public banks and from 2022 to 2023 for non-public companies. Small public banks are defined as the public banks with a public float of less than $250 million, or annual revenue of less than $100 million. This proposal was approved by the FASB on November 15, 2019. Subsequently, due to the COVID-19 pandemic, in March 2020 President Donald Trump signed into the law the CAREs Act, which grants banks the option to postpone their adoption of CECL until the pandemic is over. However, the change due to COVID-19 does not influence the analyses in this paper because the sample period ends in the fourth quarter of 2018. See appendix A.1 for the history on the changes in CECL adoption dates for different types of banks. 27 In the above journal entry, the loan loss allowance is credited because the broader definition of the loan loss allowance means that, on average, banks will adjust their allowance for loan losses upward.29 The above adjustment reflects the fact that the deferred tax asset account is debited for the effective tax rate portion of the allowance adjustment. The income tax regulations require loan losses to be excluded from expenses until the loan becomes wholly worthless. The retained earnings account is debited for the after-tax portion of the allowance adjustment. On day-2 of CECL adoption and beyond, banks must adjust their loan loss allowance by recording a debit or credit to the loan loss provision at the end of each quarter. The day-2 journal entry for CECL is similar to the entry under ILM, involving the loan loss provision account and the loan loss allowance account30. Theoretically, the adjustment consists of three components. First, banks recognize additional expected credit losses for any new loans issued during the quarter. This component of loan loss reserve adjustment is unique for CECL because ILM does not allow banks to recognize future expected credit losses when loans are issued. This component depends on the current and forecasted future economic conditions, so the same type of loans originated at different times can have different amounts of initial loan losses. Second, banks adjust upward or downward their previous estimates of loan losses on loans that have been outstanding throughout the quarter because CECL requires the loan loss allowance to reflect changes in current and expected future economic conditions. Although ILM 29 The prediction of an upward adjustment is consistent with the actual CECL adjustment recorded by the large public banks that adopted CECL in 2020Q1. According to the 2020Q1 financial reports, the large public banks increased their loan loss allowance by between 31% and 52% on day 1 CECL. See https://www.moodysanalytics.com/-/media/article/2020/cecl-adoption-and-q1-results-amid-covid-19.pdf for more details. 30 If banks need to adjust upward (downward) their loan loss allowance, then they debit (credit) the loan loss provision and credit (debit) the loan loss allowance. 28 has a similar component, ILM does not allow banks to incorporate any forecasted economic conditions in their loan loss estimate. As you will recall, ILM allows banks to recognize loan losses only when the losses become probable and estimable. Third, banks adjust the loan loss allowance when a loan reaches the end of its life (i.e., either being repaid or charged-off). For example, if a loan is fully repaid with no default but had been estimated to have 100 dollars of loan loss allowance, then on the repayment date, the bank adjusts downward the loan allowance by 100 dollars. This component exists in the ILM regime as well. In the long run, if bank managers estimate loan losses without manipulation, this component is expected to have a time-series mean of zero. 2.2.4.3 Criticism of CECL CECL seems to increase bank disclosures by requiring banks to provide a longer horizon of credit loss information for all loans with a positive probability of default. However, CECL has also sparked concerns regarding its unintended negative impacts. The most relevant concern to this paper is that CECL can distort banks’ incentives to lend. CECL requires that when banks originate a loan, they must recognize all loan losses expected to occur over the loan’s entire contractual life when banks originate a loan. However, CECL does not correspondingly allow banks to immediately recognize the higher expected future interest revenue banks receive in compensation for taking on higher default risk. This asymmetric treatment creates a mismatch between expenses and revenues, distorting banks’ incentive to originate new loans and grow their loan portfolios (PwC 2016; U.S. Congress 2018; Ryan 2019). Relatedly, FASB member Lawrence Smith also brought up this mismatching concern. At the AICPA’s National Conference on Banks and Savings Institutions, he stated that, while he 29 supported disclosure of expected losses, he would vote against the proposal because it requires banks to recognize a loss on the day that a bank originates a loan (Wheeler 2021). As mentioned in section 1, the widespread debate about the merits of CECL has led banks to lobby regulators and the FASB to delay CECL’s implementation. In 2019, the U.S. Senate and House of Representatives proposed bills to delay CECL until the government could evaluate CECL’s impact on bank lending.31 Responding to this call, this study takes an initial step to investigate how banks change their lending strategy after CECL was approved in 2015. 2.2.4.4 Loan loss accounting, regulatory capital, and bank lending The Financial Stability Forum (FSF) Report (2009) identified ILM as one potential cause of procyclical bank lending32 during the 2007 financial crisis. ILM’s probable threshold for loss recognition requires banks to delay loss recognition in good times, creating an overhang of loan losses that carry forward and must be recognized in bad times. Due to such loss overhang, banks must record capital-decreasing provisions during economic downturns, increasing the pressure on them to add new capital or reduce lending in order to maintain their capital ratios above the capital adequacy constraints. While a bank can raise equity to maintain its capital when recognizing larger loan losses, this may prove to be too costly during downturns. Wall and Koch (2000) argue that banks experiencing losses and falling capital during bad times are likely to have relatively low stock 31 See the proposed bills on: (1) https://www.congress.gov/bill/116th-congress/senate-bill/1564/text?r=77&s=4 for the U.S. Senate (2) https://www.congress.gov/bill/116th-congress/house-bill/3182/text?r=1571&s=5 for The U.S. House of Representatives. 32 Procyclical lending refers to supply-driven changes in lending that amplify the business cycle. That is, procyclical changes in lending are systematic, cyclical changes in lending not explained by changes in the demand for loans (Wheeler 2019). 30 prices. Their managers may be unable or unwilling to issue new stock at what they perceive as a distressed price. Thus, rather than raising new shares to maintain their capital ratio, banks are more likely to downsize their risky assets by reducing their loan issuances. The argument that the accounting for loan losses influences bank lending through regulatory capital is consistent with the well-developed capital crunch theory in the economics literature (e.g., Bernanke and Lown 1991; Berger and Udell 1994; Peek and Rosengren 1995; Peek and Rosengren 1997; Furfine 2001). The capital crunch theory states that when banks need to raise their equity-to-asset ratio, they usually choose to cut the issuance of new loans (i.e., decrease risky assets) to avoid the high costs of raising new equity. Under CECL, the requirement to add new capital to compensate for a growing loan loss allowance is expected to occur in two circumstances. First, it likely occurs when there is an increase in expected defaults during an economic downturn33. Second, it likely occurs when loan balances are growing. In the first instance, the capital crunch theory is expected to apply. In the second case, growth in loans can occur both during economic downturns and when the economy is expanding. Even in strong economic times, banks will likely prefer to minimize new equity issuances to avoid diluting the claim of their existing equity holders and maintain the multiplier effect that leverage has on their return on equity. Banks that do not want to add common equity could issue preferred stock or subordinated debt to boost their regulatory capital. Adding noncumulative perpetual preferred stock would help increase Tier 1 capital, and adding preferred stock and subordinated debt would help 33 This is similar to what would occur under ILM. However, the magnitude of the shock to the allowance, and to regulatory capital, is likely to be less than under ILM as some allowance for expected future losses will already have been accrued under CECL. 31 increase total capital. However, they do not change a bank’s Common Equity Tier 1 capital (CET1). The CET1 capital ratio is considered particularly important under Basel III. 2.2.4.5 Prior research on how the accounting for loan losses influences bank lending through capital ratio requirements Three recent empirical studies in accounting examine loan loss accounting’s impact on lending through the capital ratio requirements. First, Beatty and Liao (2011) examine a sample of U.S. publicly listed banks and find a positive association between delays in loan loss recognition34 and lending contraction during economic downturns. They also find that banks with a greater delay in their loan loss recognition have a stronger association between low regulatory capital and lending contraction. Their finding is consistent with delaying loan loss recognition exacerbating the procyclical lending through the capital ratio channel. Second, Kim (2021) also finds that banks with a greater delay in their loan loss recognition experience a greater reduction in their lending during the financial crisis. Unlike Beatty and Liao (2011), Kim uses mortgage loan data at the contract level to control for geographical demand-side factors. The reduction in mortgage lending Kim finds in his study is stronger for less-well-capitalized banks and for mortgage loans with a higher capital burden. His finding is also consistent with loan loss accounting influencing banks’ willingness to lend being driven by concerns over meeting capital ratio requirements. Third, Jayaraman et al. (2019) use the late 1990s emerging market crisis to capture an adverse supply shock to the capital of U.S. banks. They show that the reduction in lending due to 34 Beatty and Liao (2011) consider banks that delay accruing a loan loss provision to be more exposed to capital losses during downturns. 32 the emerging market capital shock is lower for the banks that appear to smooth their loan loss provisions than other banks. Banks are considered smoothing their loan loss provisions if they book larger (smaller) provisions when pre-provision earnings are high (low). In sum, the first two studies (Beatty and Liao 2011; Kim 2021) find that delaying the recognition of loan loss provisions influences bank lending by depleting their regulatory capital during downturns. The third study by Jayaraman et al. finds that loan loss smoothing is likely to mitigate a negative shock on regulatory capital. However, what is still underexplored is how future changes in the measurement of loan losses brought about by CECL influence banks’ current lending strategy through the capital ratio channel. 2.3 Hypothesis development As the CECL transition period is crucial to understanding this study‘s hypotheses, I begin by defining this period before developing my hypotheses. The rules governing when banks must adopt CECL and include the CECL loan loss allowance in calculating their regulatory capital had changed many times since November 2015, when CECL was first announced. Also, adoption dates differ for different types of banks (e.g., SEC filers versus non-SEC filers and public banks versus private banks).35 Initially, when CECL was first announced by the FASB in November 2015, it was scheduled to become effective for financial reporting and calculating regulatory capital in 2019Q1 for all SEC filing public entities. However, when the final draft of the standard was released seven months later in June 2016, the first effective date by which SEC-filing public 35 Appendix A.1 lays out the various timelines, set by the FASB and by bank regulators, for when CECL would become effective for financial reporting and for calculating regulatory capital. 33 entities must adopt CECL was extended by one year to 2020Q1. This schedule continued to apply for financial reporting and calculating regulatory capital until December 21, 2018, when bank regulators offered banks relief by delaying when the financial reporting day-1 adjustment would be incorporated into the calculation of regulatory capital. Regulators approved a new rule giving all banks the option to phase in their day-1 financial reporting adjustment to their regulatory capital over three years on a quarterly basis. For this study, I treat the implementation schedule announced in June 2016 as the primary implementation schedule. I examine how the prospect of CECL impacts bank lending during the transition period from 2016Q1 to 2018Q4. By stopping in 2018Q4, I avoid the impact of changing expectations due to the regulatory capital phase-in. I also avoid two further financial reporting extensions to CECL adoption dates, one announced in November 2019 and one in March 2020. Unfortunately, by stopping at the end of 2018, before the adoption date has been reached, I will miss some of the transitional adjustments that banks make, particularly for shorter-term loans. 2.3.1 CECL and bank lending (H1) CECL has one of the longest transition periods, from announcement to the adoption date, of any new accounting standard. In part, this is due to the significance of its impact on the banks that must adopt it. This transition period allows us to observe CECL’s impact on bank lending practices even before it comes into effect. Longer-term loans issued during the transition period are likely to be still outstanding when CECL must be adopted. In addition, some shorter-term loans are likely to roll over and be renewed for a period that extends into the CECL adoption period. For example, a credit card loan may be repaid by the cardholder before CECL’s adoption date, but the cardholder will continue 34 to hold the credit card and is unlikely to have their credit limit reduced before CECL’s adoption date. Therefore, the bank issuing the card can expect to remain exposed to the borrower for longer than the term of the initial loan. As mentioned previously, in section 2.4.2, CECL requires banks to expand how they estimate their allowance for expected future credit losses. Whereas under ILM, banks were only able to look at past events in determining what losses had been incurred and should be accrued as a reduction in the carrying value of their loans, under CECL, current economic conditions and future forecasts must also be considered. Since CECL requires the loan loss allowance to also reflect expected future losses that have not yet been incurred, it is expected to substantially increase the size of bank loan loss allowances. The increase in loan loss allowance will reduce their retained earnings, a primary component of regulatory capital36. A bank’s sensitivity to this expected capital decline and whether they alter their lending policies to limit the degree of expected capital adjustment to CECL is likely to depend on the current level of their capital ratios and how close they are to regulatory capital minimums. When capital-constrained banks experience a negative shock to their regulatory capital, the capital crunch theory (see section 2.4.4) argues that they will tend to alleviate the situation by originating fewer loans rather than by issuing new equity. By issuing fewer loans during the CECL transition period, banks can build a larger capital buffer to absorb the day-1 capital shock. This is because fewer loans mean lower risk-weighted assets, the denominator in three of the five 36 In the sample used in this study, banks’ retained earnings make up about 58% of their Tier 1 regulatory capital. Moreover, as mentioned in footnote 26, the prediction of an upward adjustment is consistent with the actual CECL adjustment recorded by the large public banks that adopted CECL in 2020Q1. According to the 2020Q1 financial reports, the large public banks increased their loan loss allowance by between 31% and 52% due to their day-1 adjustments. See https://www.moodysanalytics.com/-/media/article/2020/cecl-adoption-and-q1-results-amid-covid-19.pdf. 35 regulatory capital ratios. In addition, issuing fewer loans during the transition period also means a smaller day-1 capital adjustment when CECL becomes effective, reducing the severity of the negative shock to the numerator of their regulatory capital ratios.37 This leads to the following hypothesis: H1: Banks with lower capital adequacy ratios originate fewer loans following the approval of CECL, compared to other banks that are not as capital constrained. Banks have four years to prepare for CECL implementation. Therefore, it could be argued that this event is not a shock, and they have sufficient time to adopt other approaches (such as raising new equity) to replenish their capital levels. However, adding new capital is not without a cost (section 2.2.4.4). Banks are able to enhance their return on equity (ROE) profitability ratio through the use of leverage, which acts as a multiplier of their return on assets (ROA). Therefore, banks have an incentive to keep their asset to equity ratio high, so their ROE can be higher for the same ROA. The choice between raising new equity, or cutting back lending will depend on the relative cost of each. If raising new equity capital increases their after tax cost of capital, then banks may be unwilling to do so to counteract the impact of CECL on their regulatory capital.38 On the other hand, to the extent that adding new loans is profitable, banks may add equity to help maintain their loan market share and profitability. Thus, we may not observe a decline in new lending by capital constrained banks if it is less costly for them to raise new equity than the cost of cutting back their lending. 37 The anticipation effect of CECL’s day-1 adjustment should happen only during the transition period. After CECL is adopted, if lending levels remain relatively constant then things are in the steady state. So, if there is no additional shock then CECL is not expected to have any additional effects after the adoption date. 38 In section 7.3, I investigate whether capital constrained banks that increase their equity to asset after CECL is announced are less likely to adjust their policies. 36 2.3.2 CECL and long-term loans (H2) Intuitively, if a loan is issued for a longer-term, there is more time for the loan to default. Therefore, ceteris paribus, the longer the loan term, the larger the loan loss allowance adjustment that will be required when CECL takes effect. This higher implicit cost of longer-term loans reported under CECL leads us to expect that, during the CECL transition period, banks will adjust their holdings of longer-term loans downward in anticipation of their larger allowance for expected loan losses once CECL is adopted. As discussed previously, more capital-constrained banks are expected to be more concerned about CECL’s impact on their capital status. Since longer-term loans are associated with higher expected credit losses, capital-constrained banks will find them costlier to hold. Therefore, capital-constrained banks are likely to issue fewer long-term loans to decrease the size of the adjustment to their loan loss allowance and regulatory capital when CECL is adopted. This will help such banks reduce their risk of being undercapitalized and subject to regulatory intervention both at the time of adoption and in the future. Issuing fewer long-term loans also helps banks to lower the volatility of their loan loss allowance. With a shorter loan remaining life, banks can predict their future loan losses more accurately because the horizon that banks must forecast is shorter. Whether banks can predict loan losses reliably is important to bank stakeholders. As shown in the comment letters on CECL submitted to the FASB and the SEC, bank investors expect to use the new CECL disclosures to evaluate management’s ability to forecast expected credit losses reliably.39 The reliability of a 39 See the following website for a summary of comment letters received by the FASB: www.fasb.org/jsp/FASB/Document_C/DocumentPage&cid=1176162917634 37 bank’s loan loss estimate is also important to banking regulators. As shorter loan terms improve the accuracy with which banks can estimate their future loan losses, they help minimize the chance that such banks will have inadequate loan loss reserves. Wheeler (2019) finds that banks with inadequate loan loss allowances are more likely to be placed under enforcement actions by banking regulators, restricting their lending activities. Therefore, more capital-constrained banks have a greater incentive to hold fewer long-term loans during the CECL transition period. This leads to the following hypothesis: H2: Banks with lower capital adequacy ratios issue fewer long-term loans following the approval of CECL, compared to other banks that are not as capital constrained. While this prediction is expected to hold, ceteris paribus, all loans are not equal in their default risk per unit of time. Therefore, banks may change the mix of their loan portfolios and the duration of their loans in anticipation of CECL. This possibility is explored in the next hypothesis. 2.3.3 CECL and loan types (H3) CECL’s impact on bank lending policy during the CECL transition period is likely to vary between loan types. One reason to expect this is because the probability a new loan issued during this period will remain on a bank’s balance sheet when CECL is adopted depends on its term-to-maturity, which varies by loan type. Any loans that are issued during this CECL transition period (starting in 2016Q1 and ending in 2018Q4) that are expected to be repaid before CECL is scheduled to take effect (in 2020Q1 for SEC filing public banks, in 2021Q1 for non-SEC filing public banks and in 2021Q4 for all other banks), will be accounted for over their entire life using the ILM and the issuer’s regulatory capital will not be impacted by CECL. On the other hand, new loans issued during 38 this period, with a maturity date beyond the expected CECL adoption date, can be expected to impact bank regulatory capital upon adoption.40 Therefore, longer-term loans issued during the transition period will be more costly for banks to issue, both because they result in a larger CECL adjustment (as discussed in section 3.2) and because they have a higher likelihood of being on bank balance sheets when CECL is finally adopted. This higher cost likely will impact initial loan terms, especially for capital-constrained banks, pushing up the interest rate they will require to issue such loans. The higher costs of borrowing for these types of loans will drive down demand, resulting in lower loan growth rates. This study predicts that the growth rates for residential loans and commercial real estate loans are likely to decrease during the CECL transition period. This is because new residential first mortgages (which account for the majority of all residential loans) are most often issued for 30 years.41 Also, the typical term for new commercial real estate loans ranges from five to 25 years, depending on the type of commercial property used for security.42 Therefore, most of the principals of residential and commercial real estate new loans, issued during the CECL transition period, are likely to remain on bank balance sheets after CECL is adopted. In contrast, during the CECL transition period, banks are more likely to continue with their existing (pre-CECL) loan contracting policies for consumer and commercial non-real estate loans, which are typically issued for a shorter term. New loans of these types are less likely to 40 To the extent that some loans issued during the CECL transition period are likely to roll-over and become new loans with maturities that extended into the post CECL adoption period they may also be a concern to bank management. However, such roll-overs typically allow the bank to adjust the contract terms and/or the interest rate on the loan, making their CECL adoption impact less of a concern for management. 41 See an article from Forbes, which is available at https://www.forbes.com/advisor/loans/what-are-loan-terms/. 42 See an article from ValuePenguin (a publicly listed lending research firm), which is available at https://www.valuepenguin.com/small-business/commercial-real-estate-loan. 39 remain on bank balance sheets when CECL becomes effective due to their typically shorter maturities. Most consumer loans are made up of auto loans (normally issued for either four or five years), personal loans (frequently issued for terms of between two and five years)43, and credit card debt. While credit card loans have no stated term to maturity, and some credit cardholders carry this debt for many years, the majority of cardholders pay off their credit card debt in under one year.44 We do not have access to information on the term-to-maturity of commercial non-real estate loans on their grant date. However, their average remaining life would be a lower bound for the typical loan term. S&P Global (2018) and Killian and Ding (2020) report that U.S banks hold commercial non-real estate loan portfolios with an average remaining life of 1.5 years. The Federal Reserve also shows that at the end of 2015Q4, the average loan remaining life of commercial non-real estate loans held by U.S. banks was 790 days, which equals 2.16 years. These statistics on loan terms demonstrate that compared to residential and commercial real estate loans, consumer and non-real estate commercial loans issued during the CECL transition period are more likely to be repaid before banks adopt CECL. While some shorter-term loans, issued later in the transition period, can be expected to be on bank balance sheets after CECL is adopted, a larger proportion of their principal is likely to have been repaid. Even though consumer loans and non-real estate commercial loans have higher net charge-off rates than the typical residential and commercial real estate loans, their shorter loan terms help offset their higher default rates. Therefore, I expect bank management to be more 43 These loan statistics are from the Federal Reserve statistics are available at https://www.federalreserve.gov/releases/e2/201512/default.htm. 44 Killian and Ding (2020) report that on average, credit card loans have a remaining life of 0.75 years. 40 concerned about residential and commercial real estate loans during the CECL transition period examined in this study. Thus, during the CECL transition period, capital-constrained banks are more likely to change the loan contract terms of residential real estate and commercial real estate loans, making them less attractive to borrowers while leaving their consumer loans and commercial non-real estate loans relatively unchanged. As a result, the cost of these two types of real estate loans, for capital-constrained banks, is predicted to increase, and we are more likely to observe a decrease in their growth rates. This leads to the following hypothesis: H3: Banks with lower capital adequacy ratios issue fewer residential loans and commercial real estate loans (but not fewer commercial non-real estate loans and consumer loans) following the approval of CECL, compared to other banks that are not as capital constrained. 2.3.4 Cross-sectional Analysis (H4 and H5) 2.3.4.1 Regulatory leniency (H4) CECL’s impact on bank lending is likely to vary depending upon the degree to which a bank’s regulator is strict versus lenient in enforcing the regulatory capital requirements. Banks that are monitored by a stricter regulator are likely to already be operating with a more conservative capital structure and risk exposure. For example, banks that are subject to stress testing are likely to be closely monitored by their regulators even before CECL was approved.45 45 Stress-tested banks account for only 16% of the BHCs in my sample because the Fed only requires stress testing for very large BHCs. In 2012, the Fed introduced stress testing for BHCs with total assets greater than $50 billion, and in 2014, as part of the Dodd-Frank Act, this was expanded to include banks with assets between $10 billion and $50 billion (Cortés et al 2020). These large banks are likely to have a different business model from other smaller banks. Therefore, I do not use bank size as a proxy for regulatory leniency. 41 There are at least two possible reasons why some regulators might be more lenient than others. First, some regulators may lack adequate resources, including highly skilled staff, to sufficiently monitor all the banks under their jurisdictions. If this is the case, there will be greater information asymmetry between these leniently regulated banks and their regulators. CECL requires all banks to disclose more information about their expected loan losses. Therefore, banks with a more lenient regulator are likely to be concerned that their CECL-required disclosures will reveal new information about their capital status and asset risk that will trigger costly regulatory intervention. In order to mitigate the risk of such actions, leniently regulated banks are more likely to rein in their lending during the CECL transition period than their more strictly regulated counterparts. Thus, after CECL was approved, capital-constrained banks with a more lenient regulator are more likely to be impacted by CECL. However, it’s also possible that regulatory leniency observed in prior research is due to regulatory forbearance rather than due to the ineffectiveness of regulatory monitoring.46 If this is the case, lenient regulators may already be aware of the real capital status and riskiness of the banks they regulate, and they will not learn new information and change their bank monitoring level after CECL. If state regulators are more lenient by choice, then regulatory leniency is unlikely to be a determinant of banks’ lending strategy during the CECL transition period. In addition, banks that are regulated by the more lenient state-level regulators are also regulated by federal regulators every other year (or regulator rotation). So, the prospect of being subject to a more rigorous federal regulator may mean all banks are affected equally. Ultimately, whether 46 For example, state regulators can be expected to practice greater forbearance in enforcing regulatory capital requirements than their federal counterparts because they are afraid stricter enforcement would trigger a reduction in lending and hurt the local economy (Agarwal et al. 2014). 42 CECL’s impact through the capital channel is stronger for banks with lenient regulators is an empirical question. This leads to the following hypotheses: H4a: CECL’s negative impact on loan growth is stronger when banking regulators are more lenient. H4b: CECL’s negative impact on growth in long-term loans is stronger when banking regulators are more lenient. H4c: CECL’s negative impact on growth in real estate loans is stronger when banking regulators are more lenient. 2.3.4.2 Public banks versus private banks (H5) CECL’s impact on lending can also vary with a bank’s ownership structure. In particular, it can depend on whether a bank’s common equity is publicly listed or not. Publicly-listed banks differ from privately-held banks on two dimensions (Nichols et al., 2009). First, public banks, on average, have greater separation between owners and managers, leading to more severe agency problems. Public banks have more dispersed equity ownership because they sell their shares to outside equity investors who are not actively involved in management. Because the outside investors do not have enough incentives to closely monitor the actions of managers, managers of public banks are more able to exploit information asymmetry to shirk and consume excessive compensation. In addition, managers of public banks are likely to own a smaller fraction of the banks commone equity. Their smaller shareholdings lead public bank managers to bear a smaller proportion of the costs if they take excessive risk. Therefore, public banks are expected to engage in more extensive contracting and monitoring mechanisms to align the incentives of owners and managers. 43 The second difference between public and private banks is their access to capital. The shares of a public bank are traded in public capital markets, so it’s easier for investors to find counterparties to trade with at a lower transaction cost. Therefore, public banks are likely to enjoy a lower cost of raising capital, avoiding the liquidity premium paid by private banks. Ex-ante, it is not clear whether public banks or private banks are expected to be more affected by CECL when their regulatory capital is closer to its regulatory minimum. On the one hand, public banks may be less affected by CECL when they are capital constrained because they have better access to capital markets. Thus, public banks are likely to be more willing to raise new capital than reduce lending to maintain their capital ratio. In this sense, it is less costly for public banks to raise new equity than private banks because they don’t have to pay the liquidity premium that a private bank would have to pay. In contrast, private banks must undergo a fundamental and costly change in ownership structure in order to raise new equity (Nichols et al., 2009). For example, a private bank could become publicly listed in order to have access to the public capital market to raise new equity. However, such change would be extremely costly and require additional disclosures that public companies must adhere to. Another option would be to remain private and conduct a private placement of new equity. However, this would mean giving up a significant portion of their existing control rights. On the other hand, public banks may be more affected by CECL during the transition period examined by this study. Public banks were scheduled to adopt CECL two years earlier than private banks47, so it is more urgent for them to prepare for CECL’s adoption sooner. 47 CECL’s adoption dates for different types of banks have changed several times since CECL was approved by the FASB in 2015. When CECL was officially announced in June 2016, public banks were scheduled to adopt CECL in 2020Q1 and private banks were scheduled to adopt CECL in 2021Q4. Please refer to Appendix A.1 for more details on the evolvement of CECL adoption timeline. 44 Private banks can first observe how public banks adopt CECL and then decide what to do to mitigate the negative impact of CECL. Since it is unclear whether public or private banks will respond more strongly during the 2016-2018 transition period examined in this study, I state the hypotheses in a non-directional form: H5a: CECL’s negative impact on loan growth varies between public and private banks. H5b: CECL’s negative impact on growth in longer-term loans varies between public and private banks. H5c: CECL’s negative impact on growth in real estate loans varies between public and private banks. It’s also hard to say whether the more severe agency problems of public banks lead them to be more impacted by CECL. At first glance, compared with private banks, public banks may be more affected by CECL because they have worse agency problems, leading to a higher cost of raising new capital when they are capital constrained. However, to mitigate the these agency problems, public banks often utilize stricter monitoring mechanisms. Public banks are subject to SEC’s monitoring on their financial reports and must comply with stricter regulations (e.g., SOX). Consistent with public banks engaging in stricter monitoring, Nichols et al. (2019) find that public banks recognize larger and more timely loan losses. 2.4 Research design 2.4.1 Timeline of the difference-in-differences design Conceptually, this study examines whether banks that are closer to their capital adequacy constraints are more likely to change their lending strategy after CECL was approved. To test my hypotheses, I adopt a difference-in-differences design. For the research design timeline, I use two 45 12-quarter windows surrounding the time when CECL was approved on November 18, 2015.48 Since this is close to the end of 2015Q4 and banks are unlikely to change their lending strategy this quarter, I set 2013Q1−2015Q4 as the benchmark period and 2016Q1−2018Q4 as the post-treatment period (i.e., the CECL transition period). The implementation of Basel III’s new capital adequacy rules in the U.S. is a contaminating event that overlaps with the first two years of my benchmark period. Recall that Basel III was agreed upon by the country members of the Basel Committee in November 2010.49 While Basel III bank capital regulations were finalized in 2013 in the U.S., the rules did not become effective until January 2014 for larger banks and in January 2015 for smaller banks (see section 2.3.4). I assume that U.S. banks would have begun their Basel III preparation in 2010 and would have fully responded to the new rules by 2014Q4 before CECL was approved. The banking industry’s transition, during 2013 and 2014, toward full adoption of the U.S. Basel III rules is expected to bias against my findings. The rules introduced stricter capital ratio requirements, encouraging banks to both increase their level of regulatory capital and decrease their holding of risk-weighted assets. As a result, banks would become less willing to grow their loan assets during my benchmark period. In turn, this lowers the benchmark used for measuring the extent of reduction in loan growth during the CECL transition period, reducing the chance that I find statistically significant results. 48 See https://www.fasb.org/jsp/FASB/Document_C/DocumentPage&cid=1176167627008 for the meeting minutes of the FASB’s approval on November 18, 2015 in page 3. 49 Basel III introduced two new regulatory capital ratios (i.e., the Common Equity Tier 1 capital ratio and the supplementary leverage ratio), and it also specified minimum levels of capital adequacy for each of the five regulatory capital ratios. 46 In order to avoid overlapping with the Basel III implementation period and the CECL transition period, I measure each bank’s degree of capital adequacy constraint based on their regulatory capital ratios during 2015. This measure will be discussed more fully in the next section. The CECL transition period stops at 2018Q4 because, on December 21, 2018, the banking regulators changed the CECL implementation date for regulatory capital. They approved a rule giving banks an option to take three years to phase in CECL’s day-1 adverse effects on regulatory capital.50 A limitation of this study is that I cannot include the period right before CECL was required to be adopted. For a complete history of how the adoption of CECL has evolved since FASB approved it in November 2015, please refer to Appendix A.1. The timeline of my research design is summarized in the figure below: Figure 2.5 Timeline of my research design 50 See the OCC website on https://www.occ.treas.gov/news-issuances/news-releases/2018/nr-ia-2018-142.html for more details on CECL and regulatory capital. 47 2.4.2 CECL’s impact on bank lending (H1, H2, and H3) Hypotheses H1, H2, and H3 examine CECL’s effect on bank lending through regulatory capital from three perspectives. H1 focuses on the growth rate of total loans; H2 focuses on the growth rate of longer-term loans; H3 focuses on the growth rate of three main loan types, i.e., residential loans, commercial loans, and consumer loans. I use the following model to test these three hypotheses: Yi,tk = βk CLOSE_TO_CONS_ALLi × POSTt + CONTROLSi,t−1 + Bank Fixed Effects +Year-quarter Fixed Effects (1) where the subscripts i and t denote the bank and quarter, respectively.51 The superscript k ∈ {H1, H2, H3} and denotes which hypothesis the equation refers to. Yi,tH1 equals ∆LOANSi,t, which indicates the change in total loans during the quarter scaled by total loans at the start of the quarter. Yi,tH2 equals ∆LONG_TERM_LOANSi,t, which denotes the change in long-term loans during the quarter scaled by long-term loans at the beginning of the quarter. Long-term loans are defined as loans with a remaining life greater than five years. Yi,tH3 denotes changes in different types of loans, scaled by their beginning of the quarter balance. The two types of real estate loans are combined into a single variable, total real estate loans (∆TOT_RE_LOANSi,t), which includes residential and commercial real estate loans52. Regressions are also estimated for the change in residential real estate loans (∆RES_RE_LOANSi,t) and commercial real estate loans (∆COM_RE_LOANSi,t) separately, along with the change in commercial non-real estate loans (∆COM_NRE_LOANSi,t), and 51 All of the analysis in this study is performed at the bank holding company level. 52 Total real estate loans include all bank real estate loans except for land development and construction loans. These loans are not analyzed in this study. 48 consumer loans (∆CONSUMER_LOANSi,t). These four types of loans make up approximately 85% of the total loans of the banks in my sample. The ending balances of different loan types are obtained from the Schedule HC-C in the FR Y-9C reports. Commercial non-real estate loans include commercial and industrial loans to both U.S. addressees and non-U.S. addressees. Consumer loans consist of non-real-estate loans to individuals, including credit cards, other revolving credit plans, automobile loans, other loans to individuals, and leases to individuals. 2.4.2.1 Main independent variable The main independent variable CLOSE_TO_CONS_ALLi measures how constrained a bank’s regulatory capital is in the benchmark period, i.e., before CECL was announced in 2015Q4.53 As mentioned in section 2.3.5, under U.S. Basel III, banks must at all times comply with the five different minimum regulatory capital requirements. CLOSE_TO_CONS_ALLi measures the most restrictive of four of these regulatory capital requirements: the common equity Tier 1 capital ratio, the Tier 1 capital ratio, the total risk-based capital ratio, and the leverage ratio. The fifth regulatory capital ratio (the supplementary leverage ratio) is only reported by large, internationally active banks (see section 2.3). To calculate CLOSE_TO_CONS_ALLi,t for each quarter during 2015, I first incorporate the four different regulatory capital ratios into a single metric that measures the status of a bank’s most restrictive regulatory capital ratio. CLOSE_TO_CONS_ALLi,t is calculated as: 53 I only use data from before CECL’s approval in 2015Q4 because the status of bank regulatory capital can be influenced by the changes in bank lending strategy during the CECL transition period. I use 2015 quarters only because Basel III regulatory capital minimums were not required for all U.S. banks until January 2015. 49 CLOSE_TO_CONS_ALLi,t = Max � 4.5%Common equity Tier 1 capital ratioi,t , 6%Tier 1 capital ratioi,t, 8%Total risk-based capital ratioi,t , 4%leverage ratioi,t� where the restrictiveness of each regulatory capital ratio is measured by the ratio of its minimum acceptable level under U.S. Basel III divided by the bank’s actual regulatory capital level. This ratio is equal to one when the bank’s capital ratio is exactly equal to the minimum set by Basel III. If the ratio is less than one, this implies that the bank has excess regulatory capital. A bank that is in breach of the restriction will have a ratio that is greater than one54. By finding the maximum of the four ratios, CLOSE_TO_CONS_ALLi,t considers the type of capital ratio with the greatest closeness to the regulatory minimum and uses it to measure a bank’s overall risk of breaching its regulatory capital requirements. The total risk-based capital ratio is by far the most restrictive of the four capital adequacy ratios examined by this study. It is the most restrictive of the four ratios 93.3% of the time. The leverage ratio is most restrictive 3.2% of the time, while the common equity Tier 1 and Tier 1 capital ratios are most restrictive only 1.4% and 2.1% of the time. I calculate CLOSE_TO_CONS_ALLi for each bank by taking the average of CLOSE_TO_CONS_ALLi,t over the four quarters from 2015Q1 to 2015Q4. I use the average 54 Another way to interpret the ratio of the minimum constraint over the actual regulatory capital ratio is to consider its inverse. Suppose 6%Tier 1 capital ratio = 0.8. Its inverse, Tier 1 capital ratio6% , can be viewed as the distance to default in terms of the constraint 6%. The 0.8’s inverse 1/0.8 = 1.25, meaning that the bank’s current Tier 1 capital ratio 1.25 times of the regulatory minimum. As this inverse gets closer to one from above, the probability of violating the capital covenant becomes greater. I don’t use the inverse in this study because I want to the main variable of interest to capture the level of closeness to capital adequacy constraints, which is more suitable to my research question. 50 during 2015 rather than measuring closeness at the end of 2015Q4 in order to avoid picking up the temporary seasonal fluctuations of capital status. For robustness, I also consider just one capital constraint, the ratio of 6% to the bank’s Tier 1 capital ratio (CLOSE_TO_CONS_T1i), as an alternative measure of closeness to the capital adequacy constraint. I examine this measure for two reasons. First, CLOSE_TO_CONS_ALLi is almost always based on the restrictiveness of the total risk-based capital ratio, and that ratio allows banks to add their loan loss allowance back to the capital, up to a maximum of 1.25% of risk-weighted assets. Therefore, CECL’s impact on capital constraints is likely to be nullified muted in the case of the total risk-based capital ratio. Thus, it is important to repeat the analysis using the Tier 1 capital ratio to see whether the results still hold. The second reason for using the Tier 1 capital ratio is for comparability with other studies, which most often rely on this measure of the regulatory capital ratio. In addition to measuring the capital constraints in a continuous fashion, I also measure banks’ closeness to their regulatory capital constraints using two dichotomous variables. Specifically, I create MORE_CONSTRAIN_ALLi, which is a dummy variable set equal to one when CLOSE_TO_CONS_ALLi is above its median, and I create MORE_CONSTRAIN_T1i as a dummy variable set equal to one when CLOSE_TO_CONS_T1i is above its median. POSTt is an indicator variable set to one for the CECL transition period (between 2016Q1 and 2018Q4). The coefficient on CLOSE_TO_CONS_ALLi × POSTt (βk) endeavors to capture the effect of CECL on bank lending during the transition period. If the βk is significantly less than zero, then the empirical finding is consistent with H1, H2, or H3. CLOSE_TO_CONS_ALLi and POSTt are excluded in Eq. (1) because their coefficients are subsumed by bank fixed effects and year-quarter fixed effects, respectively. 51 2.4.2.2 Control variables and fixed effects Following prior literature (e.g., Beatty and Liao 2011; Wheeler 2019), Eq. (1) includes a battery of bank-quarter level control variables (CONTROLSi,t−1). All control variables are measured for bank i at the end of quarter t-1 (or, equivalently, at the beginning of quarter t). In particular, I control for (1) bank size (SIZEi,t-1 measured as log value of total assets), (2) portfolio composition of assets (RES_RE_LOANSi,t-1, COM_RE_LOANSi,t-1, COM_NRE_LOANSi,t-1, CONSUMER_LOANSi,t-1, LONG_TERM_LOANSi,t-1, TRADING_ASSETSi,t-1; all six variables are scaled by total assets), (3) funding structure (DEPOSITSi.t-1, scaled by total liabilities), (4) liquidity (CASH&MKTSECi,t-1, scaled by total assets), (5) loan quality (NPLi,t-1, calculated as non-performing loans scaled by total assets), and (6) profitability (ROAi,t-1, calculated as net income before discontinued operations scaled by average total assets). See Appendix A.2 for detailed definitions of all the variables included in Eq. (1). Eq. (1) also controls for bank fixed effects, so the identification of the main parameter βk comes from comparing the bank lending strategies for the same bank before and after CECL’s approval date. In other words, Eq. (1) uses each bank as its own control to adjust for all determinants of the lending strategies that tend to be stable over the entire sample period, including business complexities, operating cost structures, the strictness of different banking regulators, and corporate governance structures. Eq. (1) also controls for year-quarter fixed effects, so the identification of the main parameters excludes the factors that affect all sample banks equally in each year-quarter. Such factors would include changes in Federal funds rates, monetary policy, national GDP growth, the national unemployment rate in each quarter, etc. All standard errors are two-way clustered at the bank and year-quarter levels. 52 2.4.3 Cross-sectional analysis (H4 and H5) 2.4.3.1 Regulatory leniency (H4) H4a, H4b, and H4c predict that CECL’s impact on bank lending through the capital channel is stronger when the banking regulators are more lenient. To test these hypotheses, I rely on the regulator leniency index developed by Agarwal et al. (2014) 55 to identify banks with a more lenient state regulator. As discussed earlier, Agarwal et al. (2014)’s measure of regulator leniency utilizes a data set from 1996 to 2010 containing the results of all on-site examinations conducted by U.S. banking regulators. They measure state-level regulatory leniency by calculating the average difference in the supervisory rating given to the bank by its state regulator versus the one given to the same bank by its federal regulator. Given that state regulators are found to be more lenient than federal regulators on average, I interpret banks in states with larger rating differences between state and federal regulators as subject to lower regulatory scrutiny relative to states with smaller differences between the two. Agarwal et al. (2014)’s regulatory leniency index is assigned to each bank depending on the leniency of the state regulator where the bank is located. This variable is denoted by REG_LENi. Since the leniency index is only defined for state-chartered banks, I follow Costello et al. (2019) by assigning a nonzero value when bank i is regulated by a state authority and a value of zero when bank i is solely regulated by federal regulators. The banks that are solely regulated by federal regulators (i.e., federally chartered banks) are included in the analysis in an effort to benchmark the average strategic response to CECL of the state-chartered banks with similar characteristics. 55 The regulatory leniency index is available at Amit Seru’s website, https://aseru.people.stanford.edu/. 53 For H4, I conduct two sets of analyses. For the first set, I split the sample at the median, based on REG_LENi, into two equal-sized groups. Banks with a more lenient regulator are denoted by HIGH_REG_LENi = 1, and those with a less lenient regulator are denoted by HIGH_REG_LENi = 0. Then I estimate the coefficients of Eq. (1) using the two subsamples. If the coefficient on CLOSE_TO_CONS_ALLi × POSTt is significantly smaller for the more lenient subsample than for the less lenient subsample, then the results would be consistent with H4a, H4b, H4c. Such a result would be consistent with regulatory leniency strengthening CECL’s anticipatory impact on bank lending through the capital channel. The second set of analyses directly relies on REG_LENi as a continuous measure of regulatory leniency to test H4a, H4b, and H4c. I estimate the following regression: Yi,tk = βkCLOSE_TO_CONS_ALLi × POSTt × REG_LENi + γ CLOSE_TO_CONS_ALLi × POSTt +δ REG_LENi × POSTt + CONTROLSi,t−1 + Bank Fixed Effects +Year-quarter Fixed Effects (2) where the subscripts i and t denote the bank and quarter, respectively. The superscript k ∈ {H4a, H4b, H4c} and denotes which hypothesis the equation refers to. Yi,tH4a equals ∆LOANSi,t. Yi,tH4b equals ∆LONG_TERM_LOANSi,t. Yi,tH4c equals ∆TOT_RE_LOANSi,t. Other variables are defined previously. See Appendix A.2 for detailed definitions of all the other variables included in Eq. (2). 2.4.3.2 Public banks versus private banks (H5) As discussed previously, H5a, H5b, and H5c state the hypotheses in a non-directional form. These three hypotheses examine whether CECL’s impact on bank lending through the capital channel varies between public and private banks. To test these hypotheses, I split the 54 sample into two subsamples: publicly listed banks and privately held banks. Then I estimate the coefficients of Eq. (1) using the two subsamples. If the coefficients on CLOSE_TO_CONS_ALLi × POSTt estimated using the two subsamples are significantly different, then the empirical finding is consistent with bank ownership structure being an important factor in determining CECL’s impact on bank lending through the capital channel. 2.5 Sample selection process and univariate analysis 2.5.1 Sample selection process The sample selection begins with all U.S. bank holding companies (BHCs) that file the FR Y-9C quarterly regulatory reports between 2013Q1 and 2018Q4. As already mentioned, the sample period stops at 2018Q4 because, in December 2018, the banking regulators approved a rule that gives banks an option to phase in CECL’s day-1 adverse effects on regulatory capital over three years (see section 4.1). I obtain the FR Y-9C data from the Federal Reserve Bank of Chicago website56. Since each Y-9C report is at the BHC level, it contains the consolidated financial report of a parent BHC and all bank subsidiaries under the BHC’s control. The FR Y-9C reports include detailed information on income statement items, balance sheet items, and off-balance sheet activities. LONG_TERM_LOANSi,t-1 (defined as the percentage of a bank’s assets made up of loans with five years or greater remaining life) is not available in the FR Y-9C reports, so I collect them from the Call Report data57 at the bank-subsidiary level. If a BHC has only one bank subsidiary, I merge this variable directly with the FR Y-9C data. However, if a BHC has 56 The FR Y9C data is available at https://www.chicagofed.org/banking/financial-institution-reports/bhc-data. 57 The Call Report data is available at https://www.fdic.gov/foia/ris/. 55 two or more bank subsidiaries, I aggregate the long-term loan amounts and merge the aggregate with the FR Y-9C data. Then I use the aggregate amount of long-term loans at the end of each year-quarter to derive the change in long-term loans (∆LONG_TERM_LOANSi,t). The regulatory leniency index developed by Agarwal et al. (2014) is collected from Amit Seru’s website.58 When a BHC has two or more bank subsidiaries in different states, I merge the state-level regulatory index (REG_LENi) into BHC level data using a weighted average of the subsidiary level regulatory leniency index. The weights are based upon the total assets of each bank subsidiary. Following Costello et al. (2019), I assign the federal-chartered banks a zero value as they are solely regulated by the federal regulator, and so their difference is zero. The final step of the sample selection process is to exclude all BHCs with at least one of the following characteristics: (1) they do not own an FDIC-insured bank subsidiary, (2) their loans-to-assets ratio is below 10 percent (inactive in the loan market), (3) they have zero or negative total deposits, (4) they have asset growth greater than 25% (this could indicate they have been involved in a merger), (5) they have less than ten quarters of observations either before or after 2015Q4, which was the time when CECL was approved (to ensure a balanced panel), or (6) they have one or more missing regression variables. To reduce the influence of outliers, I winsorize all continuous variables at the 1% and the 99% levels. Variable definitions are provided in Appendix A.2. Table 2.1 Panel A summarizes the sample selection process for the FR Y-9C sample, resulting in a final sample of 7,073 bank-quarter observations by 313 unique banks. Table 2.1 Panel B shows the number of banks in each quarter. Table 2.1 Panel B shows that during the 58 Amit Seru’s website is available at https://aseru.people.stanford.edu/. 56 period from 2013Q1 to 2018Q2, the number of unique banks is around 305 per quarter. However, starting from 2018Q3, the number of banks drops by 42% from 306 to 178 because the Federal Reserve increased the asset-size threshold for filing the Y-9C from $1 billion to $3 billion, effective in September 2018. Table 2.1 Panel C shows the numbers of the large, medium, and small banks in my sample. Small banks with assets less than $10 Billion make up 85% of my sample (5,978 out of 7,073 bank-quarters). Medium banks with assets between $10 and $50 Billion make up 9% of my sample, and large banks with assets greater than $50 Billion make up 6% of my sample. To identify the public banks in my sample (for H5), I use the CRSP-FRB link file from the Federal Reserve Bank of New York’s website59. Table 2.1 Panel D shows the numbers of public and private banks in my sample. Just over fifty percent of my sample are publicly traded banks, and the rest are privately held banks. In my sample, leniently regulated banks are not all privately held. Neither all strictly regulated banks are publicly listed. Table 2.1 Panel E shows that in my sample, there are 3,471 leniently regulated banks (i.e., banks with REG_LENi higher than the sample median) and 3,602 strictly regulated banks (i.e., banks with REG_LENi lower than the sample median). Among the 3,471 leniently regulated banks, 1,749 (i.e., 50.4%) of them are privately held. Moreover, only 2,004 of the 3,602 (i.e., 55.6%) strictly regulated banks are publicly listed. 59 The CRSP-FRB link file is available at https://newyorkfed.org/research/banking research/datasets.html. 57 2.5.2 Descriptive statistics Table 2.2 Panel A reports the descriptive statistics of key variables used in my analysis. On average, loans60 account for 66% of the total assets of the banks in my sample. Loans consist of residential real estate loans (accounting for 20% of assets); commercial real estate loans (accounting for 20% of assets); commercial non-real estate loans (accounting for 13% of assets); and consumer loans (accounting for 2.9% of assets). Other loans account for the remaining 9.7% of assets61. Across the entire sample period, the average quarter-to-quarter growth rate in loans (∆LOANSi,t) is 2.0%, consistent with prior studies (e.g., Beatty and Liao 2014; Wheeler 2020). The quarter-to-quarter growth rate of loans with a remaining life greater than five years (∆LONG_TERM_LOANSi,t) has a mean of 2.7%. Commercial non-real estate loans grew at a faster rate than total real estate loans and consumer loans during the period of my study. The average quarter-to-quarter growth in commercial non-real estate loans (∆COM_NRE_LOANSi,t) is 2.6%; the quarter-to-quarter growth rates in residential and commercial real estate loans (∆RES_RE_LOANSi,t-1, and ∆COM_RE_LOANSi,t-1) are 1.6% and 2.1%, respectively, on average. The average growth rate in consumer loans (∆CONSUMER_LOANSi,t) is 1.4% during the sample period. For the main independent variables, banks’ closeness to their Tier 1 capital adequacy constraints (CLOSE_TO_CONS_T1i) has a mean (median) of 0.462(0.469)62. A greater closeness measure represents a higher risk of violating the Tier 1 capital ratio requirement. 60 Total loans include both loans and lease financing receivables. 61 Other loans include construction, land development, and other land loans; loans to depository institutions and acceptances of other banks; loans to non-depository financial institutions; and loans to foreign governments, and official institutions (including foreign central banks) in the Y9C report. 62 As previously mentioned, I winsorize CLOSE_TO_CONS_T1i at 1% and 99% levels. Before being winsorized, CLOSE_TO_CONS_T1i has a maximum of 0.942 (untabulated). 58 Alternatively, one minus the closeness can be viewed as the distance to default on the Tier 1 ratio covenant. Because 1 − 6%Tier 1 capital ratio = Tier 1 capital ratio − 6%Tier 1 capital ratio , the numerator can be thought of as the difference between the bank’s Tier 1 ratio and the minimum requirement. Thus, 1 −6%Tier 1 capital ratio can be interpreted as the margin of safety or the % by which the current Tier 1 capital ratio can decrease before reaching its minimum regulatory requirement. The 0.462 mean of CLOSE_TO_CONS_T1i shows that an average bank can afford a 53.8% (i.e., 1 – 46.2%) decline in its current Tier 1 capital ratio before violating the Tier 1 ratio covenant. As discussed in section 4.2, CLOSE_TO_CONS_ALLi63 incorporates the four capital ratio types (i.e., the common equity Tier 1 capital ratio, the Tier 1 capital ratio, the total risk-based capital ratio, and the leverage ratio) into one metric that measures how close a bank is to its most restrictive capital adequacy constraints, where it would be subject to prompt corrective action by its regulators. CLOSE_TO_CONS_ALLi has a mean (median) of 0.559 (0.564). As in the last paragraph, a mean 0.559 for CLOSE_TO_CONS_ALL indicates that the most restrictive capital ratio can afford a 44.1% (i.e., 1 – 55.9%) decline before reaching its regulatory minimum. Table 2.2 panel B presents the sample descriptive statistics during the periods before and after FASB approved CECL in 2015Q4. On average, banks hold a larger portion of their assets as loans after CECL was approved than during the benchmark period. The average percentage of loans over total assets increases from 64.5% to 68.0% during the CECL transition period. Another interesting aspect of changes during the CECL transition period is the changes in the 63 As previously mentioned, I winsorize CLOSE_TO_CONS_ALLi at 1% and 99% levels. Before being winsorized, CLOSE_TO_CONS_ALLi has a maximum of 1.186 (untabulated), indicating that there are some banks that violated their capital ratio requirement during 2015Q1 and 2015Q4. I further check my sample and discover that greater-than-one maximum is due to one bank (i.e., CIB MARINE BANCSHARES, INC.). Excluding this bank from the sample does not change the inferences from my analysis. 59 capital ratios. The average of total equity over assets (EQUITY_TOTAL_ASSETSi,t) increases from 10.5% to 10.8%. However, the three risk-based regulatory capital ratios decrease. The average Tier 1 capital ratio (T1_CAP_RATIOi,t) decreases from 14.0% to 13.5%; the average total risk-based capital ratio (TOTAL_CAPITAL_RATIOi,t) decreases from 15.4% to 14.9%; and the average common equity Tier 1 capital ratio (CET1_CAP_RATIOi,t)64 decreases from 12.6% to 12.5%. The average bank does not appear to add new regulatory capital in anticipation of CECL. 2.5.3 Correlations Table 2.2 panel C reports the correlation matrix of the regression variables for descriptive purposes. The pair-wise correlations among the regression variables do not validate or reject the hypotheses because the identification in my regression models relies on difference-in-differences and fixed effects. Not surprisingly, the two measures of regulatory capital constraints, CLOSE_TO_CONS_T1i and CLOSE_TO_CONS_ALLi, are significantly and highly correlated, implying that these two variables capture similar economic phenomena. However, their high correlation does not necessarily imply that the two variables will produce qualitatively similar results in the regression analysis because the correlation is just univariate. CLOSE_TO_CONS_T1i and CLOSE_TO_CONS_ALLi can have different economic implications after controlling for bank characteristics, bank fixed effects, and year-quarter fixed effects. 64 The number of observations for CET1_CAP_RATIOi,t is less than the numbers for other variables because it is only available for all banks after U.S. Basel III rules becomes effective in 2015Q1. 60 For the main dependent variables, ∆LOANSi,t is positively correlated with CLOSE_TO_CONS_ALLi,t, implying that banks that are closer to their capital adequacy constraints tend to have higher total loan growth across the entire sample period. ∆TOT_RE_LOANSi,t and ∆COM_NRE_LOANSi,t are positively correlated with CLOSE_TO_CONS_ALLi, suggesting that more constrained banks had higher growth in their long-term real estate and commercial loans during the entire sample period. 2.5.4 Difference-in-differences univariate analysis Table 2.3 reports the difference-in-difference univariate analysis for the FR Y-9C sample. Panel A reports the descriptive statistics for banks that are more versus less capital-constrained separately for the benchmark period and the CECL transition period. Capital-constrained is determined based on a bank’s average CLOSE_TO_CONS_ALL during the four quarters of 2015.65 More (less) capital-constrained banks are the banks with average CLOSE_TO_CONS_ALLi in 2015 above (below) the median for all banks. In Panel A, during the benchmark period, the average growth in total loans (∆LOANSi,t) is 2.45% (see column A1) for more capital-constrained banks and 1.66% (see column B1) for less capital-constrained banks, and the difference in these means is significant. It’s consistent with the notion that more capital-constrained banks are more aggressive in their lending policy. During the CECL transition period, the average ∆LOANSi,t difference between the two bank groups narrows to only 0.02% (specifically, 1.98% for more capital-constrained banks in column C1, and 1.96% for other banks), and it’s no longer significantly different. The trend we observed 65 Note that the capital-constrained is measured for each bank before CECL was announced, but after Basel III became effective for all banks. Recall that the final set of banks were required to adopt Basel III by January 1, 2015. 61 is consistent with CECL negatively affecting more capital-constrained banks to a greater extent. If the change to CECL is expected to result in a more precise measure of regulatory capital this prospect may be motivating the more capital-constrained banks to be less aggressive in lending. However, it could also reflect mean reversion in loan growth rates. The average growth in loans secured by real estate (i.e., total real estate loans, residential real estate loans, and commercial real estate loans) also show a similar trend to the growth of total loans. In the benchmark period, the more capital-constrained banks were growing their real estate loans at a significantly faster rate, but in the CECL transition period, the two groups of banks came closer together in their real estate loan growth rates. Interestingly, the growth rate for long-term loans (∆LONG_TERM_LOANSi,t) declined from the benchmark period to the CECL transition period for both the more and less capital-constrained banks. If this is due to changes in interest rates during the transition period, the year-quarter fixed effects in the regressions should help to control for this. To analyze the differences in covariate balances between more capital-constrained banks and other banks, I follow Imbens and Wooldridge (2009), who compute the normalized differences for each variable separately in the pre- and post-periods. Normalized differences greater than 0.25 can result in specification sensitivity in the linear regression model.66 The normalized difference column is calculated as (X�MORE − X�LESS) �SMORE2 − SLESS2⁄ where X�MORE and SMORE2 (X�LESS and SLESS2 ) are the sample means and sample variances for more (less) capital-constrained banks. Among all the required variables in the regression, only the two normalized 66 This method is also used in other published accounting papers (e.g., Nicoletti 2018; Iselin and Nicoletti 2017). 62 differences of LOANSi,t-1 and COM_NRE_LOANSi,t-1 are above the recommended 0.25 threshold, suggesting that more and less capital-constrained banks are comparable. Table 2.3 Panel B reports the difference-in-differences of the regression variables. The loan growth rate (∆LOANSi,t) for the more capital-constrained banks decreases more than for the less capital-constrained banks after the FASB approved CECL, consistent with more capital-constrained banks, during the CECL transition period, issuing fewer loans as a response to the anticipated CECL capital shock. The change in the growth rate of total real estate loans (∆TOT_RE_LOANSi,t) is significantly less for the more capital-constrained banks than the less constrained banks, consistent with banks that are closer to their capital adequacy constraints originating fewer real estate loans during the CECL transition period. However, these findings are only at the univariate level, and controlling for other determinants of lending is required for further inferences. 2.6 Main results 2.6.1 CECL and bank lending (H1) H1 predicts that banks that are closer to their capital adequacy constraints originate fewer loans after CECL was approved, compared to other banks that are not as close to their capital adequacy constraints. Table 2.4 presents the results of estimating Eq. (1) for H1. The dependent variable is the quarterly loan growth rate (∆LOANSi,t). Columns [1] and [2] show the results when closeness to the capital adequacy constraints is measured using the most restrictive of the four types of regulatory capital ratios. Columns [3] and [4] show the results when closeness is measured using only the Tier 1 capital ratio. In columns [1] and [3], I use the continuous specifications of closeness to constraints, while in columns [2] and [4], I use the binary specifications (i.e., whether the corresponding version of closeness is greater than its median). 63 The main coefficient of interest is POSTt interacted with one of the capital adequacy constraint measures (CLOSE_TO_CONS_ALLi, MORE_CONSTRAIN_ALLi, CLOSE_TO_CONS_T1i, and MORE_CONSTRAIN_T1i). All coefficients on the four interaction terms are negative and significant, consistent with H1. The main coefficients of interest are also economically significant. For example, in column [2], the coefficient on MORE_CONSTRAIN_ALLi × POSTt (i.e., -0.421 percentage points) is equivalent to 20.93% of average total loan growth.67 For the control variables in Table 2.4, the coefficients on the log value of total assets (SIZEi,t-1) are significant and negative, suggesting that larger banks experienced less loan growth. The coefficients on RES_RE_LOANSi,t-1 (residential real estate loans), COM_RE_LOANSi,t-1 (commercial real estate loans), and COM_NRE_LOANSi,t-1, are also significant and negative. This finding suggests that banks that held a larger fraction of their assets in these three types of loans at the beginning of a quarter were less likely to grow their loan portfolio. The coefficients on non-performing loans (NPLi,t-1) are significant and negative. This finding suggests that banks experiencing greater difficulty collecting their loans grew their loan portfolios more slowly, consistent with the capital crunch theory. 2.6.2 CECL and long-term loans (H2) H2 predicts that banks that are closer to their capital adequacy constraints hold fewer long-term loans after CECL was approved, compared to other banks that are not as close to their capital adequacy constraints. Table 2.5 presents the results of estimating Eq. (1) for H2. The dependent variable is the quarter-to-quarter growth rate of loans with five years or more of 67 The 20.93% decrease is calculated by dividing the coefficient estimate (-0.421%) by the average loan growth rate in the sample which is 2.011%. 64 remaining life (∆LONG_TERM_LOANSi,t). As in the previous table, Columns [1] and [2] show the results when closeness to the capital adequacy constraints is measured using the most restrictive of the four types of regulatory capital ratios. Columns [3] and [4] show the results when closeness is measured using only the Tier 1 capital ratio. Both continuous and binary specifications of the closeness to constraints measures are used. All the interaction terms between closeness to capital adequacy constraints and the dummy variable for the post period in the four columns are insignificant. These results fail to support H2 that more capital-constrained banks reduce their holding of longer-term loans during the CECL transition period.68 For the control variables in Table 2.5, the coefficients on residential real estate loans (RES_RE_LOANSi,t-1) and commercial real estate loans (COM_RE_LOANSi,t-1) are significant and positive, consistent with these loans, on average, having the highest loan maturity. The coefficients on TRADING_ASSETSi,t-1 are also significant and negative, suggesting that banks that issue more longer-term loans hold fewer trading assets. 2.6.3 CECL and loan types (H3) H3 predicts that banks that are closer to their capital adequacy constraints issue fewer total real estate loans after CECL was approved, compared to other banks that are not as close to their capital adequacy constraints. Table 2.6 presents the results of estimating Eq. (1) for H3. The dependent variables are (a) the growth rate of total real estate loans (∆TOT_RE_LOANSi,t), (b) the growth rate of commercial non-real estate loans (∆COM_NRE_LOANS), and (c) the growth 68 I have repeated this analysis using alternative definitions of long-term loans (such as loans with remaining life greater than 15 years), and the interaction coefficients are never significant. These results imply that during the CECL transition period, more capital constrained banks were not likely to reduce the amount of longer-term loans that they held. 65 rate of consumer loans (∆CONSUMER_LOANS) in columns [1], [2], and [3], respectively. Columns [4] and [5] show the results when I set the dependent variables as the growth rate of the two components of total real estate loans, residential real estate loans (∆RES_RE_LOANSi,t) and commercial real estate loans (∆COM_RE_LOANSi,t). For brevity, I only present the results when the closeness to the capital adequacy constraints is measured by the most restrictive of the four regulatory capital ratio types, transformed into a dummy variable indicating whether the closeness is greater than its median. The results are qualitatively similar when I use the other three specifications of closeness. In column [1], the coefficient on the interaction term, MORE_CONSTRAIN_ALLi × POSTt, is negative and significant, while in columns [2] and [3], the interaction term coefficients are insignificantly different from zero. This empirical finding supports H3. Specifically, banks that are closer to their capital constraints are more likely to reduce their real estate loan supply in anticipation of CECL’s negative shock to regulatory capital. The finding is also economically significant. In column [1], the coefficient on MORE_CONSTRAIN_ALLi × POSTt (i.e., -0.428 percentage points) is equivalent to a 23.75% decrease in the average growth rate of total real estate loans.69 Total real estate loans consist of loans secured by residential properties and loans secured by commercial properties. These two types of loans might exhibit different risk profiles. Therefore, banks may reformulate their lending strategies differently on these two different types of loans in anticipation of CECL. Thus, I further check whether the growth rates of these two 69 The 23.75% decrease is obtained from dividing the coefficient estimate (0.428%) by the average growth rate of long-term real estate loans, 1.802%. 66 loan types are affected differently during the CECL transition period. Columns [4] and [5] show the results when I set the dependent variables as the growth in residential real estate loans (∆RES_RE_LOANSi,t) and commercial real estate loans (∆COM_RE_LOANSi,t). The results show that more capital-constrained banks reduce their growth only in the real estate loans secured by residential properties. For the control variables in Table 2.6, the coefficients on bank size (SIZEi,t-1) are significant and negative in all regressions, consistent with larger banks being less likely to expand the supply of each type of loan. The coefficients on residential real estate loans (RES_RE_LOANSi,t-1) and commercial real estate loans (COM_RE_LOANSi,t-1) are significant and negative in columns [1], [4], and [5], consistent with the mean reversion of loan portfolio allocation. The coefficients on non-performing loans (NPLi,t-1) are significant and negative in all regressions, consistent with banks with higher non-performing loans experiencing slower growth in their loan portfolios. 2.6.4 Cross-sectional analyses (H4 and H5) 2.6.4.1 Regulatory leniency (H4) H4 predicts that CECL’s impact on bank lending through the regulatory capital constraints is stronger when the banking regulators are more lenient. As discussed previously, I rely on the Agarwal et al. (2014) index to identify the degree of regulatory leniency. Table 2.7 Panel A presents the results of estimating Eq. (1) on the two subsamples: banks with higher-than-median regulatory leniency and banks with lower-than-median regulatory leniency. Table 2.7 Panel B reports a test of whether the coefficient on the variable of interest is significantly different between the two subsamples. 67 The first two columns of Table 2.7 estimate Eq. (1) separately for the more leniently and less leniently regulated subsamples with loan growth rate (∆LOANSi,t) as the dependent variable. Only the more leniently regulated subsample shows a significantly lower loan growth for more capital-constrained banks during the CECL transition period. Panel B shows that the interaction term (MORE_CONSTRAIN_ALLi × POSTt) in Column [1] is marginally significantly smaller than the one in column [2] (coef. diff. = -0.508, one-tailed p-value = 0.06), consistent with more leniently regulated banks reducing their loan growth rates (H4a). Table 2.7 Columns [3] and [4] report the subsample results for H2 when the dependent variable is loans with remaining life greater than five years (∆LONG_TERM_LOANSi,t). The more and the less leniently regulated subsamples both show an insignificantly lower growth rate of longer-term loans for the more capital-constrained banks during the CECL transition period. Panel B shows that the interaction term (MORE_CONSTRAIN_ALLi × POSTt) in Column [3] is significantly smaller than the one in column [4] (coef diff = -1.954 one-tailed p-value = 0.04), consistent with H4b. The final two columns of Table 2.7 present the results when the dependent variable is the growth rate of total real estate loans (∆TOT_RE_LOANSi,t) for the two subsamples. The interaction term (MORE_CONSTRAIN_ALLi × POSTt) is only significantly negative for the more leniently regulated banks, and the term for the more leniently regulated banks is significantly smaller than the term for the less leniently regulated banks (coef diff = -0.929, one-tailed p-value = 0.01), consistent with H4c. For robustness, I check whether the above findings still hold if I use the continuous measure of Agarwal et al. regulatory leniency rather than a binary measure created using the median of the leniency index. Table 2.8 presents the H4 results of estimating Eq. (2), which 68 includes the leniency index on the right-hand side of the regression model. Columns [1], [2], and [3] show the coefficient estimates for Eq. (2). The dependent variables are loan growth rate (∆LOANSi,t), long-term loan growth (∆LONG_TERM_LOANSi,t), and the growth rate of total real estate loans (∆TOT_RE_LOANS). The coefficients of interest (MORE_CONSTRAIN_ALLi × REG_LENi × POSTt) in columns [1], [2], and [3] are significantly negative, consistent with H4a, H4b, and H4c. 2.6.4.2 Public banks versus private banks (H5) H5 recognizes that CECL’s impact on bank lending through regulatory capital can vary between public and private banks. Table 2.9 Panel A presents the results of estimating Eq. (1) separately for the two subsamples: public banks and private banks. Table 2.9 Panel B tests whether the coefficient on the variable of interest (MORE_CONSTRAIN_ALLi × POSTt) is significantly different between public and private banks. The first two columns of Table 2.9 present the public and private subsample results for H1 when the dependent variable is loan growth rate (∆LOANSi,t). Both public and private capital-constrained banks reduce their loan growth in the CECL transition period. Panel B shows the reduction in loan growth by capital-constrained public banks is not significantly different from that of private banks in column [2]. Table 2.9 Columns [3] and [4] report the public and private subsample results for H2 when the dependent variable is growth in loans with a remaining life of greater than five years (∆LONG_TERM_LOANSi,t). The interaction term (MORE_CONSTRAIN_ALLi × POSTt) is only significantly negative for public banks, and it is significantly smaller for public banks relative to private banks (coef diff = -2.969, two-tailed p-value = 0.01), consistent with H5b. 69 The last two columns of Table 2.9 present the results when the dependent variable is the growth rate of total real estate loans (∆TOT_RE_LOANSi,t) for the two subsamples. In Panel A, when I estimate the regression separately for public and private banks, the reductions in real estate loan growth during the CECL transition period are not significantly different from zero for either public or private capital-constrained banks.70 Panel B shows that the reductions in real estate loan growth for the two bank groups are not significantly different from each other. 2.7 Additional analysis 2.7.1 CECL and small business lending 2.7.1.1 Motivation The main analysis in this study uses bank-level data from the FR Y-9C reports. One caveat of using bank-level data is that we cannot control for the different economic conditions banks face in their branch locations. The results may be biased if the main variable (i.e., the closeness to the capital adequacy constraints) in the regression is correlated with the changes in local economic conditions. Another caveat is that the loan origination amount is not available from the Y-9C bank-level data, so I estimate the loan origination using loan growth rate (i.e., the change in loan balances during the quarter scaled by the start of quarter balances). However, the loan growth rate is also determined by loan repayments, charge-offs, and the sale of loans. To mitigate the two caveats of using bank-level data, I conduct a set of analyses using the Community Reinvestment Act (CRA) small business lending data. The CRA data provides the annual amount of small business loan origination by each bank in each county where it operates 70 In untabulated results, the reductions in both residential and commercial real estate loan growth during the CECL transition period are not significantly different from zero for either public or private capital-constrained banks 70 in the U.S. This enables me to keep local credit demand constant, by comparing the new small business loans originated within the same county-years, across banks affected differently by CECL. Small business loans are important because of the role small businesses play in the U.S. economy. Small businesses account for over 43% of U.S. private, nonfarm gross domestic product (see, e.g., Kobe 2018), and they are responsible for 47.3% of all U.S. employment.71 Bank loans are the primary source of external financing for small businesses (Dou 2020; Granja et al. 2020; Cortés et al. 2020). A reduction in the credit supply to this sector would be detrimental to the growth of small businesses and the role they play in the broader economy. Even though in section 6.3, I find that CECL does not significantly impact the quarterly commercial loan growth rate, I can’t generalize this finding to commercial loans issued to different types of commercial borrowers. Compared to large firms, small firms are riskier and informationally opaque. Prior research emphasizes that small firms lack quantitative data, audited financial statements, and other types of hard and easily transmitted information (see, e.g., Petersen and Rajan 1994, 1995; Berger and Udell 1995; Agarwal, Chang, and Yavas 2012). Consequently, lending to small firms relies more on soft information, such as the lender’s subjective assessment of the borrower’s character and the community‘s economic prospects (Levine et al. 2020). As Stein (2002) argues, soft information (such as the firmness of a borrower’s handshake, the cleanliness of her premises, or her punctuality in meetings) can reveal valuable information about the likelihood of repayment. 71 See the U.S. official small business statistics in 2019, which is available at https://cdn.advocacy.sba.gov/wp-content/uploads/2019/04/23142719/2019-Small-Business-Profiles-US.pdf. 71 2.7.1.2 Data and research design The small business lending CRA data is collected from the Federal Financial Institutions Examination Council.72 Small business loans are defined as commercial loans with a principal amount of less than or equal to $1 million. The CRA data reports the annual volume of small business loans originated by banks that have total assets greater than $1 billion. Each bank’s small business loans are stratified by the county of the loan recipient. As the small business loan volume amounts in the CRA data are reported at the bank subsidiary level, I aggregate this data to the BHC-county-year level. Table 2.10 reports the sample selection process and composition. The sample used for the small business lending analysis includes 256,849 bank-county-year observations with 256 unique banks and 3,084 unique counties for the years 2013 to 2018. Table 2.11 reports the descriptive statistics for the small business lending sample. The average annual amount of small business loans originated per bank-county (SBLi,c,t) is $2.517 million at the bank-county-year level, which is comparable to prior studies (e.g., Dou 2020). I use the following model to examine whether the anticipation of CECL had a negative impact on small business lending between 2016Q1 and 2018Q4: LOGSBLi,c,t = δ CLOSE_TO_CONS_ALLi × POSTt + CONTROLSi,t−1 + Bank Fixed Effects +County-year Fixed Effects (3) where LOG_SBLi,c,t denotes the log value of small business loan origination for bank i in county c during year t. As I do not observe the total amount of small business loans outstanding at the 72 The CRA data is available at https://www.ffiec.gov/cra/default.htm. Also see, e.g., Bord et al. (2018) for a more comprehensive description of the CRA data. 72 bank-county level, I do not measure the small business loan growth rate. Instead, I use the log of loan originations. CLOSE_TO_CONS_ALLi indicates banks’ closeness to their capital adequacy constraints. CONTROLSi,t-1 includes the same set of variables as the one in Eq. (1), except that they are at bank-year level. See Appendix A.2 for detailed definitions. Eq. (3) includes bank fixed effects to control for time-invariant bank-level factors that influence a bank’s supply of small business loans. Eq. (3) also includes county-year fixed effects to control each county’s local economic condition during a particular sample year. To reduce the influence of outliers, I winsorize all continuous variables at the 1% and the 99% levels. All standard errors are two-way clustered at the bank and year-county levels.73 2.7.1.3 Results Table 2.12 presents the results of estimating Eq. (3). Columns [1], [2], [3], and [4] show the results when the closeness to the capital adequacy constraints is measured using the four different regulatory capital ratios used to test H1. In all four regressions, more capital-constrained banks in the post-period reduce the growth rate of their small business lending, consistent with H1. Table 2.13 columns [1] and [2], and Table 2.14 present the tests on whether CECL’s negative impact on small business lending through the capital ratio channel is stronger when banking regulators are more lenient. In Table 2.13 columns [1] and [2], I find that the interaction terms, MORE_CONSTRAIN_ALLi × POSTt in Column [1] is significantly more negative than the one in column [2] (coef diff = -0.235, one-tailed p-value = 0.05), consistent with CECL’s 73 Although I include bank and county-year fixed effects, clustering by bank and county-year is still crucial, as the fixed effects do not fully capture serial correlation in residuals (e.g., they cannot fully capture autoregressive shocks to banks). Since small business lending is a highly localized banking activity, local shocks likely yield correlated residuals across banks within each county-year (Dou 2020). 73 impact on small business lending, through capital constraints, being more pronounced for banks with lenient regulators. In Table 2.14, I check whether the coefficient on MORE_CONSTRAIN_ALLi × POSTt is smaller when the continuous version of the Agarwal et al. regulatory leniency index (REG_LENi) is higher. This analysis uses the same methodology applied in Table 2.8. I find that the three-way interaction term, MORE_CONSTRAIN_ALLi × REG_LENi × POSTt, is not significantly negative. However, the coefficient on MORE_CONSTRAIN_ALLi × POSTt is still significantly negative, consistent with H1. Table 2.13 columns [3] and [4] present the test of whether CELC’s negative impact on small business lending through the regulatory capital channel varies between public and private banks. I find that the coefficient on MORE_CONSTRAIN_ALLi × POSTt is significantly smaller for public banks than for private banks (coef diff = -0.447, one-tailed p-value = 0.00), consistent with CECL’s impact through capital constraints on small business lending being more pronounced for public banks. 2.7.2 Placebo tests The findings in this study may not be driven by CECL but by the inherent relationship between capital adequacy constraints and lending. Other studies find that when banks experience a negative shock to their capital adequacy ratios, they tend to cut back on their lending (e.g., Beatty and Liao 2011). If this is driving my results, then I should find similar results by using different treatment dates. To explore this possibility, I repeat all of my analyses using four different placebo treatment dates for the announcement of CECL. Specifically, I use (1) 2011Q4, 74 (2) 2012Q4, (3) 2013Q4, and (4) 2014Q4 as the placebo CECL approval dates.74 Then I test my hypotheses using the placebo announcement dates.75 The placebo results are, in general, qualitatively weaker than the tests using the actual CECL announcement date (2015Q4). Table 2.15 reports the number of significant test results for each hypothesis using the actual announcement date and placebo announcement dates. Using the true announcement date, I find 19 out of the possible 27 test results that support my hypotheses. The test results using 2014Q4 as the placebo CECL approval date only retains 37%76 of coefficients of interest that are statistically significant at conventional levels. For 2013Q4, 2012Q4, and 2011Q4, the retention rates are 32%, 32%, and 26%, respectively. The degree to which my original results can be replicated using the placebo dates declines as the assumed treatment dates move further from the actual treatment date.77 The much stronger results that I find using the true CECL approval date imply that something different happened after 2015Q4 during the CECL transition period. 2.7.3 Are banks that increase their equity less affected by CECL? As mentioned in section 2.4.4, banks can improve their regulatory capital status by raising new equity or retaining a profit. Thus, banks can alleviate CECL’s negative impact on their regulatory capital ratio by using a combination of decreasing new lending and raising 74 One limitation of this placebo period is that it begins during the global financial crisis and ends during the implementation of the Basel III regulatory capital requirements. 75 Since the common equity ratio is only available for all banks after 2015Q1, I calculate the capital constraint measure (CLOSE_TO_CONS_ALLi) using only the three regulatory capital ratios (Tier 1 capital ratio, total risk-based capital ratio, and leverage ratio). 76 The 36.84% is calculated using the following procedures. The 2014Q4 placebo test results in seven statistical tests of my hypotheses that are significant in the predicted direction. The seven significant coefficients tests retain 36.84% of the 19 significant tests that I get by using the actual CECL approval dates (7/19 = 36.84%). 77 For brevity, this paper does not tabulate the full regression results for all the placebo tests. All untabulated results are available upon request. 75 equity. The extent to which a bank uses one versus the other is likely to depend on their relative costs. The decrease in lending is expected to be smaller if a capital-constrained bank grows its equity after CECL is approved. To explore this possibility, I test whether CECL’s impact on bank lending through capital constraints is lower if banks increase their equity to asset ratio during the CECL transition period. Tables 2.16, 2.17, and 2.18 present the test results. In Table 2.16, I check whether CECL’s negative impact on growth in total loans is lower when banks increase their equity during the CECL transition period. Specifically, I extend the original H1 regression model by adding the bank’s change in total equity to asset ratio during the CECL transition period (dEQi)78 as a proxy for increasing in its equity79. I interact this new variable with closeness to capital constraints and the dummy variable for the post period. In columns [1] and [3], when an increase in equity (dEQi) is interacted with POSTt and one of the continuous measures of capital constraints (either CLOSE_TO_CONS_ALLi or CLOSE_TO_CONS_T1i), the coefficient estimates on both of these triple interaction terms are positive and significant. This finding implies that increasing equity capital helps banks that were capital-constrained to continue their lending. After controlling for equity increases, I continue to find that more capital-constrained banks tend to reduce their lending in the post CECL announcement period. In columns [2] and [4], when the dichotomous measure of capital constraints is interacted with POSTt and increase in equity, the coefficients are insignificant, 78 dEQi equals the change in bank i’s equity to asset ratio from the beginning of 2016Q1 to the end of 2018Q2. I use 2018Q2 as the end the CECL transition period to avoid losing observations due to a change in reporting requirements. In 2018Q3 smaller banks with assets below $3 Billion became exempt from filing Y-9C reports. See section 5.1 for more details. 79 In using this measure of equity raised over almost the entire post-CECL announcement period I am assuming that during the earlier quarters of the post-CECL period bank decision makers fully anticipated their equity raising strategy. 76 suggesting that raising equity moderates the lending reduction only when capital constraints are extremely high. In Table 2.17, I check whether CECL’s impact on the growth of long-term loans is lower when banks increase their equity during the CECL transition period. Similarly, I add the change in the total equity to asset ratio to the original H2 regression model. As we can see, the coefficient estimates on all the triple interaction terms are not significantly different from zero, as was the case in the original H2 test. In Table 2.18, I check whether CECL’s negative impact on the growth in various types of loans reduces when banks increase their equity during the CECL transition period. To investigate this question, I add the new three-way interacted variable CLOSE_TO_CONS_ALLi × POSTt × dEQi to each regression model used to test H3.80 In Table 2.18, the coefficient estimates on the new equity term interacted with post and capital constraints positively load for the growth of total real estate loans (∆TOT_RE_LOANSi,t) and growth in commercial loans (∆COM_RE_LOANSi,t) regressions in Columns [1] and [5], respectively. This finding implies that previously capital-constrained banks that increase their equity during the CECL transition period are able to issue more longer-term real estate loans during the post-period. After controlling for an increase in equity during the CECL transition period, I continue to find that banks that were more capital-constrained when CECL was approved experienced less growth in both their residential and commercial real estate lending after the announcement. While the results in this section are somewhat mixed, overall they tend to suggest that during the CECL 80 For brevity, I do not report results for the dichotomous capital constraint variables which are never significant. I also do not report results for the second continuous capital constraint variable (CLOSE_TO_CONS_T1i) that uses only the Tier 1 capital ratio constraint. The results for this measure are qualitatively similar to what I report in the Table 18 when I measure the capital constraint using all four regulatory capital ratios (CLOSE_TO_CONS_ALLi). 77 transition period, banks that were able to alleviate their capital constraints by increasing their equity experienced less reduction in their lending. The analysis in this section does not estimate a full structural model for the increase in equity capital and adjustments to lending by banks experiencing constraints on their regulatory capital. Therefore, the results here could be subject to a collider bias. Collider bias appears when we include an independent variable that is both an outcome of our treatment variable and an outcome of our dependent variable (i.e., a collider). Research shows that a collider is likely to bias the true treatment effect we are interested in.81 In the context here, raising equity during the CECL transition period is likely a collider because both increasing total loans (i.e., the dependent variable) and being capital constrained (i.e., a treatment variable) can cause banks to want to increase their equity capital (i.e., a new independent variable added). Therefore, the finding in this section should not be viewed as conclusive evidence that banks that raised new equity were able to avoid the impact of CECL on their lending practices. 2.8 Conclusion This study documents evidence consistent with the hypothesis that CECL influences bank loan supply through its anticipatory negative impact on bank regulatory capital. Specifically, I find that during the first three years of the CECL transition period (2016 - 2018), banks that were closer to their capital adequacy constraints (1) reduced their total loan growth, (2) reduced their growth rate of real estate loans, and (3) reduced their small business lending relative to less capital-constrained banks. In cross-sectional analyses, I find that the above findings are 81 See Gow et al. (2016) for a discussion of the potential for collider bias in accounting research. Also see the following video for an excellent explanation on collider bias: https://youtu.be/vKeBD6JLexY. 78 concentrated in banks operating under a more lenient regulator, consistent with financially weaker banks being more sensitive to the anticipated capital shock induced by CECL. Loan growth rates for total loans and real estate loans were higher on average for the more capital-constrained banks than for the less constrained banks during the benchmark period (before CECL was approved). During the CECL transition period, the two groups of banks become much more similar in their lending growth rates. This may reflect the fact that CECL led the capital-constrained banks to cut back on their more aggressive lending policies, and the less constrained banks took advantage of the opportunity by increasing their lending rates. Alternatively, it could be an indication of mean reversion in lending growth rates over time. It is not possible to say whether the new lending policies that I observe as the adoption of CECL approaches are a better or worse societal outcome than what occurred before CECL was approved. Bank regulatory capital is also likely to be more negatively impacted by CECL when loans have a longer maturity. I do not find that capital-constrained banks, on average, reduce their issuance of long-term loans (i.e., loans with a remaining life of five years or more). However, I do find that two groups of capital-constrained banks reduce the growth rate of their long-term loans during the CECL transition period in this study, consistent with an effort to mitigate CECL’s negative impact. Capital-constrained banks are found to reduce their long-term loan growth when they are regulated by a lenient regulator or when they are publicly traded banks, which must adopt CECL earlier than their privately-held counterparts. The findings in this paper are consistent with many banks’ concerns that CECL is expected to reduce their regulatory capital and, as a result, will impact their lending policies. This paper provides evidence supporting CECL’s potentially disparate effects on certain types of 79 lending (e.g., longer-term loans), which has been mentioned as a key area of debate in a policy assessment report written by the U.S. Department of Treasury.82 Policymakers should be interested to learn this study’s finding that it is not only growth in long-term loans that is being reduced by CECL impacted banks. Capital-constrained banks have also reduced the growth rate of their residential loans and originated fewer small business loans. Thus, banking regulators should also consider CECL’s potential effects on banks’ willingness to originate home mortgages and its potential impact on the availability of small business loans in the economy. This study also has a key takeaway for standard setters interested in evaluating the economic impact of a new accounting change. The finding that CECL appears to influence bank lending decisions during the CECL transition period implies that only conducting an ex-post analysis after a new standard’s effective date is likely to miss a large part of a new rule’s economic impact. By ignoring the transition period, it is even possible that researchers could miss a new standard’s entire impact if all of the responses were to happen in anticipation of the new standard. Expanding the potential response period to include the pre-adoption period is crucial as we see many other new accounting standards with long transition periods in recent times (e.g., two years for the new U.S. lease accounting standard, ASC 842; and three years for the new U.S. revenue recognition accounting standard, ASC 606). The transition period examined in this study ends in the fourth quarter of 2018 because additional changes to the CECL transition timeline were announced in 2019 and 2020. Therefore, I am unable to observe changes to lending policies governing shorter-term loans, such as consumer loans, that may happen closer to the CECL adoption date. Finally, a further 82 The report is available at https://www.nafcu.org/sites/default/files/Treasury%20Study%20CECL.pdf. 80 limitation of this study is the fact that all banks must adopt CECL, making it more difficult to use the difference-in-difference approach to identify the impact of CECL on bank lending. I rely on a bank’s regulatory capital adequacy in 2015 to indicate those banks that are likely to be more impacted by CECL. As mentioned in section 7.2, one has to keep in mind that the reduction in the loan growth of capital-constrained banks might be a general outcome due to deteriorating capital status rather than a specific one due to the anticipation of CECL. To explore this concern, I conduct a placebo test using a different sample period, and I find far fewer significant results. The difference-in-difference approach could also lead to results that appear to support my hypotheses even if CECL was not the influencing factor. If there was another event that happened around the same time of CECL approval and the impact of that event also relates to a bank’s capital status, it could influence the results I observe. This is always a potential problem with a difference-in-difference approach. 81 Table 2.1 Composition of the FR Y-9C sample Panel A - Sample selection process Number of bank-quarters Bank holding companies (BHCs) filing Y9C during 2013Q1 and 2018Q4 19,148 Less: BHCs with no commercial bank subsidiary (1,787) BHCs with a loans-to-assets ratio < 10% (inactive in loan market) (125) BHCs with lower than zero or missing total deposits (3,752) BHCs with asset growth > 20% (likely involve in a merger) (256) BHCs with less than ten quarters before and after CECL approval in 2015Q4 (5,664) BHCs with missing variables (491) Total 7,073 Number of unique banks 313 Panel B - Number of observations in each time period Number of banks in: Year Quarter 1 Quarter 2 Quarter 3 Quarter 4 Total 2013 305 305 306 299 1,215 2014 306 306 309 305 1,226 2015 304 301 305 306 1,216 2016 308 304 304 310 1,226 2017 305 303 307 304 1,219 2018 307 306 178 180 971 7,073 Note: 82 Panel A summarizes the sample selection process. The bank data is collected from the FR Y-9C database, which is available on https://www.chicagofed.org/banking/financial-institution-reports/bhc-data. Panel B illustrates the breakdown of the FR Y-9C Sample, including the number of banks in each quarter. The number of banks significantly drops from 306 in 2018Q2 to 178 in 2018Q3 because Federal Reserves increased the asset-size threshold for filing the FR Y-9C from $1 billion to $3 billion effective in 2018Q3. See https://www.federalreserve.gov/apps/reportforms/reportdetail.aspx?sOoYJ+5BzDal8cbqnRxZRg== for more details. Panel C - Number of large, medium, and small banks Bank-quarters Unique banks Assets ≥ $50 billions (Large) 443 6% 21 $10b ≤ Assets < $50b (Medium) 652 9% 43 Assets < $10 billions (Small) 5,978 85% 274 Total 7,073 Panel D - Number of public banks Bank-quarters Unique banks Public 3,726 53% 165 Private 3,347 47% 148 Total 7,073 Panel E - Leniently regulated and public banks Leniently regulated banks (HIGH_REG_LENi = 1) Strictly regulated banks (HIGH_REG_LENi = 0) Public banks 1,723 2,004 3,726 Private banks 1,748 1,598 3,347 Total # of bank-quarters 3,471 3,602 Note: 83 Panel C shows the number of large, medium, and small banks in the sample used in the analysis. Panel D shows the number of public banks in the sample used in the analysis. Public banks are defined as the banks that are publicly traded in a stock exchange or over the counter. The list of public banks is obtained from the Federal Reserve. See https://www.newyorkfed.org/research/banking research/datasets.html for more details. 84 Table 2.2 Descriptive Statistics and Correlations - Y9C Sample (bank-quarter) Panel A: Descriptive Statistics Variables N Mean St. Dev. Min Pctl(25) Median Pctl(75) Max LOANSi,t-1 (%) 7,073 66.152 13.319 22.120 58.488 67.926 76.097 88.424 TOT_RE_LOANSi,t-1 (%) 7,073 40.939 15.480 5.468 30.937 40.560 51.469 77.627 RES_RE_LOANSi,t-1 (%) 7,073 20.781 11.770 2.509 12.444 18.973 26.212 60.738 COM_RE_LOANSi,t-1 (%) 7,073 20.058 9.692 0.449 13.398 19.798 25.448 53.248 COM_NRE_LOANSi,t-1 (%) 7,073 12.679 8.434 0.392 6.178 10.589 17.937 39.249 CONSUMER_LOANSi,t-1 (%) 7,073 2.882 4.132 0.006 0.518 1.232 3.059 21.572 LONG_TERM_LOANSi,t-1 (%) 7,073 18.626 11.456 1.478 10.113 16.068 24.969 53.886 ∆LOANSi,t (%) 7,073 2.011 3.126 -5.193 0.209 1.644 3.335 15.348 ∆TOT_RE_LOANSi,t-1 (%) 7,073 1.802 3.505 -6.300 -0.207 1.319 3.224 16.231 ∆RES_RE_LOANSi,t-1 (%) 7,073 1.615 4.519 -10.494 -0.821 1.043 3.368 20.229 ∆COM_RE_LOANSi,t-1 (%) 7,073 2.069 4.646 -8.043 -0.650 1.398 3.977 21.752 ∆COM_NRE_LOANSi,t (%) 7,073 2.619 7.489 -15.542 -1.506 1.842 5.678 35.017 ∆CONSUMER_LOANSi,t (%) 7,073 1.401 11.660 -32.431 -3.398 0.344 4.181 62.221 ∆LONG_TERM_LOANSi,t (%) 7,073 2.697 9.000 -25.095 -1.284 1.807 5.604 43.718 CLOSE_TO_CONS_T1i 7,073 0.462 0.096 0.225 0.400 0.469 0.520 0.708 CLOSE_TO_CONS_ALLi 7,073 0.559 0.109 0.289 0.495 0.564 0.623 0.941 EQUITY_TO_ASSETSi,t (%) 7,073 10.643 2.579 5.529 8.941 10.329 12.011 21.286 85 dEQi (%) 7,073 0.391 1.416 -3.240 -0.431 0.337 0.956 4.999 CET1_CAP_RATIOi,t (%) 4,632 12.553 3.509 5.486 10.307 11.828 14.170 25.882 T1_CAP_RATIOi,t (%) 6,950 13.730 3.324 8.112 11.595 12.989 15.195 26.663 TOTAL_CAPITAL_RATIOi,t (%) 6,950 15.172 3.189 10.273 13.085 14.427 16.448 27.918 LEV_RATIOi,t (%) 6,950 10.139 2.088 5.648 8.875 9.834 10.967 18.943 TOTAL_ASSETSi,t-1 ($ Bill) 7,073 37.766 213.747 0.638 1.199 1.933 5.064 1849.182 SIZEi,t-1 7,073 14.986 1.490 13.366 13.997 14.475 15.438 21.338 NPLi,t-1 (%) 7,073 1.219 1.157 0.031 0.479 0.867 1.564 6.640 DEPOSITSi,t-1 (%) 7,073 89.692 7.458 59.148 86.495 91.259 94.847 99.552 CASH&MKTSECi,t-1 (%) 7,073 6.014 5.613 0.835 2.439 4.187 7.354 31.891 TRADING_ASSETSi,t-1 (%) 7,073 0.282 1.348 0.000 0.000 0.000 0.000 11.000 ROAi,t-1 (%) 7,073 0.257 0.139 -0.186 0.187 0.247 0.313 0.904 REG_LENi 7,073 0.069 0.058 -0.006 0.000 0.073 0.107 0.266 Note: Panel A reports the descriptive statistics for the FR Y-9C sample, which is at the bank-quarter level. The number of observations for common equity Tier 1 capital ratio (CET1_RATIOi,t) is equal to 4,632, which is lower than the number of most other variables because CET1_RATIOi,t is only available after 2015Q1. The other three regulatory capital ratios (T1_CAP_RATIOi,t, TOTAL_CAPITAL_RATIOi,t, LEV_RATIOi,t) also have fewer observations than others because my sample selection process does not require these three capital ratios to be non-missing in my whole sample period, 2013Q1-2018Q4. Instead, I only require them to be non-missing in 2015Q1-2015Q4. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 86 Panel B: Descriptive Statistics before and after CECL approval Benchmark period - Before CECL approval (2013Q1-2015Q4) CECL transition period - After CECL approval (2016Q1-2018Q4) Variables N Mean Median St. Dev. N Mean Median St. Dev. LOANSi,t-1 (%) 3,657 64.466 65.949 13.347 3,416 67.956 69.913 13.052 TOT_RE_LOANSi,t-1 (%) 3,657 40.324 39.690 15.468 3,416 41.597 41.405 15.469 RES_RE_LOANSi,t-1 (%) 3,657 20.597 18.774 11.792 3,416 20.979 19.163 11.745 COM_RE_LOANSi,t-1 (%) 3,657 19.636 19.148 9.626 3,416 20.509 20.431 9.743 COM_NRE_LOANSi,t-1 (%) 3,657 12.444 10.225 8.590 3,416 12.930 11.074 8.258 CONSUMER_LOANSi,t-1 (%) 3,657 2.805 1.264 3.932 3,416 2.965 1.198 4.335 LONG_TERM_LOANSi,t-1 (%) 3,657 17.925 15.457 11.168 3,416 19.376 16.815 11.710 ∆LOANSi,t (%) 3,657 2.049 1.667 3.299 3,416 1.970 1.636 2.930 ∆TOT_RE_LOANSi,t-1 (%) 3,657 3.007 2.170 7.916 3,416 2.205 1.499 6.981 ∆RES_RE_LOANSi,t-1 (%) 3,657 1.684 1.090 4.729 3,416 1.542 0.960 4.282 ∆COM_RE_LOANSi,t-1 (%) 3,657 1.888 1.125 4.822 3,416 2.263 1.617 4.442 ∆COM_NRE_LOANSi,t (%) 3,657 1.723 1.194 3.694 3,416 1.886 1.441 3.289 ∆CONSUMER_LOANSi,t (%) 3,657 1.377 0.184 11.416 3,416 1.426 0.507 11.918 ∆LONG_TERM_LOANSi,t (%) 3,657 3.567 2.361 9.475 3,416 1.766 1.207 8.362 87 CLOSE_TO_CONS_T1i 3,657 0.461 0.469 0.096 3,416 0.462 0.469 0.096 CLOSE_TO_CONS_ALLi 3,657 0.559 0.564 0.109 3,416 0.560 0.564 0.108 EQUITY_TOTAL_ASSETSi,t (%) 3,657 10.521 10.196 2.704 3,416 10.774 10.464 2.431 CET1_CAP_RATIOi,t (%) 1,216 12.591 11.784 3.770 3,416 12.539 11.835 3.413 T1_CAP_RATIOi,t (%) 3,534 13.994 13.282 3.430 3,416 13.457 12.689 3.189 TOTAL_CAPITAL_RATIOi,t (%) 3,534 15.446 14.744 3.316 3,416 14.888 14.120 3.026 LEV_RATIOi,t (%) 3,534 10.085 9.785 2.156 3,416 10.194 9.865 2.014 TOTAL_ASSETSi,t-1 ($ Bill) 3,657 35.048 1.674 206.726 3,416 40.677 2.375 221.009 SIZEi,t-1 3,657 14.839 14.330 1.464 3,416 15.143 14.680 1.501 NPLi,t-1 (%) 3,657 1.519 1.107 1.321 3,416 0.899 0.668 0.840 DEPOSITSi,t-1 (%) 3,657 89.842 91.509 7.569 3,416 89.531 91.061 7.334 CASH&MKTSECi,t-1 (%) 3,657 6.490 4.557 5.913 3,416 5.504 3.764 5.225 TRADING_ASSETSi,t-1 (%) 3,657 0.282 0.000 1.372 3,416 0.281 0.000 1.322 ROAi,t-1 (%) 3,657 0.253 0.239 0.146 3,416 0.261 0.256 0.133 REG_LENi 3,657 0.069 0.073 0.058 3,416 0.069 0.073 0.058 Note: 88 Panel B reports the descriptive statistics for the FR Y-9C sample in periods before and after the approval of CECL. The number of observations for common equity Tier 1 capital ratio (CET1_RATIOi,t) in the benchmark period is equal to 1,216, which is lower than the number of observations for most other variables because CET1_RATIOi,t is only available after 2015Q1. The other three regulatory capital ratios (T1_CAP_RATIOi,t, TOTAL_CAPITAL_RATIOi,t, LEV_RATIOi,t) also have fewer observations than others because my sample selection process does not require these three capital ratios to be non-missing in my whole sample period, 2013Q1-2018Q4. Instead, I only require them to be non-missing in 2015Q1-2015Q4. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 89 Panel C: Pearson (top) and Spearman (bottom) correlation coefficients [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [1] LOANSi,t-1 0.63 0.43 0.48 0.29 -0.04 0.35 0.06 0.06 0.06 0.03 [2] TOT_RE_LOANSi,t-1 0.59 0.78 0.64 -0.41 -0.32 0.58 0.05 0.03 0.04 0.01 [3] RES_RE_LOANSi,t-1 0.38 0.77 0.01 -0.42 -0.14 0.58 0.01 -0.02 -0.01 0.01 [4] COM_RE_LOANSi,t-1 0.45 0.63 0.12 -0.14 -0.33 0.22 0.07 0.06 0.08 0.01 [5] COM_NRE_LOANSi,t-1 0.25 -0.41 -0.40 -0.07 0.00 -0.34 -0.01 0.01 0.02 -0.01 [6] CONSUMER_LOANSi,t-1 -0.16 -0.40 -0.14 -0.37 0.11 -0.09 -0.03 -0.07 -0.08 -0.03 [7] LONG_TERM_LOANSi,t-1 0.28 0.55 0.56 0.23 -0.30 -0.08 0.08 0.07 0.05 0.07 [8] ∆LOANSi,t 0.11 0.08 0.00 0.10 0.02 -0.08 0.10 0.77 0.60 0.56 [9] ∆TOT_RE_LOANSi,t 0.11 0.09 0.00 0.11 0.02 -0.11 0.11 0.71 0.71 0.76 [10] ∆RES_RE_LOANSi,t 0.08 0.08 0.02 0.08 0.02 -0.10 0.09 0.55 0.69 0.22 [11] ∆COM_RE_LOANSi,t 0.09 0.07 0.01 0.08 0.01 -0.05 0.09 0.50 0.74 0.17 [12] ∆COM_NRE_LOANSi,t 0.05 0.08 0.06 0.06 -0.03 -0.02 0.04 0.50 0.12 0.12 0.09 [13] ∆CONSUMER_LOANSi,t -0.01 -0.06 -0.02 -0.07 0.03 0.15 0.00 0.21 0.09 0.10 0.06 [14] ∆LONG_TERM_LOANSi,t -0.03 0.02 -0.02 0.04 -0.04 -0.02 0.00 0.39 0.37 0.35 0.20 [15] CLOSE_TO_CONS_T1i 0.36 -0.01 -0.06 0.18 0.48 0.01 -0.05 0.08 0.08 0.06 0.07 [16] CLOSE_TO_CONS_ALLi 0.35 0.03 -0.06 0.22 0.42 -0.03 -0.03 0.09 0.11 0.07 0.08 [17] SIZEi,t-1 -0.02 -0.13 -0.02 -0.20 0.08 0.14 -0.05 -0.01 -0.05 -0.04 -0.02 [18] NPLi,t-1 -0.20 -0.06 0.04 -0.09 -0.08 0.10 -0.10 -0.22 -0.25 -0.19 -0.17 [19] DEPOSITSi,t-1 -0.06 -0.09 -0.25 0.17 0.07 0.04 -0.13 0.00 0.05 0.03 0.01 [20] CASH&MKTSECi,t-1 -0.20 -0.25 -0.29 -0.09 0.00 0.05 -0.31 -0.09 -0.07 -0.06 -0.05 [21] TRADING_ASSETSi,t-1 -0.13 -0.24 -0.05 -0.30 0.14 0.14 -0.18 -0.06 -0.10 -0.07 -0.05 90 [22] ROAi,t-1 0.00 -0.19 -0.20 -0.08 0.13 0.08 -0.11 0.06 0.05 0.06 0.03 [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [1] LOANSi,t-1 0.03 0.01 -0.04 0.35 0.30 -0.17 -0.16 0.06 -0.41 -0.30 0.00 [2] TOT_RE_LOANSi,t-1 0.08 -0.01 0.00 0.01 0.04 -0.27 -0.03 -0.01 -0.34 -0.29 -0.18 [3] RES_RE_LOANSi,t-1 0.07 0.00 -0.03 -0.11 -0.10 -0.08 0.01 -0.22 -0.28 -0.12 -0.17 [4] COM_RE_LOANSi,t-1 0.04 -0.02 0.04 0.17 0.18 -0.34 -0.06 0.25 -0.21 -0.32 -0.09 [5] COM_NRE_LOANSi,t-1 -0.09 0.02 -0.03 0.43 0.35 0.08 -0.12 0.11 -0.08 -0.02 0.10 [6] CONSUMER_LOANSi,t-1 -0.02 0.03 -0.02 0.00 -0.05 0.29 0.05 -0.08 0.01 0.15 0.09 [7] LONG_TERM_LOANSi,t-1 0.06 0.01 -0.06 -0.09 -0.08 -0.13 -0.12 -0.10 -0.29 -0.18 -0.11 [8] ∆LOANSi,t 0.50 0.21 0.36 0.06 0.05 -0.06 -0.21 0.00 -0.06 -0.03 0.05 [9] ∆TOT_RE_LOANSi,t 0.18 0.12 0.33 0.07 0.08 -0.09 -0.22 0.03 -0.04 -0.06 0.04 [10] ∆RES_RE_LOANSi,t 0.15 0.11 0.30 0.06 0.06 -0.07 -0.16 0.01 -0.05 -0.05 0.04 [11] ∆COM_RE_LOANSi,t 0.15 0.09 0.21 0.04 0.04 -0.04 -0.17 -0.01 -0.02 0.00 0.03 [12] ∆COM_NRE_LOANSi,t 0.09 0.14 0.01 0.01 -0.03 -0.03 -0.04 -0.04 -0.01 -0.02 [13] ∆CONSUMER_LOANSi,t 0.10 0.06 0.02 0.00 0.01 -0.03 -0.01 -0.02 0.01 0.01 [14] ∆LONG_TERM_LOANSi,t 0.13 0.09 0.00 0.01 -0.07 0.01 0.01 0.00 -0.02 -0.03 [15] CLOSE_TO_CONS_T1i 0.05 0.02 0.00 0.91 0.13 -0.12 0.04 -0.13 -0.04 -0.08 [16] CLOSE_TO_CONS_ALLi 0.05 0.00 0.01 0.94 0.03 -0.11 0.07 -0.13 -0.08 -0.11 [17] SIZEi,t-1 0.01 0.07 -0.07 0.15 0.08 0.05 -0.46 0.23 0.56 0.06 [18] NPLi,t-1 -0.06 -0.03 0.00 -0.11 -0.14 -0.03 -0.01 0.14 0.11 -0.19 [19] DEPOSITSi,t-1 -0.04 0.00 0.03 -0.04 -0.01 -0.30 -0.03 0.00 -0.49 0.03 [20] CASH&MKTSECi,t-1 -0.05 -0.01 -0.01 -0.10 -0.14 -0.02 0.11 0.31 0.42 0.02 91 [21] TRADING_ASSETSi,t-1 0.00 0.08 -0.06 0.12 0.07 0.54 0.07 -0.30 0.06 0.00 [22] ROAi,t-1 0.01 0.04 -0.01 0.00 -0.03 0.14 -0.21 0.06 -0.02 0.02 Note: Panel C reports the Pearson correlation above the diagonal and the Spearman correlation below the diagonal for the variables included in the regression models. Correlation coefficients in bold are significantly different from zero at the 0.05 level. The sample selection process and its composition are presented in Table 2.1. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 92 Table 2.3 Difference-in-difference descriptive statistics Panel A: Banks that are closer to their capital adequacy constraints versus other banks Benchmark period - Before CECL approval (2013Q1-2015Q4) More capital-constrained banks Less capital-constrained banks Normalized Diff [(A1-B1)/ √(A2^2+B2^2)] t statistics P-value (A1) MEAN (A2) STD (B1) MEAN (B2) STD LOANSi,t-1 (%) 67.98 12.15 61.05 13.57 0.381 16.285 0.000 TOT_RE_LOANSi,t-1 (%) 39.70 14.67 40.93 16.19 -0.057 -2.424 0.021 RES_RE_LOANSi,t-1 (%) 18.94 10.10 22.20 13.04 -0.198 -8.474 0.000 COM_RE_LOANSi,t-1 (%) 20.73 9.13 18.58 9.97 0.159 6.804 0.000 COM_NRE_LOANSi,t-1 (%) 15.59 8.95 9.39 6.98 0.546 23.323 0.000 CONSUMER_LOANSi,t-1 (%) 2.74 3.82 2.86 4.04 -0.021 -0.907 0.264 LONG_TERM_LOANSi,t-1 (%) 17.30 10.16 18.53 12.04 -0.078 -3.323 0.002 ∆LOANSi,t (%) 2.45 3.53 1.66 3.01 0.172 7.360 0.000 ∆TOT_RE_LOANSi,t (%) 2.22 4.02 1.24 3.28 0.191 8.149 0.000 ∆RES_RE_LOANSi,t (%) 2.21 5.10 1.18 4.28 0.155 6.613 0.000 ∆COM_RE_LOANSi,t (%) 2.34 5.09 1.44 4.51 0.133 5.662 0.000 ∆COM_NRE_LOANSi,t (%) 3.26 7.57 2.76 8.23 0.045 1.919 0.063 ∆CONSUMER_LOANSi,t (%) 1.50 11.49 1.25 11.35 0.015 0.657 0.322 ∆LONG_TERM_LOANSi,t (%) 3.71 9.76 3.42 9.19 0.022 0.925 0.260 SIZEi,t-1 14.88 1.33 14.80 1.59 0.038 1.612 0.109 NPLi,t-1 (%) 1.29 1.21 1.75 1.38 -0.250 -10.714 0.000 93 DEPOSITSi,t-1 (%) 90.27 6.39 89.42 8.54 0.080 3.423 0.001 CASH&MKTSECi,t-1 (%) 5.89 5.37 7.08 6.34 -0.143 -6.139 0.000 TRADING_ASSETSi,t-1 (%) 0.17 0.68 0.39 1.80 -0.115 -4.948 0.000 ROAi,t-1 (%) 0.25 0.14 0.26 0.15 -0.030 -1.278 0.176 N of obs (See note) 1,802 1,855 CECL transition period - After CECL approval (2016Q1-2018Q4) More capital-constrained banks Less capital-constrained banks Normalized Diff [(C1-D1)/ √(C2^2+D2^2)] t statistics P-value (C1) MEAN (C2) STD (D1) MEAN (D2) STD LOANSi,t-1 (%) 71.07 11.75 64.90 13.53 0.344 -1.103 0.217 TOT_RE_LOANSi,t-1 (%) 41.14 14.52 42.05 16.34 -0.041 -1.693 0.095 RES_RE_LOANSi,t-1 (%) 19.31 9.86 22.62 13.14 -0.201 -5.049 0.000 COM_RE_LOANSi,t-1 (%) 21.81 9.31 19.23 9.99 0.189 -1.163 0.203 COM_NRE_LOANSi,t-1 (%) 15.84 8.50 10.07 6.90 0.528 3.794 0.000 CONSUMER_LOANSi,t-1 (%) 2.75 4.11 3.18 4.54 -0.069 -1.221 0.189 LONG_TERM_LOANSi,t-1 (%) 18.24 10.54 20.50 12.66 -0.137 -3.502 0.001 ∆LOANSi,t (%) 1.98 3.00 1.96 2.86 0.003 0.398 0.369 ∆TOT_RE_LOANSi,t (%) 1.96 3.33 1.82 3.25 0.030 0.241 0.387 ∆RES_RE_LOANSi,t (%) 1.52 4.42 1.56 4.14 -0.007 0.819 0.285 ∆COM_RE_LOANSi,t (%) 2.27 4.27 2.26 4.61 0.002 -0.973 0.249 94 ∆COM_NRE_LOANSi,t (%) 2.11 6.67 2.30 7.27 -0.019 -1.733 0.089 ∆CONSUMER_LOANSi,t (%) 1.32 11.37 1.53 12.43 -0.012 -3.057 0.004 ∆LONG_TERM_LOANSi,t (%) 1.78 8.82 1.76 7.89 0.002 2.695 0.011 SIZEi,t-1 15.22 1.36 15.07 1.62 0.072 -0.525 0.348 NPLi,t-1 (%) 0.84 0.75 0.96 0.91 -0.101 -0.461 0.359 DEPOSITSi,t-1 (%) 89.78 6.14 89.29 8.34 0.048 -0.999 0.242 CASH&MKTSECi,t-1 (%) 4.79 4.61 6.20 5.68 -0.193 -2.849 0.007 TRADING_ASSETSi,t-1 (%) 0.17 0.60 0.39 1.76 -0.122 -3.347 0.001 ROAi,t-1 (%) 0.26 0.13 0.26 0.13 -0.021 -0.003 0.399 N of obs (See note) 1,693 1,723 Panel B: Difference-in-Difference statistics DiD [ (C1-D1) - (A1-B1)] P-value t-stat LOANSi,t-1 (%) -0.76 0.21 -1.25 TOT_RE_LOANSi,t-1 (%) 0.33 0.65 0.45 RES_RE_LOANSi,t-1 (%) -0.04 0.94 -0.07 COM_RE_LOANSi,t-1 (%) 0.43 0.35 0.93 COM_NRE_LOANSi,t-1 (%) -0.42 0.26 -1.13 CONSUMER_LOANSi,t-1 (%) -0.31 0.12 -1.56 LONG_TERM_LOANSi,t-1 (%) -1.04 0.06 -1.92 * ∆LOANSi,t (%) -0.79 0.00 -5.32 *** ∆TOT_RE_LOANSi,t (%) -0.85 0.00 -5.13 *** 95 ∆RES_RE_LOANSi,t (%) -1.07 0.00 -5.02 *** ∆COM_RE_LOANSi,t (%) -0.89 0.00 -4.03 *** ∆COM_NRE_LOANSi,t (%) -0.69 0.05 -1.96 * ∆CONSUMER_LOANSi,t (%) -0.46 0.41 -0.82 ∆LONG_TERM_LOANSi,t (%) -0.27 0.52 -0.64 SIZEi,t-1 0.08 0.28 1.07 NPLi,t-1 (%) 0.34 0.00 6.59 *** DEPOSITSi,t-1 (%) -0.36 0.31 -1.02 CASH&MKTSECi,t-1 (%) -0.22 0.41 -0.83 TRADING_ASSETSi,t-1 (%) -0.01 0.93 -0.09 ROAi,t-1 (%) 0.00 0.75 0.32 Note: This table reports the difference-in-difference descriptive statistics of the FR Y-9C Sample (bank-quarter level). Panel A reports the descriptive statistics for banks that are more versus less capital constrained separately for the pre-period and post-period. More (less) capital-constrained banks are the banks with a CLOSE_TO_CONS_ALLi above (below) than the median during 2015Q1 and 2015Q4 after Basel III came into effect at the beginning of 2015. The benchmark period is the period during 2013Q1 and 2015Q4, which is before CECL was approved. The CECL transition period is defined as the period during 2016Q1 and 2018Q4 after CECL is approved. The normalized difference column is calculated as (𝑋𝑋�𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 − 𝑋𝑋�𝐿𝐿𝑀𝑀𝐿𝐿𝐿𝐿) �𝑆𝑆𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀2 − 𝑆𝑆𝐿𝐿𝑀𝑀𝐿𝐿𝐿𝐿2⁄ where 𝑋𝑋�𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 and 𝑆𝑆𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀2 (𝑋𝑋�𝐿𝐿𝑀𝑀𝐿𝐿𝐿𝐿 and 𝑆𝑆𝐿𝐿𝑀𝑀𝐿𝐿𝐿𝐿2 ) are the sample mean and sample variance for more (less) capital-constrained banks. The numbers of observations for less capital-constrained banks are the numbers for all variables except residential and commercial real estate loan growth rates (∆RES_RE_LOANSi,t-1, and ∆COM_RE_LOANSi,t-1). Panel B reports the difference in difference of the descriptive statistics. ***, **, * in the two panels denote a statistically significant t-statistics of the mean differences at the 0.01, 0.05, and 0.1 levels, respectively. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 96 Table 2.4 CECL and bank lending (H1) Dependent Variable = ∆LOANSi,t Independent Variables [1] [2] [3] [4] CLOSE_TO_CONS_ALLi × POSTt -2.029 t = -2.238** MORE_CONSTRAIN_ALLi × POSTt -0.421 t = -2.628** CLOSE_TO_CONS_T1i × POSTt -2.897 t = -2.740** MORE_CONSTRAIN_T1i × POSTt -0.407 t = -2.303** Controls SIZEi,t-1 -2.282 -2.277 -2.206 -2.266 t = -4.418*** t = -4.413*** t = -4.276*** t = -4.459*** RES_RE_LOANSi,t-1 -0.151 -0.152 -0.151 -0.154 t = -8.942*** t = -8.862*** t = -8.882*** t = -9.030*** COM_RE_LOANSi,t-1 -0.14 -0.139 -0.141 -0.141 t = -4.851*** t = -4.803*** t = -4.885*** t = -4.876*** COM_NRE_LOANSi,t-1 -0.123 -0.122 -0.122 -0.123 t = -3.426*** t = -3.418*** t = -3.377*** t = -3.405*** CONSUMER_LOANSi,t-1 -0.091 -0.093 -0.09 -0.094 t = -1.641 t = -1.706 t = -1.669 t = -1.697 LONG_TERM_LOANSi,t-1 0.026 0.025 0.024 0.026 t = 1.376 t = 1.364 t = 1.329 t = 1.380 NPLi,t-1 -0.457 -0.449 -0.45 -0.455 97 t = -5.337*** t = -5.336*** t = -5.335*** t = -5.338*** DEPOSITSi,t-1 -0.005 -0.004 -0.003 -0.003 t = -0.231 t = -0.176 t = -0.162 t = -0.141 CASH&MKTSECi,t-1 -0.002 -0.001 -0.002 -0.0003 t = -0.077 t = -0.058 t = -0.070 t = -0.015 TRADING_ASSETSi,t-1 0.31 0.267 0.327 0.265 t = 1.358 t = 1.141 t = 1.439 t = 1.131 ROAi,t-1 -0.373 -0.403 -0.371 -0.407 t = -0.792 t = -0.859 t = -0.787 t = -0.862 Year-quarter Fixed Effects Yes Yes Yes Yes Bank Fixed Effects Yes Yes Yes Yes R2 0.276 0.276 0.277 0.276 Number of bank-quarters 7,073 7,073 7,073 7,073 Note: This table reports the results from examining whether banks that are closer to their capital adequacy constraints are more likely to contract lending during the CECL transition period. The dependent variable is the loan growth rate (∆LOANSi,t). The key independent variables include the closeness to capital adequacy constraints (measured by CLOSE_TO_CONS_ALLi and CLOSE_TO_CONS_T1i), a dummy indicating observations that are during the CECL transition period (POSTt), and various control variables. Regressions [1] and [2] report the results obtained from using the three types of capital ratio to measure the closeness to capital adequacy constraints (CLOSE_TO_CONS_ALLi and whether CLOSE_TO_CONS_ALLi is greater than its median, i.e., MORE_CONSTRAIN_ALLi). Regressions [3] and [4] report the results obtained from using only the Tier 1 capital ratio to measure the closeness to capital adequacy constraints (CLOSE_TO_CONS_T1i and whether CLOSE_TO_CONS_T1i is greater than its median, MORE_CONSTRAIN_T1i). Variable definitions are presented in Appendix A.2. The regressions are based on the bank-quarters with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and year-quarter. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 98 Table 2.5 CECL and long-term loans (H2) Dependent Variable = ∆LONG_TERM_LOANSi,t Independent Variables [1] [2] [3] [4] CLOSE_TO_CONS_ALLi × POSTt 0.931 t = 0.336 MORE_CONSTRAIN_ALLi × POSTt -0.275 t = -0.438 CLOSE_TO_CONS_T1i × POSTt -0.857 t = -0.262 MORE_CONSTRAIN_T1i × POSTt 0.003 t = 0.004 Controls SIZEi,t-1 -2.041 -1.904 -1.928 -1.987 t = -1.757* t = -1.648 t = -1.673 t = -1.709 RES_RE_LOANSi,t-1 -0.137 -0.136 -0.136 -0.136 t = -2.118** t = -2.096** t = -2.105** t = -2.103** COM_RE_LOANSi,t-1 -0.222 -0.221 -0.222 -0.222 t = -3.447*** t = -3.431*** t = -3.446*** t = -3.414*** COM_NRE_LOANSi,t-1 -0.036 -0.038 -0.037 -0.037 t = -0.369 t = -0.388 t = -0.382 t = -0.375 CONSUMER_LOANSi,t-1 -0.249 -0.255 -0.252 -0.251 t = -1.399 t = -1.428 t = -1.407 t = -1.395 LONG_TERM_LOANSi,t-1 -0.437 -0.439 -0.439 -0.438 t = -6.452*** t = -6.460*** t = -6.489*** t = -6.373*** NPLi,t-1 -0.3 -0.282 -0.289 -0.295 99 t = -1.326 t = -1.222 t = -1.284 t = -1.306 DEPOSITSi,t-1 0.022 0.02 0.021 0.021 t = 0.455 t = 0.408 t = 0.422 t = 0.422 CASH&MKTSECi,t-1 0.006 0.003 0.004 0.004 t = 0.106 t = 0.051 t = 0.067 t = 0.075 TRADING_ASSETSi,t-1 -1.094 -1.069 -1.053 -1.072 t = -1.822* t = -1.825* t = -1.850* t = -1.799* ROAi,t-1 -3.552 -3.5 -3.503 -3.523 t = -2.267** t = -2.230** t = -2.250** t = -2.245** Year-quarter Fixed Effects Yes Yes Yes Yes Bank Fixed Effects Yes Yes Yes Yes R2 0.132 0.132 0.132 0.132 Number of bank-quarters 7,073 7,073 7,073 7,073 Note: This table reports the results from examining whether banks that are closer to their capital adequacy constraints are more likely to reduce their holding of long-term loans during the CECL transition period. The dependent variable is the growth rate of long-term loans (∆LONG_TERM_LOANSi,t). The key independent variables include the closeness to capital adequacy constraints (measured by CLOSE_TO_CONS_ALLi and CLOSE_TO_CONS_T1i), a dummy indicating observations that are during the CECL transition period (POSTt), and various control variables. Regressions [1] and [2] report the results obtained from using the three types of capital ratios to measure the closeness to capital adequacy constraints (CLOSE_TO_CONS_ALLi and whether CLOSE_TO_CONS_ALLi is greater than its median, i.e., MORE_CONSTRAIN_ALLi). Regressions [3] and [4] report the results obtained from using only the Tier 1 capital ratio to measure the closeness to capital adequacy constraints (CLOSE_TO_CONS_T1i and whether CLOSE_TO_CONS_T1i is greater than its median, i.e., MORE_CONSTRAIN_T1i). Variable definitions are presented in Appendix A.2. The regressions are based on the bank-quarters with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and year-quarter. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 100 Table 2.6 CECL and loan growth of different loan types (H3) Dependent Variable = ∆TOT_RE_LOANSi,t ∆COM_NRE_LOANSi,t ∆CONSUMER_LOANSi,t ∆RES_RE_LOANSi,t ∆COM_RE_LOANSi,t Independent Variables [1] [2] [3] [4] [5] MORE_CONSTRAIN_ALLi × POSTt -0.428 -0.436 0.111 -0.789 -0.243 t = -2.222** t = -1.162 t = 0.163 t = -2.833*** t = -1.054 Controls SIZEi,t-1 -2.095 -3.913 -4.36 -1.564 -2.775 t = -4.012*** t = -5.433*** t = -3.338*** t = -2.379** t = -4.593*** RES_RE_LOANSi,t-1 -0.19 -0.065 -0.07 -0.302 -0.059 t = -9.693*** t = -1.133 t = -0.660 t = -8.698*** t = -2.143** COM_RE_LOANSi,t-1 -0.231 -0.057 -0.185 -0.091 -0.385 t = -7.045*** t = -0.846 t = -1.527 t = -2.346** t = -8.338*** COM_NRE_LOANSi,t-1 0.023 -0.735 0.215 0.034 0.011 t = 0.580 t = -8.218*** t = 2.706** t = 0.720 t = 0.238 CONSUMER_LOANSi,t-1 0.013 0.028 -1.174 -0.129 0.091 t = 0.226 t = 0.197 t = -4.221*** t = -2.160** t = 1.201 LONG_TERM_LOANSi,t-1 0.014 0.043 0.148 0.002 0.03 t = 0.776 t = 1.298 t = 2.827*** t = 0.098 t = 1.260 NPLi,t-1 -0.338 -0.495 -0.668 -0.258 -0.428 t = -5.109*** t = -2.376** t = -2.214** t = -3.016*** t = -4.696*** DEPOSITSi,t-1 0.015 -0.021 -0.137 0.042 0.001 t = 0.846 t = -0.415 t = -2.475** t = 1.905* t = 0.018 CASH&MKTSECi,t-1 -0.009 -0.053 -0.046 -0.021 -0.015 t = -0.506 t = -0.856 t = -0.552 t = -0.877 t = -0.566 101 TRADING_ASSETSi,t-1 -0.269 -0.006 0.908 -0.291 -0.168 t = -0.765 t = -0.009 t = 2.143** t = -0.695 t = -0.572 ROAi,t-1 -0.833 -1.246 -0.197 -0.661 -0.393 t = -1.483 t = -1.313 t = -0.128 t = -1.157 t = -0.698 Year-quarter Fixed Effects Yes Yes Yes Yes Yes Bank Fixed Effects Yes Yes Yes Yes Yes R2 0.255 0.158 0.120 0.223 0.191 Number of bank-quarters 7,073 7,073 7,073 7,073 7,073 Note: This table reports the results from examining whether banks that are closer to their capital adequacy constraints are more likely to reduce the origination of real estate loans during the CECL transition period. The dependent variable is the loan growth rate for real estate loans, residential real estate loans, commercial real estate loans, commercial loans, and consumer loans (∆TOT_RE_LOANSi,t-1; ∆RES_RE_LOANSi,t-1; ∆COM_RE_LOANSi,t-1; ∆COM_NRE_LOANSi,t; and ∆CONSUMER_LOANSi,t). The key independent variables include the capital inadequacy risk (measured by MORE_CONSTRAIN_ALLi), a dummy indicating observations that are during the CECL transition period (POSTt), and various control variables. Regressions [1], [2], [3], [4], and [5] report the results from using the loan growth rate for real estate loans, residential real estate loans, commercial real estate loans, commercial loans, and consumer loans, respectively, as the dependent variables. The number of observations in column [5] is equal to 7,512, which is lower than the number of observations in other columns. This is because commercial real estate loans occasionally have a zero beginning of the quarter balance, leading to a missing value for their growth rates. Variable definitions are presented in Appendix A.2. The regressions are based on the bank-quarters with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and year-quarter. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 102 Table 2.7 CECL's impact on lending and regulatory leniency - using subsample tests (H4) Panel A: Regression estimation Dependent Variable = ∆LOANSi,t ∆LONG_TERM_LOANSi,t ∆TOT_RE_LOANSi,t-1 Subsample = High regulatory leniency (HIGH_REG_LENi = 1) Low regulatory leniency (HIGH_REG_LENi = 0) High regulatory leniency Low regulatory leniency High regulatory leniency Low regulatory leniency Hypotheses = H4a H4b H4c Independent Variables [1] [2] [3] [4] [5] [6] MORE_CONSTRAIN_ALLi × POSTt -0.662 -0.154 -1.329 0.625 -0.885 0.044 t = -2.985*** t = -0.648 t = -1.521 t = 0.853 t = -3.173*** t = 0.159 Controls SIZEi,t-1 -2.742 -2.040 -1.357 -2.770 -2.718 -1.561 t = -5.797*** t = -2.968*** t = -0.950 t = -1.573 t = -4.769*** t = -2.081** RES_RE_LOANSi,t-1 -0.192 -0.125 -0.148 -0.112 -0.207 -0.181 t = -5.837*** t = -8.960*** t = -1.487 t = -1.275 t = -5.876*** t = -7.856*** COM_RE_LOANSi,t-1 -0.174 -0.112 -0.317 -0.158 -0.256 -0.228 t = -3.979*** t = -2.797** t = -2.728** t = -1.805* t = -5.766*** t = -4.861*** COM_NRE_LOANSi,t-1 -0.132 -0.132 -0.032 -0.051 -0.008 0.049 t = -2.940*** t = -2.476** t = -0.259 t = -0.383 t = -0.138 t = 1.232 CONSUMER_LOANSi,t-1 -0.123 -0.078 0.076 -0.437 0.007 0.029 t = -1.277 t = -1.258 t = 0.329 t = -1.706 t = 0.079 t = 0.405 103 LONG_TERM_LOANSi,t-1 0.032 0.017 -0.515 -0.400 0.005 0.024 t = 1.869* t = 0.581 t = -6.004*** t = -4.598*** t = 0.282 t = 0.868 NPLi,t-1 -0.451 -0.455 -0.333 -0.215 -0.362 -0.319 t = -4.403*** t = -3.860*** t = -1.112 t = -0.548 t = -3.833*** t = -2.771** DEPOSITSi,t-1 -0.001 -0.008 0.066 -0.041 0.009 0.021 t = -0.049 t = -0.270 t = 0.938 t = -0.580 t = 0.387 t = 0.769 CASH&MKTSECi,t-1 -0.044 0.055 -0.045 0.066 -0.040 0.031 t = -1.396 t = 1.054 t = -0.560 t = 0.627 t = -1.389 t = 0.996 TRADING_ASSETSi,t-1 1.008 -0.072 -0.022 -1.600 0.186 -0.467 t = 2.142** t = -0.800 t = -0.039 t = -1.429 t = 1.260 t = -1.019 ROAi,t-1 -1.043 0.368 -4.646 -2.247 -1.598 0.001 t = -1.426 t = 0.935 t = -1.733* t = -1.203 t = -1.794* t = 0.002 Year-quarter Fixed Effects Yes Yes Yes Yes Yes Yes Bank Fixed Effects Yes Yes Yes Yes Yes Yes R2 0.274 0.291 0.145 0.132 0.248 0.272 Number of bank-quarters 3,471 3,602 3,471 3,602 3,471 3,602 Panel B: Tests of difference in coefficients across subsamples Hypothesis H4a H4b H4c Regression columns [1] − [2] < 0? [3] − [4] < 0? [3] − [4] < 0? MORE_CONSTRAIN_ALLi × POSTt coef diff -0.508 -1.954 -0.929 z-statistics -1.562 -1.714 -2.364 One-tailed p-value 0.06 0.04 0.01 Note: 104 Panel A reports the findings from examining whether the negative impact of CECL on bank lending through the capital channel is stronger when banks face a more lenient regulator. The dependent variables are the growth rate of total loans (∆LOANSi,t), the growth rate of long-term loans (LONG_TERM_LOANSi,t), and the growth rate of real estate loans (∆TOT_RE_LOANSi,t). The key independent variables include the closeness to capital adequacy constraints (MORE_CONSTRAIN_ALLi), a dummy indicating observations that are during the CECL transition period (POSTt), and various control variables. Regressions [1], [3], and [5] reports the results obtained from estimating the regression model using banks with regulatory leniency greater than the median. Regressions [2], [4], and [6] reports the results obtained from estimating the regression model using banks with regulatory leniency lower than the median. Variable definitions are presented in Appendix A.2. The regressions are based on the bank-quarters with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and year-quarter. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. Panel B presents z-tests of differences in coefficients across the different subsample regressions in Panel A. Specifically, the first two rows of Panel B indicate which regression columns are compared and the relevant hypothesis. The third and fourth rows provide the calculated difference of the MORE_CONSTRAIN_ALLi × POSTt coefficients and the associated z-statistics for the indicated regression columns. The last row provides the associated p-value for the significance of difference in the MORE_CONSTRAIN_ALLi × POSTt coefficients. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 105 Table 2.8 CECL's impact on lending and regulatory leniency - using continuous measure (H4) Dependent Variable = ∆LOANSi,t ∆LONG_TERM_LOANSi,t ∆TOT_RE_LOANSi,t Hypotheses = H4a H4b H4c Independent Variables [1] [2] [3] MORE_CONSTRAIN_ALLi × REG_LENi × POSTt -5.347 -18.802 -7.872 t = -2.297** t = -2.249** t = -2.493** Controls MORE_CONSTRAIN_ALLi × POSTt -0.055 1.032 0.118 t = -0.236 t = 1.490 t = 0.413 REG_LENi × POSTt 4.822 11.225 5.318 t = 2.607** t = 2.290** t = 2.104** SIZEi,t-1 -2.314 -1.967 -2.129 t = -4.431*** t = -1.723* t = -3.992*** RES_RE_LOANSi,t-1 -0.153 -0.14 -0.192 t = -8.980*** t = -2.161** t = -9.840*** COM_RE_LOANSi,t-1 -0.141 -0.231 -0.235 t = -4.855*** t = -3.621*** t = -7.056*** COM_NRE_LOANSi,t-1 -0.124 -0.039 0.022 t = -3.454*** t = -0.410 t = 0.551 CONSUMER_LOANSi,t-1 -0.095 -0.253 0.013 t = -1.726* t = -1.418 t = 0.221 LONG_TERM_LOANSi,t-1 0.025 -0.439 0.014 t = 1.366 t = -6.415*** t = 0.788 106 NPLi,t-1 -0.447 -0.287 -0.339 t = -5.281*** t = -1.243 t = -5.096*** DEPOSITSi,t-1 -0.005 0.018 0.014 t = -0.215 t = 0.364 t = 0.802 CASH&MKTSECi,t-1 -0.001 0.003 -0.009 t = -0.060 t = 0.048 t = -0.519 TRADING_ASSETSi,t-1 0.258 -1.102 -0.283 t = 1.084 t = -1.863* t = -0.800 ROAi,t-1 -0.424 -3.518 -0.847 t = -0.900 t = -2.211** t = -1.506 Year-quarter Fixed Effects Yes Yes Yes Bank Fixed Effects Yes Yes Yes R2 0.277 0.133 0.256 Number of bank-quarters 7,073 7,073 7,073 Note: This table reports the findings from examining whether the negative impact of CECL on bank lending through the capital channel is stronger when the banks face a more lenient regulator. The dependent variables are the loan growth rate (∆LOANSi,t), the long-term loan growth rate (∆LONG_TERM_LOANSi,t), and the growth rate of residential loans (∆TOT_RE_LOANSi,t). The key independent variables include the closeness to capital adequacy constraints (MORE_CONSTRAIN_ALLi), the proxy for regulatory leniency index (REG_LENi) obtained from Agarwal et al. (2014), a dummy indicating observations that are during the CECL transition period (POSTt), and various control variables. Variable definitions are presented in Appendix A.2. The regressions are based on the bank-quarters with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and year-quarter. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 107 Table 2.9 CECL's impact on lending and bank share listing status (H5) Panel A: Regression estimation Dependent Variable = ∆LOANSi,t ∆LONG_TERM_LOANSi,t ∆TOT_RE_LOANSi,t Subsample = Public Bank (PUBLICi = 1) Private Bank (PUBLICi = 0) Public Bank Private Bank Public Bank Private Bank Hypotheses = H5a H5b H5c Independent Variables [1] [2] [3] [4] [5] [6] MORE_CONSTRAIN_ALLi × POSTt -0.369 -0.464 -1.571 1.398 -0.359 -0.462 t = -1.803* t = -2.196** t = -1.751* t = 1.794* t = -1.457 t = -1.622 Controls SIZEi,t-1 -2.292 -4.207 -1.346 -7.174 -1.9 -4.351 t = -3.626*** t = -4.868*** t = -0.996 t = -2.337** t = -3.193*** t = -3.836*** RES_RE_LOANSi,t-1 -0.131 -0.187 -0.11 -0.229 -0.17 -0.231 t = -6.354*** t = -4.601*** t = -1.391 t = -1.571 t = -6.899*** t = -5.246*** COM_RE_LOANSi,t-1 -0.112 -0.171 -0.102 -0.362 -0.202 -0.28 t = -2.636** t = -5.107*** t = -1.155 t = -3.664*** t = -4.489*** t = -6.750*** COM_NRE_LOANSi,t-1 -0.102 -0.135 0.041 -0.094 0.024 0.04 t = -2.102** t = -3.308*** t = 0.362 t = -0.740 t = 0.426 t = 0.774 CONSUMER_LOANSi,t-1 -0.06 -0.159 -0.352 0.083 0.049 -0.07 t = -1.356 t = -1.312 t = -2.085** t = 0.326 t = 0.720 t = -0.911 LONG_TERM_LOANSi,t-1 0.032 0.015 -0.393 -0.571 0.013 0.015 t = 1.224 t = 0.742 t = -4.926*** t = -6.491*** t = 0.531 t = 0.639 NPLi,t-1 -0.391 -0.439 -0.382 -0.073 -0.225 -0.416 t = -2.905*** t = -3.781*** t = -1.096 t = -0.208 t = -1.888* t = -4.965*** DEPOSITSi,t-1 -0.03 0.036 0.017 0.031 -0.019 0.071 t = -1.136 t = 1.321 t = 0.234 t = 0.438 t = -0.855 t = 2.645** 108 CASH&MKTSECi,t-1 0.004 -0.002 -0.082 0.071 -0.031 0.018 t = 0.100 t = -0.083 t = -1.021 t = 1.115 t = -0.975 t = 0.595 TRADING_ASSETSi,t-1 0.423 -1.151 -0.735 -2.742 -0.183 -1.16 t = 1.586 t = -3.294*** t = -1.106 t = -2.161** t = -0.487 t = -4.695*** ROAi,t-1 0.242 -1.031 -4.206 -2.875 -0.296 -1.415 t = 0.353 t = -1.976* t = -1.940* t = -1.520 t = -0.370 t = -2.136** Year-quarter Fixed Effects Yes Yes Yes Yes Yes Yes Bank Fixed Effects Yes Yes Yes Yes Yes Yes R2 0.298 0.262 0.123 0.158 0.298 0.211 Number of bank-quarters 3,726 3,347 3,726 3,347 3,726 3,347 Panel B: Tests of difference in coefficients across subsamples Hypothesis H5a H5b H5c Regression columns: [1] − [2] ≠ 0? [3] − [4] ≠ 0? [3] − [4] ≠ 0? MORE_CONSTRAIN_ALLi × POSTt coef diff 0.095 -2.969 0.103 z-statistics 0.323 -2.498 0.273 Two-tailed p-value 1.00 0.01 1.00 Note: 109 Panel A reports the findings from examining whether the negative impact of CECL on bank lending through the capital channel is stronger when banks are publicly listed in a stock exchange. The dependent variables are the growth rate of total loans (∆LOANSi,t), the growth rate of long-term loans (∆LONG_TERM_LOANSi,t), and the growth rate of real estate loans (∆TOT_RE_LOANSi,t). The key independent variables include the closeness to capital adequacy constraints (MORE_CONSTRAIN_ALLi), a dummy indicating observations that are during the CECL transition period (POSTt), and various control variables. Regressions [1], [3], and [5] ([2], [4], and [6]) report the results obtained from estimating the regression model using publicly traded (privately held) banks. Variable definitions are presented in Appendix A.2. The regressions are based on the bank-quarters with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and year-quarter. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. Panel B presents z-tests of differences in coefficients across the different subsample regressions in Panel A. Specifically, the first two rows of Panel B indicate which regression columns are compared and the relevant hypothesis. The third and fourth rows provide the calculated difference of the MORE_CONSTRAIN_ALLi × POSTt coefficients and the associated z-statistics for the indicated regression columns. The last row provides the associated p-value for the significance of difference in the MORE_CONSTRAIN_ALLi × POSTt coefficients. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 110 Table 2.10 Composition of the Small Business Loan Sample Panel A - Sample selection process Number of bank-county-years BHC main sample merged with the CRA small business lending data Number of banks in the main Y9C sample 313 Less: number of banks with no small business loan origination available - 57 Number of unique banks in the SBL sample 256 SBL Sample of year-bank-county observations (2013-2018) 256,849 Number of unique counties 3,084 Note: This panel summarizes the sample selection process. The SBL Sample is the inner joint of the FR Y-9C data and the CRA small business lending data. The bank data is collected from the FR Y-9C database, which is available on https://www.chicagofed.org/banking/financial-institution-reports/bhc-data. The small business lending data is collected from the CRA database, which is available on https://www.ffiec.gov/cra/default.htm. See, e.g., Bord et al. (2018) for a more comprehensive description of the CRA data. Panel B - Sample composition by year SBL Sample (Bank-county-year level) Year Number of observations Number of banks Number of counties 2013 35,726 187 3,080 2014 37,623 214 3,079 2015 41,691 225 3,081 2016 46,135 236 3,084 2017 48,105 238 3,082 2018 47,569 165 3,082 Total 256,849 Note: 111 This panel illustrates the breakdown of the SBL Sample, including the number of bank-county pairs, the number of unique banks, and the number of unique counties in each sample year. The number of unique banks significantly drops from 238 in 2017 to 165 in 2018 because Federal Reserves increased the asset-size threshold for filing the Y-9C from $1 billion to $3 billion effective in 2018Q3. See https://www.federalreserve.gov/apps/reportforms/reportdetail.aspx?sOoYJ+5BzDal8cbqnRxZRg== for more details. 112 Table 2.11 Descriptive Statistics - Small business lending (SBL) sample Variables Mean St. Dev. Min Pctl(25) Median Pctl(75) Max SBLi,c,t (thousand) 2,517 7,839 3 58 250 1,073 56,436 LOG_SBLi,c,t 5.610 2.138 1.099 4.060 5.521 6.978 10.941 CLOSE_TO_CONS_T1i 0.487 0.061 0.299 0.460 0.485 0.535 0.624 CLOSE_TO_CONS_ALLi 0.559 0.066 0.381 0.521 0.561 0.607 0.715 SIZEi,t-1 18.491 2.433 13.930 16.400 19.030 21.246 21.668 TOTAL_ASSETSi,t-1 (%) 650.281 881.166 1.122 13.254 183.872 1687.155 2572.773 TOT_RE_LOANSi,t-1 (%) 27.463 13.332 3.091 16.833 26.669 35.685 63.372 RES_RE_LOANSi,t-1 (%) 16.910 7.460 0.565 11.858 16.729 21.208 43.611 COM_RE_LOANSi,t-1 (%) 10.450 8.693 0.299 3.773 7.098 16.964 35.493 COM_NRE_LOANSi,t-1 (%) 14.277 8.094 3.392 8.265 11.389 19.277 39.969 CONSUMER_LOANSi,t-1 (%) 9.297 9.471 0.002 2.879 7.932 10.579 44.076 LONG_TERM_LOANSi,t-1 (%) 13.443 7.196 2.154 8.539 12.712 17.492 38.170 NPLi,t-1 (%) 1.767 1.301 0.184 0.849 1.510 2.216 7.216 DEPOSITSi,t-1 (%) 79.374 13.052 40.833 75.042 81.664 90.323 97.667 CASH&MKTSECi,t-1 (%) 9.688 9.088 1.353 2.850 4.943 16.404 35.523 TRADING_ASSETSi,t-1 (%) 3.537 5.695 0.000 0.010 0.400 4.300 19.000 ROAi,t-1 (%) 1.013 0.419 -0.365 0.816 1.016 1.264 2.271 REG_LENi 0.040 0.055 -0.055 0.000 0.000 0.085 0.315 # of obs = 256,849 Note: This table reports the descriptive statistics for the SBL Sample. Bank-county-year level data is aggregated at the bank holding company-county level. The selection process of these two samples and their composition are presented in Table 2.10. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 113 Table 2.12 CECL and small business lending Dependent Variable = LOG_SBLi,c,t Independent Variables [1] [2] [3] [4] CLOSE_TO_CONS_ALLi × POSTt -2.011 t = -3.949*** MORE_CONSTRAIN_ALLi × POSTt -0.239 t = -3.154*** CLOSE_TO_CONS_T1i × POSTt -1.706 t = -3.269*** MORE_CONSTRAIN_T1i × POSTt -0.233 t = -3.764*** Controls SIZEi,t-1 0.055 0.024 -0.023 -0.024 t = 0.245 t = 0.109 t = -0.092 t = -0.097 RES_RE_LOANSi,t-1 -0.008 -0.009 -0.012 -0.016 t = -0.720 t = -0.872 t = -1.087 t = -1.523 COM_RE_LOANSi,t-1 0.03 0.028 0.026 0.022 t = 1.813* t = 1.739* t = 1.513 t = 1.321 COM_NRE_LOANSi,t-1 -0.023 -0.024 -0.026 -0.025 t = -1.421 t = -1.517 t = -1.526 t = -1.463 CONSUMER_LOANSi,t-1 -0.025 -0.031 -0.028 -0.035 t = -1.289 t = -1.602 t = -1.438 t = -1.847* LONG_TERM_LOANSi,t-1 -0.006 -0.005 -0.007 -0.008 t = -0.705 t = -0.592 t = -0.731 t = -0.866 NPLi,t-1 0.086 0.095 0.075 0.054 114 t = 2.060** t = 2.185** t = 1.695* t = 1.218 DEPOSITSi,t-1 0.02 0.023 0.02 0.02 t = 2.949*** t = 3.009*** t = 2.813*** t = 2.665*** CASH&MKTSECi,t-1 -0.017 -0.02 -0.018 -0.02 t = -1.648 t = -1.917* t = -1.868* t = -1.969** TRADING_ASSETSi,t-1 -0.017 -0.028 -0.029 -0.024 t = -0.510 t = -0.764 t = -0.855 t = -0.669 ROAi,t-1 -0.003 -0.014 -0.007 -0.004 t = -0.047 t = -0.228 t = -0.110 t = -0.066 Bank Fixed Effects Yes Yes Yes Yes County-year Fixed Effects Yes Yes Yes Yes R2 0.448 0.448 0.448 0.448 Number of bank-county-years 256,849 256,849 256,849 256,849 Note: This table reports the results from examining whether banks that are closer to their capital adequacy constraints are more likely to reduce small business lending during the CECL transition period. The dependent variable is the log value of small business lending (LOG_SBLi,c,t). The key independent variables include the closeness to capital adequacy constraints(measured by CLOSE_TO_CONS_ALLi and CLOSE_TO_CONS_T1i), a dummy indicating observations that are during the CECL transition period (POSTt), and various control variables. Regressions [1] and [2] report the results obtained from using the three types of capital ratio to measure the closeness to capital adequacy constraints (CLOSE_TO_CONS_ALLi and whether CLOSE_TO_CONS_ALLi is greater than its median, i.e., MORE_CONSTRAIN_ALLi). Regressions [3] and [4] report the results obtained from using only the Tier 1 capital ratio to measure the closeness to capital adequacy constraints(CLOSE_TO_CONS_T1i and whether CLOSE_TO_CONS_T1i is greater than its median, i.e., MORE_CONSTRAIN_T1i). Variable definitions are presented in Appendix A.2. The regressions are based on the bank-county-years with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and county-year. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 115 Table 2.13 Cross-sectional analysis of CECL's impact on small business lending Panel A: Regression estimation Dependent Variable = LOG_SBLi,c,t LOG_SBLi,c,t Subsample = High regulatory leniency (HIGH_REG_LENi = 1) Low regulatory leniency (HIGH_REG_LENi = 0) Public Bank (PUBLICi = 1) Private Bank (PUBLICi = 0) Independent Variables [1] [2] [3] [4] MORE_CONSTRAIN_ALLi × POSTt -0.272 -0.037 -0.297 0.150 t = -2.892*** t = -0.335 t = -3.481*** t = 2.195** Controls SIZEi,t-1 0.098 -0.448 0.088 -0.145 t = 0.521 t = -1.261 t = 0.381 t = -0.574 RES_RE_LOANSi,t-1 -0.014 -0.011 -0.012 -0.002 t = -1.212 t = -0.602 t = -1.018 t = -0.121 COM_RE_LOANSi,t-1 0.034 0.008 0.033 -0.002 t = 2.060** t = 0.315 t = 1.743* t = -0.217 COM_NRE_LOANSi,t-1 -0.029 -0.047 -0.023 -0.009 t = -1.935* t = -1.859* t = -1.294 t = -0.786 CONSUMER_LOANSi,t-1 -0.032 -0.028 -0.038 0.028 t = -1.966* t = -1.021 t = -1.916* t = 1.479 NPLi,t-1 -0.008 0.016 -0.004 -0.003 t = -0.976 t = 0.852 t = -0.396 t = -0.306 LONG_TERM_LOANSi,t-1 -0.038 0.186 0.12 0.006 t = -0.961 t = 3.273*** t = 2.665*** t = 0.186 DEPOSITSi,t-1 0.027 0.016 0.025 -0.006 t = 2.127** t = 2.055** t = 2.992*** t = -0.687 CASH&MKTSECi,t-1 -0.006 -0.051 -0.023 -0.011 116 t = -0.632 t = -3.573*** t = -1.834* t = -1.309 TRADING_ASSETSi,t-1 -0.09 -0.059 -0.022 0.298 t = -2.182** t = -1.443 t = -0.630 t = 1.240 ROAi,t-1 -0.032 0.111 -0.004 -0.053 t = -0.561 t = 1.517 t = -0.066 t = -0.841 Bank Fixed Effects Yes Yes Yes Yes County-year Fixed Effects Yes Yes Yes Yes R2 0.430 0.588 0.499 0.498 Number of bank-county-years 143,039 113,810 232,260 24,589 Panel B: Tests of difference in coefficients across subsamples Regression columns: [1] − [2] < 0? [3] − [4] ≠ 0? MORE_CONSTRAIN_ALLi × POSTt coef diff -0.235 -0.447 z-statistics -1.620 -4.085 One-tailed p-value 0.05 Two-tailed p-value 0.00 Note: 117 Panel A reports the findings from examining whether the negative impact of CECL on bank lending through the capital channel is stronger when banks are publicly listed in a stock exchange. The dependent variable is the log value of small business lending (LOG_SBLi,c,t). The key independent variables include the closeness to capital adequacy constraints (MORE_CONSTRAIN_ALLi), a dummy indicating observations that are during the CECL transition period (POSTt), and various control variables. Regressions [1] ([2]) reports the results obtained from estimating the regression model using banks with regulatory leniency greater (smaller) than the median, respectively. Regressions [3] ([4]) reports the results obtained from estimating the regression model using publicly traded (privately held banks). Variable definitions are presented in Appendix A.2. The regressions are based on the bank-quarters with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and county-year. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. Panel B presents z-tests of differences in coefficients across the different subsample regressions in Panel A. Specifically, the first two rows of Panel B indicate which regression columns are compared and the relevant hypothesis. The third and fourth rows provide the calculated difference of the MORE_CONSTRAIN_ALLi × POSTt coefficients and the associated z-statistics for the indicated regression columns. The last row provides the associated p-value for the significance of difference in the MORE_CONSTRAIN_ALLi × POSTt coefficients. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 118 Table 2.14 CECL's impact on small business lending and regulatory leniency - using a continuous measure Dependent Variable = LOG_SBLi,c,t Independent Variables [1] MORE_CONSTRAIN_ALLi × REG_LENi × POSTt -0.242 t = -0.195 MORE_CONSTRAIN_T1i × REG_LENi × POSTt 0 0 Controls MORE_CONSTRAIN_ALLi × POSTt -0.216 t = -2.125** REG_LENi × POSTt 0.084 t = 0.411 SIZEi,t-1 -0.006 t = -0.596 RES_RE_LOANSi,t-1 0.027 t = 1.760* COM_RE_LOANSi,t-1 -0.023 t = -1.438 COM_NRE_LOANSi,t-1 -0.029 t = -1.637 CONSUMER_LOANSi,t-1 -0.009 t = -0.973 LONG_TERM_LOANSi,t-1 0.103 t = 2.573** NPLi,t-1 0.02 119 t = 2.626*** DEPOSITSi,t-1 -0.02 t = -1.898* CASH&MKTSECi,t-1 -0.022 t = -0.582 TRADING_ASSETSi,t-1 0.011 t = 0.180 ROAi,t-1 -0.022 t = -0.582 Bank Fixed Effects Yes County-year Fixed Effects Yes R2 0.449 Number of bank-county-years 256,849 Note: This table reports the findings from examining whether the negative impact of CECL on bank lending through the capital channel is stronger when banks are state-chartered. The dependent variable is the log value of small business lending (LOG_SBLi,c,t). The key independent variables include the closeness to capital adequacy constraints (MORE_CONSTRAIN_ALLi), the proxy for regulatory leniency (REG_LENi) obtained from Agarwal et al. (2014), a dummy indicating observations that are during the CECL transition period (POSTt), and various control variables. Variable definitions are presented in Appendix A.2. The regressions are based on the bank-quarters with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and county-year. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 120 Table 2.15 Summary of placebo test results Assumed treatment dates How much significance does a placebo test retain? Total (A) How many statistical tests on the coefficients of interests are statistically significant? Table 2.4 Table 2.5 Table 2.6 Table 2.7 Table 2.8 Table 2.9 Table 2.12 Table 2.13 Table 2.14 H1 H2 H3 H4a H4b H4c H4a H4b H4c H5a H5b H5c Add1 Add2 Add3 Main analysis (A divided by 19) 2015Q4 NA 19 4 0 2 1 1 1 1 1 1 0 1 0 4 2 0 Placebo Test 2014Q4 36.84% 7 4 0 0 1 0 0 0 0 0 0 0 0 1 1 0 2013Q4 31.58% 6 4 0 0 0 0 1 0 0 0 0 0 0 0 1 0 2012Q4 31.58% 6 4 1 0 0 0 1 0 0 0 0 0 0 0 0 0 2011Q4 26.32% 5 0 0 2 0 0 0 0 0 0 0 0 0 2 1 0 Note: This table summarizes the results obtained from the placebo test. For the placebo test, the pre-period is 12 quarters before the assumed treatment dates, and the post-period is 12 quarters after the assumed treatment dates. Specifically, I check how many statistical tests on the coefficients of interest are still statistically significant if I replace the 2015Q4 actual approval date of CECL with four different placebo dates. For example, the 2014Q4 placebo test results in a 36.84% retention rate, which is calculated using the following procedures. The 2014Q4 placebo test shows seven coefficients tests that are significant in the same direction as predicted. The seven significant coefficient tests retain 36.84% of the 19 significant tests that I obtain from using the actual CECL approval dates (7/19 = 36.84%). 121 Table 2.16 Does increasing equity mitigate the impact of CECL on lending? Dependent Variable = ∆LOANSi,t Independent Variables [1] [2] [3] [4] CLOSE_TO_CONS_ALLi × POSTt × dEQi 1.028 t = 2.424** MORE_CONSTRAIN_ALLi × POSTt × dEQi 0.154 t = 0.977 CLOSE_TO_CONS_T1i × POSTt × dEQi 1.194 t = 2.248** MORE_CONSTRAIN_T1i × POSTt × dEQi 0.114 t = 0.754 CLOSE_TO_CONS_ALLi × POSTt -2.826 t = -3.275*** MORE_CONSTRAIN_ALLi × POSTt -0.459 t = -2.950*** CLOSE_TO_CONS_T1i × POSTt -3.46 t = -3.450*** MORE_CONSTRAIN_T1i × POSTt -0.428 t = -2.511** dEQi × POSTt -0.574 -0.105 -0.55 -0.089 t = -2.462** t = -1.037 t = -2.239** t = -0.690 Controls SIZEi,t-1 -2.33 -2.321 -2.313 -2.302 t = -4.561*** t = -4.564*** t = -4.635*** t = -4.671*** RES_RE_LOANSi,t-1 -0.152 -0.152 -0.154 -0.155 122 t = -8.843*** t = -9.062*** t = -8.950*** t = -9.130*** COM_RE_LOANSi,t-1 -0.138 -0.138 -0.141 -0.141 t = -4.689*** t = -4.824*** t = -4.854*** t = -4.848*** COM_NRE_LOANSi,t-1 -0.122 -0.123 -0.122 -0.123 t = -3.333*** t = -3.425*** t = -3.361*** t = -3.372*** CONSUMER_LOANSi,t-1 -0.089 -0.095 -0.089 -0.092 t = -1.624 t = -1.736* t = -1.650 t = -1.658 LONG_TERM_LOANSi,t-1 0.028 0.027 0.028 0.027 t = 1.559 t = 1.435 t = 1.579 t = 1.443 NPLi,t-1 -0.427 -0.441 -0.426 -0.45 t = -4.987*** t = -5.074*** t = -5.047*** t = -5.356*** DEPOSITSi,t-1 -0.005 -0.003 -0.005 -0.004 t = -0.234 t = -0.145 t = -0.215 t = -0.171 CASH&MKTSECi,t-1 0.002 0.0001 0.002 -0.0004 t = 0.101 t = 0.003 t = 0.086 t = -0.018 TRADING_ASSETSi,t-1 0.345 0.292 0.356 0.285 t = 1.664 t = 1.273 t = 1.699 t = 1.217 ROAi,t-1 -0.361 -0.389 -0.348 -0.373 t = -0.736 t = -0.799 t = -0.717 t = -0.770 Year-quarter Fixed Effects Yes Yes Yes Yes Bank Fixed Effects Yes Yes Yes Yes R2 0.277 0.276 0.278 0.276 Number of bank-quarters 7,073 7,073 7,073 7,073 Note: 123 This table reports the results from examining whether increasing equity mitigates CECL's impact on loan growth during the CECL transition period. The dependent variable is the loan growth rate (∆LOANSi,t). The key independent variables include the closeness to capital adequacy constraints (measured by CLOSE_TO_CONS_ALLi and CLOSE_TO_CONS_T1i), a dummy indicating observations that are during the CECL transition period (POSTt), the change in total equity to asset ratio during the CECL transition period (dEQi), and various control variables. Regressions [1] and [2] report the results obtained from using the three types of capital ratio to measure the closeness to capital adequacy constraints(CLOSE_TO_CONS_ALLi and whether CLOSE_TO_CONS_ALLi is greater than its median, i.e., MORE_CONSTRAIN_ALLi). Regressions [3] and [4] report the results obtained from using only the Tier 1 capital ratio to measure the closeness to capital adequacy constraints (CLOSE_TO_CONS_T1i and whether CLOSE_TO_CONS_T1i is greater than its median, MORE_CONSTRAIN_T1i). Variable definitions are presented in Appendix A.2. The regressions are based on the bank-quarters with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and year-quarter. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 124 Table 2.17 Does increasing equity mitigate the impact of CECL on long-term lending? Dependent Variable = ∆LONG_TERM_LOANSi,t Independent Variables [1] [2] [3] [4] CLOSE_TO_CONS_ALLi × POSTt × dEQi 0.47 t = 0.365 MORE_CONSTRAIN_ALLi × POSTt × dEQi -0.317 t = -0.715 CLOSE_TO_CONS_T1i × POSTt × dEQi 0.424 t = 0.234 MORE_CONSTRAIN_T1i × POSTt × dEQi -0.321 t = -0.750 CLOSE_TO_CONS_ALLi × POSTt 1.72 t = 0.642 MORE_CONSTRAIN_ALLi × POSTt 0.058 t = 0.105 CLOSE_TO_CONS_T1i × POSTt -0.17 t = -0.055 MORE_CONSTRAIN_T1i × POSTt 0.301 t = 0.537 dEQi × POSTt -0.599 -0.138 -0.493 -0.164 t = -0.825 t = -0.421 t = -0.594 t = -0.440 Controls SIZEi,t-1 -1.916 -1.662 -1.835 -1.702 t = -1.735* t = -1.484 t = -1.624 t = -1.462 RES_RE_LOANSi,t-1 -0.145 -0.142 -0.143 -0.141 125 t = -2.162** t = -2.113** t = -2.111** t = -2.112** COM_RE_LOANSi,t-1 -0.221 -0.224 -0.221 -0.221 t = -2.970*** t = -3.318*** t = -3.173*** t = -2.965*** COM_NRE_LOANSi,t-1 -0.037 -0.039 -0.039 -0.039 t = -0.372 t = -0.395 t = -0.400 t = -0.401 CONSUMER_LOANSi,t-1 -0.261 -0.264 -0.263 -0.27 t = -1.201 t = -1.455 t = -1.369 t = -1.421 LONG_TERM_LOANSi,t-1 -0.435 -0.44 -0.437 -0.439 t = -5.988*** t = -6.401*** t = -5.749*** t = -5.995*** NPLi,t-1 -0.295 -0.31 -0.287 -0.318 t = -1.316 t = -1.373 t = -1.303 t = -1.402 DEPOSITSi,t-1 0.029 0.025 0.025 0.029 t = 0.563 t = 0.479 t = 0.489 t = 0.558 CASH&MKTSECi,t-1 0.003 -0.005 -0.00003 -0.002 t = 0.043 t = -0.092 t = -0.0004 t = -0.037 TRADING_ASSETSi,t-1 -0.995 -1.009 -0.965 -0.994 t = -1.962* t = -1.826* t = -1.982* t = -1.779* ROAi,t-1 -3.37 -3.325 -3.327 -3.372 t = -2.084** t = -2.056* t = -2.071** t = -2.074** Year-quarter Fixed Effects Yes Yes Yes Yes Bank Fixed Effects Yes Yes Yes Yes R2 0.132 0.132 0.132 0.132 Number of bank-quarters 7,073 7,073 7,073 7,073 Note: 126 This table reports the results from examining whether increasing equity mitigates CECL's impact on long-term loan growth during the CECL transition period. The dependent variable is the growth rate of long-term loans (∆LONG_TERM_LOANSi,t). The key independent variables include the closeness to capital adequacy constraints(measured by CLOSE_TO_CONS_ALLi and CLOSE_TO_CONS_T1i), a dummy indicating observations that are during the CECL transition period (POSTt), the change in total equity to asset ratio during the CECL transition period (dEQi,2015Q4-2018Q2), and various control variables. Regressions [1] and [2] report the results obtained from using the three types of capital ratios to measure the closeness to capital adequacy constraints(CLOSE_TO_CONS_ALLi and whether CLOSE_TO_CONS_ALLi is greater than its median, i.e., MORE_CONSTRAIN_ALLi). Regressions [3] and [4] report the results obtained from using only the Tier 1 capital ratio to measure the closeness to capital adequacy constraints(CLOSE_TO_CONS_T1i and whether CLOSE_TO_CONS_T1i is greater than its median, i.e., MORE_CONSTRAIN_T1i). Variable definitions are presented in Appendix A.2. The regressions are based on the bank-quarters with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and year-quarter. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 127 Table 2.18 Does increasing equity mitigate the impact of CECL on different types of lending? Dependent Variable = ∆TOT_RE_LOANSi,t ∆COM_NRE_LOANSi,t ∆CONSUMER_LOANSi,t ∆RES_RE_LOANSi,t ∆COM_RE_LOANSi,t Independent Variables [1] [2] [3] [4] [5] CLOSE_TO_CONS_ALLi × POSTt × dEQi 1.071 0.037 2.093 0.732 1.167 t = 1.867* t = 0.023 t = 1.340 t = 1.006 t = 2.061* CLOSE_TO_CONS_ALLi × POSTt -3.059 -1.029 1.279 -3.351 -2.872 t = -2.549** t = -0.393 t = 0.382 t = -2.088** t = -2.084** dEQi × POSTt -0.61 0.134 -1.17 -0.61 -0.554 t = -1.941* t = 0.148 t = -1.387 t = -1.496 t = -1.580 Controls SIZEi,t-1 -2.132 -4.084 -4.595 -1.539 -2.847 t = -4.303*** t = -5.283*** t = -3.556*** t = -2.455** t = -4.965*** RES_RE_LOANSi,t-1 -0.19 -0.063 -0.075 -0.306 -0.058 t = -9.479*** t = -1.111 t = -0.741 t = -8.679*** t = -1.863* COM_RE_LOANSi,t-1 -0.23 -0.058 -0.181 -0.091 -0.383 t = -6.947*** t = -0.874 t = -1.518 t = -2.358** t = -8.319*** COM_NRE_LOANSi,t-1 0.024 -0.733 0.22 0.033 0.011 t = 0.574 t = -8.204*** t = 2.729** t = 0.689 t = 0.253 CONSUMER_LOANSi,t-1 0.017 0.04 -1.167 -0.13 0.097 t = 0.293 t = 0.280 t = -4.179*** t = -2.163** t = 1.311 LONG_TERM_LOANSi,t-1 0.017 0.044 0.155 0.005 0.033 t = 0.970 t = 1.418 t = 2.769** t = 0.234 t = 1.228 NPLi,t-1 -0.314 -0.509 -0.619 -0.258 -0.393 128 t = -4.248*** t = -2.271** t = -2.078** t = -2.690** t = -3.964*** DEPOSITSi,t-1 0.014 -0.023 -0.134 0.044 -0.003 t = 0.753 t = -0.452 t = -2.424** t = 1.898* t = -0.088 CASH&MKTSECi,t-1 -0.005 -0.049 -0.034 -0.022 -0.01 t = -0.304 t = -0.861 t = -0.393 t = -0.845 t = -0.325 TRADING_ASSETSi,t-1 -0.181 -0.037 0.914 -0.143 -0.116 t = -0.679 t = -0.050 t = 1.948* t = -0.436 t = -0.460 ROAi,t-1 -0.777 -1.346 -0.255 -0.5 -0.399 t = -1.339 t = -1.378 t = -0.167 t = -0.833 t = -0.685 Year-quarter Fixed Effects Yes Yes Yes Yes Yes Bank Fixed Effects Yes Yes Yes Yes Yes R2 0.256 0.158 0.121 0.225 0.192 Number of bank-quarters 7,073 7,073 7,073 7,073 7,073 Note: This table reports the results from examining whether increasing equity mitigates CECL's impact on the growth of different types of loans during the CECL transition period. The dependent variable is the loan growth rate for real estate loans, residential real estate loans, commercial real estate loans, commercial loans, and consumer loans (∆TOT_RE_LOANSi,t-1; ∆RES_RE_LOANSi,t-1; ∆COM_RE_LOANSi,t-1; ∆COM_NRE_LOANSi,t; and ∆CONSUMER_LOANSi,t). The key independent variables include the capital inadequacy risk (measured by MORE_CONSTRAIN_ALLi), a dummy indicating observations that are during the CECL transition period (POSTt), the change in total equity to asset ratio during the CECL transition period (dEQi), and various control variables. Regressions [1], [2], [3], [4], and [5] report the results from using the loan growth rate for real estate loans, residential real estate loans, commercial real estate loans, commercial loans, and consumer loans, respectively, as the dependent variables. The number of observations in column [5] is equal to 7,512, which is lower than the number of observations in other columns. This is because commercial real estate loans occasionally have a zero beginning of the quarter balance, leading to a missing value for their growth rates. Variable definitions are presented in Appendix A.2. The regressions are based on the bank-quarters with required data in the period of 2013Q1–2018Q4. t-statistics are reported below the coefficients and based on standard errors clustered by bank and year-quarter. ∗∗∗, ∗∗, and ∗ denote statistical significance at the two-tailed 0.01, 0.05, and 0.1 levels, respectively. The boldface font indicates the estimates for the variable of interest. All continuous variables are winsorized at the 1% and 99% levels. Variable definitions are presented in Appendix A.2. 129 Chapter 3: Bond Liquidity and Voluntary Disclosures: Evidence from the Volcker Rule 3.1 Introduction Well-functioning capital markets are an important hallmark of a strong and healthy economy, giving firms access to capital at best possible price. One required element for capital markets to be well functioning is a rich information environment that facilitates price efficiency. Banks have an important role in this process, gathering funds from depositors and facilitating its distribution to worthy borrowers. However, before the financial crisis, banks were doing more than lending to their customers. They were also engaged in proprietary trading83 on their own account, exposing them to additional risk backstopped by the FDIC deposit insurance program. After the financial crisis, regulators introduced several regulatory reforms to make the banking system more transparent and to reduce the likelihood of future financial crises. Among the many regulatory changes introduced following the crisis, few are more controversial than the Volcker Rule. Enacted as part of the Dodd-Frank Act in April 2014, the Volcker Rule prohibits proprietary trading in an effort to limit bank risk-taking. The rule reduces the ability of banks to hold inventories of securities, lowering their incentives to gather information and convey that information to the markets through their trades. In addition, as banks can no longer hold inventories of securities, the Volcker Rule reduces the ability of banks to provide liquidity in corporate bond markets by taking the other side of trades that their customers request. A recent commentary by market participants indicates that liquidity has indeed deteriorated since the 83 Proprietary trading occurs when a bank invests for its own direct gain instead of buying and selling on behalf of its clients to earn commissions. 130 Volcker Rule, with a 2016 Wall Street Journal article84 noting that “Three-quarters of institutional bond investors say that liquidity provided by bond dealers has declined in the past year.” This paper aims to investigate whether the loss of liquidity caused by the Volcker Rule impacts the actions of firms that issue bonds. After the rule change, affected bond dealers (i.e., bond dealers affiliated with government-insured banks) have reduced their market-making services, and they hold fewer bond inventories (see Schultz 2017; Bessembinder, Jacobsen, Maxwell, and Venkataraman 2018), reducing their incentive to gather information on bond issuers. In turn, bond trading prices are expected to become less informative, increasing the potential for information asymmetry between investors. Also, since the ability of these dealers to provide immediacy has decreased, bond investors need more time to find counterparties to trade with. These increased trading frictions in bond markets discourage investors from lending, increasing a firm’s cost of borrowing. As a result, firms are likely to increase their voluntary disclosures to fill this information vacuum and mitigate Volcker Rule’s negative impact on their debt financing. Theoretical models such as Diamond (1985) and Diamond and Verrecchia (1991) show that when there is information asymmetry between investors, firms are likely to commit to disclosing more information than mandated to reduce the information asymmetry. Viewing the loss of bond liquidity following the Volcker Rule as an information problem that significantly increases the frictions in public debt markets, we predict that after the Volcker Rule is implemented, firms that rely on bond financing are more likely to increase their voluntary disclosures. 84 Wall Street Journal (2016/3/31), “Big Bond Investors Say Liquidity Has Declined in Past Year”. 131 It is also possible that firms relying on bond financing increased their voluntary disclosures following the Volcker Rule to encourage non-Volcker-affected dealers (dealers not affiliated with government-insured banks) to engage in market-making. Relative to dealers affected by the Volcker Rule (such as J.P. Morgan, Goldman Sachs), unaffected dealers are on average smaller in size (Bao, O’Hara, and Zhou 2018), so they have less ability to take bond inventory risk. Also, these unaffected dealers are unlikely to be affiliated with a commercial bank, which has private information channels (e.g., through a syndicated loan network) that can be used to assess a bond issuer’s default risk. The unaffected dealers will become willing to participate in market making for a particular corporate bond only when the information environment of the bond is transparent enough to reduce the dealers’ inventory risk to an acceptable level. Thus, if bond issuers aim at encouraging more non-affected dealers to replace the liquidity previously provided by the Volcker Rule affected dealers, bond issuers are likely to disclose more forward-looking information to create a more transparent information environment. We use a difference-in-difference design to test our prediction that firms affected by the Volcker Rule will increase their voluntary disclosures. The treatment sample consists of firms that issue public bonds, while the control sample consists of firms that do not. The treatment time is the implementation year of the Volcker Rule. The full sample contains 30,365 firm-quarters (for 1,971 unique firms) from eight quarters before to eight quarters after the year when Volcker Rule was enacted. The Volcker Rule came into effect in April 2014. Therefore, we exclude all firm-quarters in 2014. The difference-in-difference test is implemented as follows. First, we examine how all firms change their tendency to provide forecasts from before versus after the implementation of the Volcker Rule. Then we compare these changes in willingness to provide 132 forecasts across firms with and those without public bonds. The results show a differential increase in willingness to forecast for firms with public bonds, consistent with the liquidity shock caused by the Volcker Rule inducing firms to increase their disclosures. Several additional sources of evidence strengthen our inferences. First, the theory leads to a differential prediction regarding which bond issuing firms are more likely to increase their voluntary disclosures after the Volcker Rule. Firms with very high credit ratings (i.e., AAA and AA) are much less likely to be affected by the decreased bond liquidity caused by the Volcker Rule. These firms have a rich information environment and low uncertainty. It is relatively straightforward to find counterparties that are willing to trade these high-quality securities. Thus, the bank-dealer liquidity of these high-rated bonds does not play a significant role in their bond pricing (see Dick-Nielsen, Feldhutter, and Lando 2012; Bao, Pan, and Wang 2011 for empirical evidence). It is also unlikely that firms with low credit ratings (i.e., firms with high yield bonds) will increase their voluntary disclosures in response to the Volcker Rule, as explained below. Even if commencing voluntary disclosures can lower the yield to maturity of bonds issued by these firms, the high uncertainty they face makes it too costly for them to disclose. The high uncertainty can render their disclosures inaccurate, damage the managers’ reputations, and lead to lawsuits when the disclosures are incorrect (see Kim, Pandit, and Wasley 2015). In this situation, the disclosure cost is likely to be greater than the benefit, discouraging these firms from increasing their disclosures. Thus, we predict that the increased forecast tendency, due to the Volcker Rule, is likely to originate from firms with intermediate credit ratings rather than from firms with very high or very low ratings. The results are consistent with this prediction. Furthermore, firms that issue public debt are likely to be systematically different from firms that do not use public debt. It’s possible that other confounding factors (e.g., future growth 133 opportunities) simultaneously influence both disclosure policy and capital structure. To mitigate endogeneity concerns, we follow prior studies (e.g., Faulkender and Petersen 2005 and 2012; Harford and Uysal 2014) and use three predictors of whether a firm issues corporate bonds as instruments85 for our main test. Our results still hold, consistent with the notion that when facing the bond liquidity shock caused by the Volcker Rule, firms that rely on public bonds increased their disclosures more than firms not using public bonds. This paper contributes to the existing literature on how firms change their disclosure policy when facing an exogenous change in market structure. For example, Li and Yang (2015) find that firms increase their voluntary disclosures after the mandatory adoption of International Financial Reporting Standards (IFRS). Balakrishnan, Billings, Kelly, and Ljungqvist (2014) show that firms respond to an exogenous loss of analyst coverage by providing timelier and more informative earnings guidance. Lo (2014) documents that borrowers whose banking relationships are threatened by declining bank health increase their public disclosures of forward-looking information. We extend these studies by examining how firms adjust their disclosure policies in response to the bond liquidity shock caused by the Volcker Rule. The results are among the first to show the importance of considering the impact of a trading mechanism change on a firm’s public disclosure policy. This paper also contributes to studies on the association between voluntary disclosures and debt markets, such as studies on how management guidance impacts CDS86 pricing 85 These instruments include whether the firm is included in the Standard & Poor’s (S&P) 500 index, whether it is listed on the NYSE, and the percentage of firms with ratings in the firm’s industry grouping. 86 A credit default swap (CDS) is a financial swap agreement that the seller of the CDS will compensate the buyer (usually the creditor of the reference loan) in the event of a loan default (by the debtor) or other credit event. CDS prices are higher when the reference debt has a greater default risk. 134 (Shivakumar, Urcan, and Zhang 2011) and credit ratings (Lin 2015). While prior studies focus on the market consequence of management forecasts, our paper investigates the determinants of issuing management forecasts. 3.2 Background and hypothesis development This paper examines whether firms adjust their disclosure strategy in response to an exogenous loss of bond liquidity caused by the Volcker Rule. Affected firms will increase their voluntary disclosures only if the benefits of doing so (such as a lower cost of debt) are greater than the potential costs (e.g., reputation damage due to forecast errors and proprietary costs due to releasing information to competitors). To understand the benefits and costs of voluntary disclosures, we begin by discussing the reasons why firms issue bonds and the role of dealers in corporate bond markets. Then we discuss how the Volcker Rule reduces the options for trading corporate bonds and potentially increases the cost of borrowing in public debt markets. Finally, we develop hypotheses on why the negative shock to bond liquidity caused by the Volcker Rule is expected to force firms to change their disclosure policies. 3.2.1 Reasons why firms issue bonds When firms raise capital to finance their projects, they have three options – equity, private debt, and public debt. Young firms, or start-ups, are more likely to rely on equity financing because their high uncertainty and default risk prohibit them from issuing debt at a cost that maximizes the returns to existing equity holders. In contrast, more mature firms that generate steady cash flows can obtain lower-cost bank loans and issue bonds to optimize their weighted average cost of capital. These firms can raise debt at a lower cost because their future cash flows are more predictable based on the existing set of information available for these firms. 135 Firms that issue debt must choose between issuing private debt, such as bank loans, or issuing corporate bonds in public markets. When choosing between alternative forms of borrowing, firms are likely to rely more on bank loans and other forms of private debt if their borrowing requirements are modest or when they cannot overcome the information asymmetry in public markets. If the amount of new financing a firm requires is small, the fixed costs of going to public debt markets, including legal costs and SEC filing costs, tend to make it unattractive to issue public bonds. Private loans often enjoy an informational advantage for two reasons. If the loan is issued by the borrower’s bank, then the bank will already have information about the customer’s creditworthiness. In addition, borrowing from a single lender (or small group of lenders) means that the borrower can disclose proprietary information to the lender without revealing it to the public (Krishnaswami, Spindt, and Subramaniam 1999). But for larger loan issues, public debt can be more cost-effective than private debt if the interest rate in public debt markets is below the interest rate on private borrowing by enough to offset the fixed costs and disclosure costs of issuing public debt. The interest rate paid on private loans includes a liquidity premium that is typically larger than the liquidity premium on public debt.87 If information asymmetry is not a major concern, the marginal cost of an additional dollar of public debt is typically less than that of private debt. So as the scale of a firm’s demand for debt increases, the lower interest rate on public debt can more than compensate for the fixed costs of issuing public debt. Other positive factors make bonds less costly than bank loans. Rajan (1992) models a possible scenario. A bank can monitor a firm and control its investment decisions. However, in 87 The liquidity premium on various types of public debt is discussed further in section 2.5. 136 doing this, the bank alters the division of surpluses between itself and the firm. This distorts the firm’s incentives, creating frictions that can lead the firm to prefer public debt. Diamond (1991) analytically explores this scenario, finding that if the information asymmetry between managers and outside capital markets is sufficiently severe, new borrowers will begin to build their reputation by borrowing from banks. Once they have demonstrated their creditworthiness, the firm will switch to issuing public debt. Thus, once the firm builds its reputation, bonds become a less costly source of financing than bank loans. Furthermore, Diamond (1996) argues that although bank loans are easier to restructure than corporate bonds, banks themselves must bear intermediation costs (e.g., the costs of monitoring and complying with regulatory capital requirements), which ultimately must be borne by borrowers. Thus, when the financial intermediation costs are too high, firms will prefer bond financing. In sum, most of the tradeoffs between public and private debt center around the idea of information asymmetry between firms and their bond investors. Corporate bonds are likely to be a cheaper source of capital than bank debt if firms can effectively communicate important information to outside investors, alleviating concerns about default. To do this, firms must maintain an information environment that limits information asymmetry with outsiders. A firm’s public information environment comes from two sources – public disclosures made by the firm and information collected and disseminated by intermediaries. Public disclosures consist of mandatory disclosures, such as audited financial statements, and voluntary disclosures, such as management forecasts. Financial information intermediaries include a firm’s underwriters in 137 primary markets, bond dealers in secondary markets, and credit rating agencies.88 Our research question is how firms adjusted their disclosure policies in response to the Volcker Rule, which substantially altered bank bond dealers’ ability to provide bond liquidity and, in doing so, convey information. To understand why this rule change is likely to induce firms to change their disclosure strategies, we first discuss the role of dealers in bond markets. 3.2.2 The role of dealers in corporate bond markets The secondary market for corporate bonds works very differently from equity markets. While equity markets are made up of multiple exchanges that consolidate quotes and orders of market participants, corporate bonds are traded in decentralized over-the-counter (OTC) markets. Investors in these markets trade their corporate bonds via a network of dealers. A customer, who intends to buy or sell her bonds, contacts a dealer, who either acts as an agent and seeks another customer (or another dealer) who is willing to sell or buy the bonds or trades as a counterparty with the customer. In these markets, pre-trade price quotes are only available upon request. As a result, bond dealers are better informed about order flows than their customers. Moreover, since dealers also profit from holding bond inventories and trading with their customers, they have an incentive to gather information about the firms whose bonds they hold. Hence, bond dealers not only provide counterparty-search services, but they also convey their private information about a bond’s value and order flow to the market through their trades. Immediately following the financial crisis, dealers affiliated with government-insured banks 88 For brevity, we will not discuss the information roles of bond underwriters and credit rating agencies in bond markets because our research question is related the liquidity functions of bond dealers. 138 were responsible for more than 90% of all bond trading volume (see Bessembinder et al. 2018, P.1643). 3.2.3 How the Volcker Rule affects corporate bond markets The Volcker Rule was implemented on April 1, 2014, as part of the Dodd-Frank Act, prohibiting government-insured banks and their affiliates from engaging in risky proprietary trading. After the Volcker Rule, insured banks were no longer allowed to use their own funds to buy, sell, and hold securities to increase their profits. The purpose of the Volcker Rule is to prevent a repeat of the 2008 financial crisis by discouraging banks from taking on too much risk. Regulators recognize that insured banks are the primary market makers for many fixed-income securities (e.g., corporate bonds), and they play a critical role in providing liquidity to these markets. Thus, the Volcker Rule permits market-making by banks but prohibits banks from trading securities to earn a profit. However, because both activities require banks to hold inventories, the distinction between market-making and proprietary trading is blurred. In practice, Volcker compliance requirements still affect a bank dealer’s inventory management and thus their ability to take the other side of the trades they are orchestrating. Under the Volcker Rule, insured banks must report several quantitative measures that regulators use to determine whether they engage in proprietary trading. Banks are expected to conduct their transaction behaviors to minimize the appearance of proprietary trading implied by these metrics. One measure banks must report is the standard deviation of daily trading profits and losses. This discourages them from holding large positions that could generate significant variation in daily trading profits. They are also required to report inventory turnover calculated over 30, 60, and 90 days. Faster inventory turnover is an indication that the dealer is making markets rather than engaging in proprietary trading. Another metric that dealers must report is 139 the customer-facing trade ratio, defined as the ratio of trades with customers to trades with other dealers. This ratio is calculated using both the number of trades and the trading volume. A high ratio of customer trading volume to interdealer trading volume suggests that the dealer is making markets rather than engaging in proprietary trading. So, the Volcker Rule mandates strict inventory control but limits a vital tool for dealers to manage their inventory – their interdealer trades. In sum, bank-affiliated dealers are expected to provide less liquidity in bond markets after the Volcker Rule. They worry that holding extensive bond inventories (or trading high volumes with other dealers) for market-making distorts these reporting metrics in a way that raises the concern of regulators and brings additional scrutiny. Prior studies find evidence consistent with the Volcker Rule affecting bank dealers’ ability to provide bond liquidity. Schultz (2017) shows that since the implementation of the Volcker Rule, bond dealers have become more reluctant to take bonds into their inventory, more likely to offset trades within a day, and they unwind their inventory positions more quickly. Bao et al. (2018) show that corporate bond liquidity has decreased after the Volcker Rule, and dealers regulated by the rule have reduced their market-making activities. These affected dealers exhibit an economically and statistically significant increase in agency trades, that is, trades in which the dealer prearranged an offsetting trade to avoid the inventory risk. They also have committed significantly less inventory capital to market-making. 3.2.4 How firms that issue bonds respond to the Volcker Rule Given the importance of bank-affiliated dealers in corporate bond markets, the Volcker Rule is expected to have a major negative impact on bond market liquidity. After the rule change, bond investors are expected to find it harder to trade their corporate bonds. They take 140 more time to search for a counterparty to trade with89 because affected dealers are now less willing to take the other side of their trade. If bond investors are subject to uncertainty and cannot transact quickly, they can incur significant losses while hunting for a counterparty. Moreover, Volcker Rule restrictions also bar bank-affiliated dealers from trading in corporate bonds to make a profit. As a result, these dealers are now less likely to gather information about bond issuers and convey this information to the markets through their trading activities. Therefore, information asymmetry among investors is expected to be higher after the Volcker Rule, reducing bond prices and increasing yields. If these increases in trading frictions raise required returns, this will cause firms’ cost debt to increase, thus, changing the allocation of resources in the economy. Prior studies on corporate bond markets find evidence consistent with this argument. Dick-Nielsen, Feldhutter, and Lando (2012), and Bao, Pan, and Wang (2011) find that bond liquidity (or the ease of trading bonds) is associated with bond returns. More liquid bonds have lower yield spreads, implying a lower cost of debt. Qi and Wang (2017) document that firms with higher liquidity of their existing corporate bonds enjoy contract terms that are more favorable when issuing new bonds. Thus, when firms issuing bonds want to mitigate the Volcker Rule’s negative impact on their financing, they are likely to increase their voluntary disclosures in order to reduce information asymmetry and increase the liquidity of their bonds. The increased disclosures help the bond issuers to fill the information vacuum created by the decreased participation of bank-affiliated dealers in the secondary bond markets. As mentioned previously, increased voluntary disclosures can also make non-Volcker-affected dealers more willing to hold bonds and expand their dealer activities. Thus, if bond 89 See Wall Street Journal (2016/3/31), “Big Bond Investors Say Liquidity Has Declined in Past Year”. 141 issuers aim to encourage more non-affected dealers to replace the affected dealers as liquidity providers, bond issuers are likely to disclose more forward-looking information to create a more transparent information environment. Lastly, Lo (2014) finds that when a firm’s banking relationship is threatened due to its bank’s declining ability to provide loans, the borrower must turn to other funding sources. To reduce the information asymmetry with other potential capital providers, such firms increase their voluntary disclosures. He also argues that those firms are likely to voluntarily disclose more to satisfy current investors (e.g., current shareholders). The reasoning is that since current investors do not know a firm’s ability to find alternative funding, banking shocks can lead investors to demand more information about the firms’ prospects due to concerns about disruptions in the firm’s operating performance. The same logic can be applied to the bond market. When facing a bond liquidity shock and an increasing cost of debt, firms are likely to consider turning to new funding sources. Thus, to reduce the information concerns of alternative capital providers, firms will voluntarily disclose more. Moreover, to minimize the worries of current capital providers about the firms’ ability to obtain new funding, firms are likely to reveal more forward-looking information. The first hypothesis is summarized below: H1: When facing bond liquidity shocks caused by the Volcker Rule, bond issuing firms increase their voluntary disclosures. 3.2.5 Do firms react to the Volcker Rule differently? This section discusses how firms with different credit ratings are likely to respond to the Volker Rule differently. Firms with excellent credit ratings (i.e., AAA and AA) are unlikely to be affected by the decreased bond liquidity caused by the Volcker Rule. These firms have a rich information environment and low uncertainty. The liquidity of bonds issued by these firms does 142 not rely heavily on the ability of government-insured bank dealers to hold their bonds in inventory for market making. Instead, it’s relatively straightforward for bond investors to find counterparties willing to trade, so dealers are likely to act as brokers between buyers and sellers to facilitate the trade. Even though after the Volcker Rule, the bond dealers are less able to hold bond inventories, high-rated firms are not affected due to their rich information environment and low uncertainty. Firms with poor credit ratings (i.e., firms with high yield bonds) face greater uncertainty and are subject to more information asymmetry. Bonds issued by these firms rely heavily on bond dealer market-making. When a seller of these bonds needs to sell a security quickly, their high degree of information asymmetry makes it difficult to find a counterparty willing to trade. As a result, the seller needs to sell their bonds to a dealer who buys in anticipation of being able to unwind the position later. Thus, a dealer’s ability to hold bond inventories is a critical factor that helps to facilitate the trade of bonds issued by firms with poor credit ratings. After the Volcker Rule, bank dealers are less able to hold bond inventories and provide liquidity for these bonds, so the rule change is expected to impact firms with poor credit ratings negatively. According to the finance literature on the structure of bond yields, the higher an issuer’s default risk, the higher the liquidity component of its bond yield. This is because higher information asymmetry and uncertainty generate greater dispersion of beliefs about bond prices. Thus, investors who invest in bonds in a highly uncertain environment tend to put a high valuation weight on the ease with which they can trade their bonds. Consistent with this theory, Dick-Nielsen et al. (2012, P480) find that 3 percent of the total yield spreads of AAA bonds is due to the liquidity component, but for A and BBB bonds, the liquidity contribution to yield increases to 11 and 8 percent, respectively. For the high yield bonds (BB and below), the 143 liquidity component accounts for 24 percent of their yields. In sum, since the Volcker Rule lowers bond liquidity as previously discussed, and the pricing of bonds with lower credit ratings is more sensitive to liquidity concerns, the Volcker Rule is likely to affect firms with poorer credit ratings more seriously. As we can see, the impact of the Volcker Rule on firms that issue bonds is likely to be negatively associated with their credit ratings. The lower their credit ratings, the greater the Volcker Rule’s impact on the liquidity of their corporate bonds. However, unlike the Volcker Rule’s negative effect on bond liquidity, the disclosure response to this rule change is expected to be nonlinear with ratings. Firms with excellent credit ratings are not affected by Volcker Rule, so they are unlikely to respond by changing their disclosure policies. On the other hand, for firms with poor credit ratings, increasing voluntary disclosures is likely to have very little impact on their bond yield spreads. The greater uncertainty that they face is likely to render their disclosures inaccurate, damaging their managers’ reputations and even prompting lawsuits (e.g., Kim et al. 2015). In this situation, the disclosure cost is likely to be greater than the benefit, discouraging these firms from increasing disclosure. Therefore, since the increased willingness to increase voluntary disclosures after the Volcker Rule is unlikely to originate from firms with excellent or poor credit ratings, we predict that the increased disclosures will come from the firms in the middle rating classes. The hypothesis is formalized as follow: H2: The tendency of bond issuing firms to increase voluntary disclosures following the Volcker Rule originates from firms with intermediate credit ratings rather than from firms with either high or low ratings. 144 3.3 Research design 3.3.1 Identifying treatment firms and post-treatment period To test the relation between the Volcker Rule and voluntary disclosures, we apply a difference-in-difference design around the time of the rule’s implementation in April 2014. Accordingly, we include quarters that ended in 2012 and 2013 as the pre-treatment period and those that ended in 2015 and 2016 as the post-treatment period. The quarters that ended in 2014 (the transition year) are excluded because we are unsure when firms affected by the Volcker Rule changed their voluntary disclosure policies during this year. Conceptually, the treatment sample consists of firms that rely on bond financing, and the control sample consists of firms that do not. We use whether a firm has an issuer credit rating reported on COMPUSTAT to indicate whether the firm uses bond financing. Prior studies find that the correspondence between having a credit rating and having public debt is high.90 By combining COMPUSTAT and the FISD91 database, we find that during our sample quarters (2012Q1-2013Q4 and 2015Q1-2016Q4), 86 percent of firm-quarters with credit ratings have outstanding public debt. In contrast, 91 percent of firms without ratings do not issue any bonds. Although a firm has a credit rating that does not perfectly identify firms with outstanding public bonds, any misclassification is expected to bias against our hypothesis. For example, suppose the treatment firms (those who rely on bond financing) are misclassified as control firms. In that 90 For example, Cantillo and Wright (2000) examine a sample of COMPUSTAT firms and find that 97.6% of firms with public debt have a credit rating and 99.7% of firms with a reported credit rating also have public debt outstanding. 91 The Mergent Fixed Income Securities Database (FISD) for academia is a comprehensive database of publicly offered U.S. bonds. FISD contains issue details on over 140,000 corporate bonds, corporate MTN (medium term note), supranational, U.S. Agency, and U.S. Treasury debt securities. FISD provides details on debt issues and the issuers, as well as transactions by insurance companies. 145 case, such misclassification will drive the control group disclosure probability toward the treatment group, lowering the results we can find in the difference-in-difference setting. The same logic can also be applied if the control firms are misclassified as the treatment firms. 3.3.2 Regression models 3.3.2.1 The regression model for H1 To test the Volcker Rule’s impact on the likelihood of firms’ voluntary disclosures, we estimate the following specification: VDISCi,t = β0 + β1Postt*Have_rati + Σ βj Controlsi,t + Firm_FEi + Time_FEt + εi,t, (1) where the dependent variable VDISCi,t represents a proxy for voluntary disclosures. We use two variables, MF_all_dumi,t, and MF_alli,t, to measure the level of voluntary disclosures. MF_all_dumi,t is a dummy variable set to 1 if a firm issues at least one management guidance regarding its quarterly performances (e.g., EPS, sales, earnings before interest and tax, etc.) during the quarter, and zero if they do not. MF_alli,t is a count variable measuring the total number of management guidance items issued during the quarter.92 Postt is an indicator set to one for the quarters after the implementation of the Volcker Rule (i.e., quarters 2015Q1 to 2016Q4). Have_rati is an indicator that equals one if firm i has an S&P issuer credit rating at the end of the year 2013. The coefficient on Postt*Have_rati captures the effect of the Volcker Rule bond liquidity shock on a firm’s voluntary disclosures. Hypothesis H1 predicts that β1 will be positive. Controlsi,t is a set of firm-specific time-varying control variables found in the literature to explain disclosure choices (e.g., Lo 2014; Li and Yang 2017). Each variable is defined in 92 See appendix B.2 for a list of the possible types of quarterly management forecasts issued by firms in the sample. 146 appendix B.1. We include firm fixed effects to control for time-invariant unobserved correlated variables and time fixed effects (including the year and fiscal-quarter fixed effects) to capture the influence of aggregate time-series trends. Since the firm and time fixed effects are included, Have_rati and Postt are subsumed in the regression model. The standard errors are clustered across firms in a quarter to account for the common macroeconomic factors at each time point. We use management forecasts as our proxy for voluntary disclosures because they are considered to be an effective disclosure tool. Prior researchers report that price reactions to forecasts are significant in both debt (Shivakumar et al. 2011; De Franco, Vasvari, and Wittenberg-Moerman 2009) and equity (Balakrishnan et al. 2014; Pownall, Wasley, and Waymire 1993) markets. The significant price reactions suggest that investors use the information in forecasts to update their beliefs on a firm’s default risk and prospects. Prior studies also show that a firm’s management guidance increases stock liquidity (Balakrishnan et al. 2014), the precision of investors’ information about future earnings (Baginski, Conrad, and Hassell 1993), and consensus of analyst earnings forecasts (Clement, Frankel, and Miller 2003). These findings indicate that firms facing increased uncertainty can issue forecasts to lower information asymmetry. In addition, Lin (2015) finds that management forecasts are associated with the likelihood of rating changes. All of these things combined make it clear that management forecasts are important means of voluntary disclosure. Rather than using a nonlinear model for our non-continuous dependent variable, we estimate equation (1) using a linear probability model. The reasons are twofold. Nonlinear models tend to produce biased estimates in panel data sets with a short time series and many fixed effects, leading to an incidental parameters problem and inconsistent estimates (Naughton, 147 Rusticus, Wang, and Yeung 2019). Also, nonlinear fixed effects models generate biased estimates for interaction terms93, which are the main coefficients of interest in this study. To ensure that the main result is not sensitive to this research design choice, we repeat our primary analysis using both logit and probit specifications and obtain the same conclusions. Following the prior literature (e.g., Lo 2014; Li and Yang 2015), we control for firm-specific disclosure determinants. These include firm size, measured by the log value of a firm’s market value of equity (LnMV); firm growth, measured by the market-to-book ratio (MTB); firm performance, captured by return on assets (ROA), the occurrence of losses (LOSSDUM), and buy-and-hold size-adjusted returns (BHAR); and firm risk, captured by equity beta (BETA), operating cash flow volatility (CFVOL), and return volatility (RETVOL). In addition, we include additional controls for institutional ownership (INST), analyst following (LnAF), proprietary costs (HERFIN), and leverage (LEV). All control variables are measured in the quarter before the choice of disclosure. Appendix B.1 provides variable definitions. 3.3.2.2 The regression model for H2 Hypothesis H2 predicts that the increased voluntary disclosures due to the Volcker Rule originate from firms with intermediate credit ratings rather than from firms with either high or low ratings. To test the impact of the Volcker Rule on firms with different credit ratings, we use the following specification: VDISCi,t = β0 + β Postt*Rating_indicatori +γ Controlsi,t + Firm_FEi + Time_FEt + εi,t, (2). 93 See Ai and Norton (2003), and Duchin and Sosyura (2014). 148 Rating_indicatori is a vector for the indicators of different credit ratings at the end of the year 2013. For example, the credit rating variable AAA is the first element of this vector, and AA is the second element. Thus, the variable AA equals one if a firm has an AA+, AA, or AA- credit rating at the end of 2013. And the same logic applies to other credit rating classes such as A, BBB, BB, etc. As in equation (1), the control group includes firms without credit ratings. Each element in the β vector is intended to measure the impact of the bond liquidity shock on voluntary disclosures of firms in each credit rating class. The control variables are the same as for equation (1). 3.4 Sample, Descriptive Statistics and Main Result 3.4.1 Data Source and Sample We collect the data for our main analysis from the intersection of the COMPUSTAT, CRSP, IBES, Capital IQ, and Thomson-Reuters Institutional Holdings databases. We begin by obtaining the firm characteristics (including S&P issuer credit rating and financial statement variables) in each of our sample quarters (2012Q1-2013Q4 and 2015Q1-2016Q4)94 from COMPUSTAT. Next, we merge this data with data from CRSP to obtain the return-related variables such as beta and return volatility. Next, the COMPUSTAT/CRSP data is combined with data from the IBES dataset to obtain analyst following, with Capital IQ data to get management guidance variables, and with Thomson-Reuters Institutional Holdings data to get institutional ownership. We exclude all firms in the financial and utility industries and firm-quarters with missing values for any of the regression variables. To remain in the sample, firms 94 As discussed previously in section 3.1, we exclude quarters in 2014, the year that the Volcker Rule was implemented. 149 must have at least five quarters of data in both the pre-treatment period and the post-treatment period. This is done to ensure enough time-series observations of each firm to estimate the firm fixed effects regression. Finally, we winsorize all continuous variables at the 1st and 99th percent levels to limit the impact of outliers. The final sample includes 30,377 firm-quarters with 1,973 unique firms. 3.4.2 Summary Statistics Table 3.1 Panel A reports summary statistics of regression variables in the pre-Volcker Rule and post-Volcker Rule eras for all firms with credit ratings, while Panel B reports summary statistics for those without credit ratings. The differences in means between pre- and post-Volcker Rule eras are also reported. Firms with credit ratings (treated firms) are presumed to rely on bond financing, so they are expected to be influenced by the Volcker Rule. As Table 3.1 shows, 30.7 (32.4) percent of rated firms issue at least one management forecast (MF_all_dum) during the pre- (post-) Volcker Rule era, while 25.9% (25.7%) of non-rated firms issue at least one management forecast during the pre- (post-) Volcker Rule era. Panel C reports the difference-in-difference value (i.e., the difference between pre-Volcker Rule disclosure and post-Volcker Rule disclosure for rated versus non-rated firms), and it is positive and significant. This univariate result provides preliminary evidence that firms with public bonds increased their voluntary disclosures after the Volcker Rule’s bond liquidity shock. We also observe that relative to non-rated firms, rated firms also experience greater increases in market-to-book ratios (MTB), return volatility (RETVOL), and leverage (LEV), a smaller increase in analyst following (LnAF), and a larger decrease in institutional ownership (INST). Changes in these control variables could be associated with differences in the probability 150 of issuing management forecasts, necessitating multivariate regression models that control for the forecast determinants. 3.4.3 Results on Testing H1 Table 3.2 reports the results from estimating equation (1). The standard errors in the brackets are clustered by year-quarter. In Columns (1) and (2), the dependent variables are a dummy variable set to 1 if the firm issues at least one management guidance regarding its quarterly performances during the quarter, and zero otherwise (MF_all_dumi,t). Column (1) reports the OLS regression estimates, and Column (2) the logistic regression estimates. In Columns (3) and (4), the dependent variables are a count variable measuring the total number of management guidance items issued during the quarter (MF_alli,t). Column (3) uses OLS, and Column (4) uses a Poisson regression. In Column (1), the coefficient on the interaction term, Postt*Have_rati, is 0.019 and significant at 1% level. Because we use a linear probability model, we can directly interpret the coefficients as the marginal effects on the probability of issuing management guidance. Thus, the 0.019 coefficient estimate implies that firms relying on public debt financing increased their forecast tendency by 1.9 percentage points (relative to firms not using public debt) after the bond liquidity shock induced by the Volcker Rule. This effect is also economically significant. Given that 30.7% of firms with bond ratings issued at least one forecast during the sample quarters before the Volcker Rule, relatively speaking, these firms increased their forecast probability by 6.2% (1.9/30.7). In Column (1), the coefficients on control variables are generally consistent with prior studies. Forecast likelihood is positively associated with firm size (LnMV), BETA, cash flow 151 volatility (CFVOL), analyst following (LnAF), and leverage (LEV) (consistent with Li and Yang 2016; Ajinkya, Bhojraj, and Sengupta 2005; Feng and Koch 2010). These results suggest that large, highly levered, volatile, high systematic-risk firms issue more forecasts. Moreover, positive coefficients on analyst following are consistent with management disclosing more to meet the information demands of analysts. Forecast probability is negatively associated with market-to-book ratio (MTB) and institutional ownership (INST) (consistent with Lennox and Park 2006; Bergman and Roychowdhury 2008). These results suggest that that firms with high growth opportunities and institutional holdings tend to forecast less. Column (2) reports the results from estimating equation (1) using a logistic model. The results are consistent with the OLS regression. We also examine whether firms that rely on public debt increase the number of items they forecast following the Volcker Rule. The number of disclosures is used as a proxy for the information content of voluntary disclosures each quarter. Column (3) shows the OLS regression results when we regress the number of forecast items on the same set of independent variables as in column 1. In Column (3), the coefficient on the interaction term, Postt*Have_rati, is 0.063 and significant at 1% level, suggesting that firms relying (relative to the ones not relying) on public debt financing increase their number of forecast items by 0.063 times after the Volcker Rule. This effect is also economically significant. Given that the average number of forecast items reported by firms with ratings is 0.785 before the Volcker Rule, firms that relied on public debt financing experienced an additional 8% (0.063/0.785) increase in the number of forecast items they disclosed. Column (4) reports the results from estimating the same regression for the 152 number of forecast items using a Poisson model. The results are consistent with the OLS regression results in Column (3).95 3.4.4 Results on Testing H2 Table 3.3 reports the results of estimating equation (2), where the change in disclosure tendency is measured separately for each rating category. The regression is estimated using OLS, and standard errors, reported in the brackets, are clustered by year-quarter. In Column (1), the dependent variable is a dummy variable set to 1 if the firm issues at least one management guidance regarding its quarterly performances during the quarter, and zero otherwise (MF_all_dumi,t). In Column (2), the dependent variable is the total number of management guidance items issued during the quarter (MF_alli,t). Column (1) shows that, among the post-rating interaction terms, only the coefficients for Post*Rating_A (0.05) and Post*Rating_BBB (0.021) are positive and significant at the 1 and 5 percent levels, respectively. This indicates that the increased disclosure tendency of rated firms following the Volcker Rule is being driven by firms in the two credit rating categories immediately above the investment-grade cutoff. Moreover, in terms of economic significance, the firms with A (BBB) ratings increase their disclosure tendency by 13% (7%) relative to their average disclosure probability of 38% (32%) before the Volcker Rule.96 In sum, this result is consistent with our predictions that the increased forecast tendency after the Volcker Rule is due to the firms with intermediate credit ratings. 95 To check the robustness, we also analyze the COMPUSTAT-FISD combined data to test two additional specifications. First, we use a continuous variable to indicate the degree to which a firm uses public debt. This is measured by bond issue proceeds from FISD scaled by total capital (long-term debt plus book value of shareholder’s equity). Second, we use a dummy variable, which is based on whether the bond issue proceeds are greater than zero to identify treated firms. The results are similar. 96 Appendix B.3 presents summary statistics of management forecasts for firms in each rating category. 153 Table 3.3 Column (2) shows the results when we use OLS to regress the number of forecast items on the public debt rating categories interacted with an indicator variable for the post period. The coefficients for Post*Rating_BBB (0.045), Post*Rating_BB (0.083), and Post*Rating_B (0.117) are all positive and significant. In terms of economic significance, the firms with BBB (BB; B) ratings increase their disclosure tendency by 5% (10%; 27%) relative to their average disclosure items of 0.83 (0.87; 0.43) before the Volcker Rule. When the results in Column (1) and Column (2) of Table 3.3 are combined, they show that the rating categories experiencing an increase in the number of forecast items being disclosed are not the same as the rating categories that increased their tendency to disclose forecasts. Firms with BBB ratings increase both their propensity to disclose and the information content of their guidance. However, firms with BB and B ratings increase the number of forecast items they disclose, but they do not change their tendency to start issuing management forecasts. This finding implies that the increased number of forecast items disclosed by firms with public debt rated in the BB and B rating categories is likely to be by firms that were already disclosing forecasts rather than by firms that are new to forecasting. This finding is consistent with managers being concerned about setting disclosure precedents that can be difficult to maintain (e.g., Graham, Harvey, and Rajgopal 2005). Therefore, the existing disclosing firms, who have already established a practice of issuing management guidance, are the ones that are likely to increase the information content of their management guidance by increasing the number of forecast items they disclose. The firms that have not disclosed in the past are not willing to start a guidance practice. Interestingly, the coefficient on Post*Rating_AA (0.143) is negative and significant at the 10% level. This finding implies that although firms with AA ratings do not 154 change their forecast tendency, as shown in Table 3.3 column 1, they decrease the average number of forecast items they report following the Volcker Rule. 3.5 Additional Tests: Instrumental variables for whether a firm issues public debt It’s possible that whether a firm issues public debt (have_rati) is endogenous to a firm’s forecast tendency. The main concern is firm characteristics simultaneously determine whether a firm issues public debt and whether it issues management forecasts. Thus, we re-estimate the equation (1) using an instrumental variable approach that is analogous to the one in Faulkender and Petersen (2006; 2011) and Harford and Uysal (2014). The first stage in the instrumental variable estimation is to estimate the endogenous variable (whether a firm issues public debt) as a function of instruments and the control variables from the second stage. The two stages of the specification are as follows: Stage 1 have_rati = β0 + β1 SP500i,t + β2 NYSEi,t + β2 Ratio_of_Rated_Firmsi,t + Σ βj Controlsi,t + Time_FEt +εi,t, (3) 155 Stage 2 VDISCi,t = β0 + β1 have_rat (hat)i,t + β2 Postt*have_rat (hat)i,t +Σ βj Controlsi,t + Time_FEt + Firm_FEi + εi,t, (4) where SP500i,t and NYSEi,t are indicator variables equal to one if firm i is included in the S&P 500 index and listed on the New York Stock Exchange in quarter t, respectively. Ratio_of_Rated_Firmsi,t is the percentage of firms in the same three-digit SIC industry with a credit rating. The fitted variable have_rat(hat)i,t is the estimated probability that firm i uses public debt in quarter t. For the interaction term in equation (4) (Postt*have_rat(hat)), we follow the method in Prilmeier (2017), interacting the predicted value of have_ratt with the post-Volcker Rule indicator. The controls in equation (4) include the same set of variables as used in the regression model for our main test. The first stage equation (3) is estimated by a probit model97 to ensure the predicted likelihood of having a credit rating is in the right range. The second stage equation (4) is estimated by OLS to analyze the marginal effects as discussed previously. Table 3.4 column 1 reports the results from estimating the stage 1 (equation 3) regression. The instruments (SP500i,t, NYSEi,t, and Ratio_of_Rated_Firmsi,t) are significantly associated with the likelihood that a firm has a credit rating, consistent with the results in Faulkender and Petersen (2006; 2011) and Harford and Uysal (2014). Table 3.4 column 2 reports the results from estimating the stage 2 (equation 4) regression. The coefficient on Postt*have_rat(hat)it is positive and significant at the one percent level, suggesting that firms with a higher predicted likelihood 97 Our conclusions remain unchanged if we use a logit model. 156 of using public bond issues increase their voluntary disclosure after the Volcker Rule. Again, the result is consistent with our hypothesis. Table 3.4 Column (3) reports the results on whether firms that are more likely to use public bond issues increased their number of forecast items following the Volcker Rule. The coefficient on Postt*have_rat(hat)it is positive and significant at the one percent level, consistent with the notion that, on average, firms using public bonds issued management forecasts covering more items after the Volcker Rule. 3.6 Conclusion We examine the relation between the negative impact of the Volcker Rule on bond liquidity and voluntary disclosures. The Volcker Rule prohibits banks and their affiliates from using their funds to buy, sell, and hold securities to make profits. Its purpose is to prevent a repeat of the 2008 financial crisis by discouraging banks from taking on too much risk. However, the rule’s compliance requirements also affect the ability and incentive of banks to act as market-makers for corporate bonds. Prior studies (e.g., Bao et al. 2018) find that after the Volcker Rule, corporate bond liquidity decreased, and dealers regulated by the rule decreased their market-making activities. Using a difference-in-difference approach, we find that, compared with firms that don’t use public debt, firms with outstanding bonds increase their tendency to provide management guidance after the Volcker Rule. This result is consistent with the notion that firms that experienced a negative shock to the trading mechanism for their debt securities increased voluntary disclosure to compensate for the loss of liquidity. Two additional tests support our main finding. First, we find that the increased management guidance is more pronounced for firms with credit ratings close to the investment-grade cutoff, rather than those with very high or very low ratings, where firms have less ability to 157 change market perceptions by their disclosures. Firms with very high credit ratings (i.e., AAA and AA) are less likely to be affected by the decreased bond liquidity caused by the Volcker Rule. These firms have a rich information environment and low uncertainty. It is relatively straightforward to find counterparties to trade these high-quality securities. Firms with very low credit ratings (i.e., BB and below) have less incentive to increase their voluntary disclosures in response to the Volcker Rule. The uncertainty they face makes it costlier for these firms to disclose and more difficult for them to resolve the uncertainty investors are concerned about. Firms that issue public debt are likely to be systematically different from firms that do not use public debt. To mitigate endogeneity concerns, we follow prior studies (e.g., Harford and Uysal 2014) and use three predictors of whether a firm issues corporate bonds as instruments for our main test. Our results continue to hold when we control for endogeneity. 158 Table 3.1 Summary Statistics Panel A: Firms With Credit Rating Period Pre-Volcker Post-Volcker 2012Q1-2013Q4 2015Q1-2016Q4 (A) MEAN MEDIAN Min Max STD (B) MEAN MEDIAN Min Max STD Mean DIFF P-value t-test MF_all_dum 0.307 0.000 0 1 0.461 0.324 0.000 0 1 0.468 0.017 0.009 2.626 *** MF_all 0.785 0.000 0 12 1.499 0.873 0.000 0 13 1.614 0.088 0.000 4.045 *** LnMV 8.468 8.404 4.134 12.260 1.527 8.644 8.673 3.633 12.136 1.654 0.176 0.000 7.897 *** MTB 2.964 2.158 0.261 24.450 2.952 3.750 2.499 0.151 32.035 4.443 0.786 0.000 14.861 *** ROA 0.012 0.013 -0.217 0.160 0.021 0.007 0.010 -0.300 0.139 0.028 -0.005 0.000 -14.598 *** LOSSDUM 0.153 0.000 0 1 0.360 0.222 0.000 0 1 0.416 0.070 0.000 12.777 *** BHAR 0.012 0.007 -0.465 0.750 0.131 -0.001 -0.003 -0.567 0.859 0.160 -0.013 0.000 -6.285 *** BETA 1.412 1.291 0.009 3.694 0.731 1.329 1.283 -0.278 3.152 0.612 -0.083 0.000 -8.791 *** CFVOL 0.020 0.015 0.002 0.162 0.017 0.020 0.015 0.003 0.156 0.016 -0.001 0.034 -2.117 ** RETVOL 0.018 0.016 0.007 0.081 0.008 0.022 0.019 0.008 0.114 0.013 0.004 0.000 28.800 *** LnAF 2.472 2.639 0.000 3.584 0.697 2.476 2.639 0.000 3.555 0.727 0.004 0.674 0.421 HERFIN 0.097 0.068 0.030 0.708 0.083 0.090 0.069 0.029 0.543 0.070 -0.008 0.000 -7.186 *** LEV 0.267 0.252 0.000 0.663 0.150 0.312 0.297 0.000 0.816 0.149 0.045 0.000 21.721 *** INST 63.973 74.266 0 100 30.079 53.958 64.756 0.000 96.345 29.336 -10.015 0.000 -24.055 *** N of obs 5,011 4,985 159 Panel B: Firms Without Credit Rating Period Pre-Volcker Post-Volcker 2012Q1-2013Q4 2015Q1-2016Q4 (C) MEAN MEDIAN Min Max STD (D) MEAN MEDIAN Min Max STD Mean DIFF P-value t-test MF_all_dum 0.259 0.000 0 1 0.438 0.256 0.000 0 1 0.437 -0.003 0.648 -0.457 MF_all 0.732 0.000 0 11 1.511 0.754 0.000 0 11 1.587 0.022 0.307 1.022 LnMV 5.737 5.780 1.850 11.639 1.755 5.936 6.020 1.740 11.719 1.848 0.200 0.000 7.899 *** MTB 2.864 1.900 0.223 24.450 2.955 3.216 2.079 0.151 32.035 3.825 0.352 0.000 7.344 *** ROA -0.006 0.009 -0.300 0.160 0.061 -0.010 0.006 -0.303 0.139 0.058 -0.004 0.000 -4.455 *** LOSSDUM 0.334 0.000 0 1 0.472 0.388 0.000 0 1 0.487 0.054 0.000 8.094 *** BHAR 0.005 -0.010 -0.525 1.646 0.200 -0.002 -0.012 -0.567 0.859 0.206 -0.008 0.008 -2.634 *** BETA 1.289 1.223 -0.073 3.694 0.633 1.204 1.175 -0.278 3.152 0.622 -0.084 0.000 -9.595 *** CFVOL 0.038 0.027 0.002 0.228 0.036 0.037 0.026 0.003 0.221 0.035 -0.001 0.046 -1.999 ** RETVOL 0.027 0.024 0.007 0.110 0.015 0.030 0.025 0.008 0.114 0.016 0.002 0.000 10.280 *** LnAF 1.340 1.386 0.000 3.584 0.999 1.379 1.386 0.000 3.555 0.980 0.039 0.005 2.813 *** HERFIN 0.093 0.064 0.030 0.708 0.083 0.088 0.072 0.029 0.830 0.066 -0.005 0.000 -4.764 *** LEV 0.097 0.017 0.000 0.663 0.140 0.130 0.060 0.000 0.697 0.161 0.033 0.000 15.689 *** INST 45.058 46.900 0 100 34.354 40.165 43.508 0 96.345 31.083 -4.893 0.000 -10.659 *** N of obs 10,218 10,151 (1) *, **, *** indicate significance at 10, 5, 1 percent levels, respectively, in a two-tailed test. (2) Please refer to Appendix B.1 for variable definitions. 160 Panel C: Difference in Difference DiD [(B-A) - (D-C)] P-value t-stat MF_all_dum 0.020 0.074 1.787 * MF_all 0.066 0.082 1.740 * LnMV -0.023 0.567 -0.573 MTB 0.434 0.000 4.855 *** ROA -0.001 0.148 -1.445 LOSSDUM 0.015 0.139 1.479 BHAR -0.005 0.186 -1.322 BETA 0.001 0.931 0.087 CFVOL 0.001 0.407 0.829 RETVOL 0.002 0.000 7.201 *** LnAF -0.035 0.080 -1.750 * HERFIN -0.003 0.146 -1.455 LEV 0.012 0.001 3.366 *** INST -5.122 0.000 -6.821 *** (1) *, **, *** indicate significance at 10, 5, 1 percent levels, respectively, in a two-tailed test. (2) Please refer to Appendix B.1 for variable definitions. 161 Table 3.2 The effect of Volcker Rule on management guidance (1) (2) (3) (4) Dep. Var = MF_all_dum MF_all Estimation Method = OLS Logit OLS Poisson Post*Have_rat 0.019*** 0.316*** 0.063*** 0.084*** [0.006] [0.094] [0.020] [0.022] Firm Controls LnMV 0.018*** 0.293*** 0.042*** 0.060** [0.005] [0.089] [0.014] [0.024] MTB -0.002** -0.026** -0.006** -0.012*** [0.001] [0.013] [0.002] [0.005] ROA 0.002 0.229 -0.129 -0.365 [0.055] [1.096] [0.167] [0.333] LOSSDUM -0.004 -0.047 -0.018 -0.024 [0.005] [0.075] [0.011] [0.015] BHAR 0 -0.044 -0.008 -0.02 [0.006] [0.099] [0.023] [0.040] BETA 0.016*** 0.327*** 0.013 0.021 [0.004] [0.079] [0.011] [0.020] CFVOL 0.229*** 7.082*** 0.552*** 1.722*** [0.068] [1.983] [0.204] [0.653] REVOL -0.16 -4.042 -0.699* -1.649* [0.146] [2.804] [0.408] [0.930] LnAF 0.025*** 0.457*** 0.071*** 0.140*** [0.006] [0.092] [0.020] [0.029] HERFIN -0.009 0.318 -0.052 -0.043 [0.049] [0.834] [0.138] [0.288] 162 LEV 0.055** 0.782** 0.254*** 0.222** [0.027] [0.364] [0.086] [0.103] INST -0.000*** -0.004*** -0.001* -0.001 [0.000] [0.001] [0.001] [0.001] Qtr FE YES YES YES YES Year FE YES YES YES YES Firm FE YES YES YES YES N 30,365 30,365 30,365 30,365 R-squared/Psuedo R-squared 0.688 0.308 0.766 0.58 (1) MF_all_dum is an indicator variable set to one for firms that issue at least one management guidance regarding its quarterly performances during each quarter, and zero otherwise. MF_All is a count variable measuring the total number of management guidance items issued during each quarter. (2) *, **, *** indicate significance at 10, 5, 1 percent levels, respectively, in a two-tailed test. (3) Number in brackets [ ] are standard errors clustered by the year-quarter end. (4) Please refer to Appendix B.1 for the definitions of independent variables. 163 Table 3.3 Examining the Volcker Rule's impact on disclosures of firms with different credit ratings (1) (2) Dep. Var = MF_all_dum MF_all Estimation Method = OLS OLS Post*Rating_AAA 0.07 0.028 [0.063] [0.063] Post*Rating_AA -0.04 -0.143* [0.026] [0.078] Post*Rating_A 0.050*** 0.053 [0.017] [0.042] Post*Rating_BBB 0.021** 0.045** [0.009] [0.021] Post*Rating_BB 0.017 0.083** [0.011] [0.035] Post*Rating_B 0.002 0.117** [0.014] [0.053] Post*Rating_CCC 0.046 -1.158 [0.260] [1.098] Controls YES YES Qtr FE YES YES Year FE YES YES Firm FE YES YES N 30,365 30,365 R-squared 0.689 0.767 (1) MF_all_dum is an indicator variable set to one for firms that issue at least one management guidance regarding its quarterly performances during each quarter, and zero otherwise. MF_All is a count variable measuring the total number of management guidance items issued during each quarter. 164 (2) *, **, *** indicate significance at 10, 5, 1 percent levels, respectively, in a two-tailed test. (3) Number in brackets [ ] are standard errors clustered by the year-quarter end. (4) Please refer to Appendix B.1 for the definitions of independent variables. 165 Table 3.4 The effect of Volcker Rule on management guidance: Instrumental Variable Approach (1) (2) (3) Dep. Var = Have_rat MF_all_dum MF_all Estimation Method = Probit OLS OLS SP500 0.527*** [0.036] NYSE 0.556*** [0.023] Ratio_of_Rated_Firms 0.744*** [0.062] Have_rat (hat) 0.078* 0.187* [0.042] [0.102] Post*Have_rat (hat) 0.029*** 0.072*** [0.010] [0.021] Controls YES YES YES Qtr FE YES YES YES Year FE YES YES YES Firm FE NO YES YES N 29,679 29,679 29,679 R-squared/Psuedo R-squared 0.537 0.689 0.767 (1) MF_all_dum is an indicator variable set to one for firms that issue at least one management guidance regarding its quarterly performances during each quarter, and zero otherwise. MF_All is a count variable measuring the total number of management guidance items issued during each quarter. Have_rat is an indicator variable set to 1 if a firm has S&P credit rating, and zero otherwise. (2) *, **, *** indicate significance at 10, 5, 1 percent levels, respectively, in a two-tailed test. (3) Number in brackets [ ] are standard errors clustered by the year-quarter end. (4) Please refer to Appendix B.1 for the definitions of independent variables. 166 Chapter 4: Conclusion This thesis is a collection of two studies that seek to broaden our understanding of banks’ role in the economy. These two studies are both related to the banking sector reforms in the post-financial crisis era. The first study focuses on how the reform in loan loss accounting influences banks’ role in the loan markets. The second study examines how the reform in bond trading regulation influences banks’ information provision role in the bond market. Because these two studies are presented in two self-contained chapters, I provide an exhaustive discussion of the contribution and position of each study in the broader accounting literature in conclusion specific to each chapter. In this chapter, I briefly summarize the takeaways from these two studies. The first study examines the economic consequences of an accounting rule reform, the current expected credit loss model (also known as CECL). CECL revolutionizes the concept of loan loss accounting. While CECL’s predecessor standard requires banks to not recognize expected loan losses until an event occurs that could make the loan uncollectible, CECL requires banks to expense the expected value of credit losses when loans are issued. As a result, when a loan is issued banks must consider whether they have enough capital in place to absorb the immediate charge for the new loan’s expected future losses. This implies CECL is likely to increase the amount of capital that banks need in order to maintain their capital adequacy, tightening their regulatory capital requirement. The first study is interested in whether the tightened capital requirement due to CECL would influence bank lending. Following the approval of CECL, I find that during the first three years of the CECL transition period (2016 - 2018), banks that were closer to their capital adequacy constraints (1) reduced their total loan growth, (2) reduced their growth rate of real estate loans, and (3) reduced their small business lending relative to less capital-constrained 167 banks. In cross-sectional analyses, I find that the above findings are concentrated in banks operating under a more lenient regulator, consistent with financially weaker banks being more sensitive to the anticipated capital shock induced by CECL. Bank regulatory capital is also predicted to be more negatively impacted by CECL when loans have a longer maturity. I do not find that capital-constrained banks, on average, reduce their issuance of long-term loans (i.e., loans with a remaining life of five years or more). However, I do find that two groups of capital-constrained banks reduce the growth rate of their long-term loans during the CECL transition period in this study, consistent with an effort to mitigate CECL’s negative impact. Capital-constrained banks are found to reduce their long-term loan growth when they are regulated by a lenient regulator or when they are publicly traded banks, which must adopt CECL earlier than their privately-held counterparts. The second study examines the consequences of the Volcker Rule, which prohibits banks from conducting proprietary trading. Proprietary trading occurs when banks trade financial assets for profits using their own funds. The goal of the Volcker Rule is to prevent future financial crises by discouraging banks from taking on too much risk. However, prior research finds that corporate bond liquidity (i.e., the ease of trading public bonds) has decreased after the Volcker Rule. Utilizing the Volcker Rule as a bond liquidity shock, this study examines whether the decreased bond liquidity encourages bond issuing firms to voluntarily disclose more information. Using a difference-in-difference design, this study finds that bond issuers are more likely to issue management guidance after the Volcker Rule was implemented, consistent with an effort to improve liquidity by replacing the information flow that previously came from banks. Further, the increased management guidance is more pronounced for firms with credit ratings close to the 168 investment-grade cutoff, rather than those with very high or very low ratings, where firms have less ability to change market perceptions by their disclosures. 169 Bibliography Acharya, V.V. and Ryan, S.G., 2016. Banks’ financial reporting and financial system stability. Journal of Accounting Research, 54(2), pp.277-340. Agarwal, S., Y. Chang, and A. Yavas. 2012. Adverse selection in mortgage securitization. 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Federal Reserve Bank of Richmond, Economic Quarterly, 105(1): 1–40. Weygandt, J.J., Kimmel, P.D. and Kieso, D.E., 2018. Financial and managerial accounting. John Wiley & Sons. Wheeler, P.B., 2017. Do Investors Impound Information About Unrecognized Expected Credit Losses into Bank Stock Prices? (Ph.D. Dissertation). Indiana University, United States -- Indiana. Wheeler, P.B., 2019. Loan loss accounting and procyclical bank lending: The role of direct regulatory actions. Journal of Accounting and Economics, 67(2-3), pp.463-495. 176 Wheeler, P.B., 2021. Unrecognized Expected Credit Losses and Bank Share Prices. Journal of Accounting Research (forthcoming). https://doi.org/10.1111/1475-679X.12353 177 Appendices Appendix A Appendix for Chapter 2 A.1 History of changes in CECL effective dates This appendix describes the history of changes in CECL’s effective dates. There are two aspects of CECL adoption. The first aspect is related to financial reporting: when are banks required to report their CECL accounting numbers in their financial statements? The second aspect is related to their regulatory capital calculations: when and how are banks required to include the impact of CECL into their regulatory capital calculations? The financial reporting aspect: when are banks required to report their CECL accounting numbers in their financial statements? There are four versions of CECL effective dates for financial reporting. The first one was the version announced when the FASB approved CECL in November 201598. The original timeline is shown below: As we can see, under the original rules, public entities who are SEC filers must adopt CECL in 2019Q1, and public entities that are not SEC filers must adopt CECL one year later in 2020Q1. The definition of public entities is documented in ASU 2013-1299. Banks are considered to be public entities when they meet one of the following criteria at the beginning of the fiscal year: 98 See https://www.fasb.org/jsp/FASB/Document_C/DocumentPage&cid=1176167627008 for the meeting minutes of the FASB’s approval on November 18, 2015 in page 3. 99 See page 5 in https://www.fasb.org/jsp/FASB/Document_C/DocumentPage?cid=1176163702930&acceptedDisclaimer=true 178 (1) they are an SEC filer, or (2) they have stock traded on the OTC market, or (3) they have total assets greater than $500 million100. Non-public entities, those banks that do not meet one of these three criteria, must adopt CECL in 2020Q4. The second version of CECL effective dates was made public in June 2016 when the FASB officially announced the details of the CECL standard in Accounting Standard Update (ASU 2016-13) in June 2016. The timeline is shown below: The second version postpones all three effective dates for different entities by one additional year. The third version of the timeline was made public when the FASB modified the CECL effective dates in November 2019101. The new timeline is shown below: 100 See https://www.eidebailly.com/insights/articles/2019/11/the-impact-of-fasb-delays-on-financial-institutions. 101 See https://www.fasb.org/jsp/FASB/FASBContent_C/ActionAlertPage&cid=1176172970471&rss=1 for the source. 179 This new timeline extends the effective dates by three years for SEC filers that fall under the definition of Small Reporting Companies. Small Reporting Companies are those SEC filers, at the beginning of the fiscal year, either have: (1) a public float of less than $250 million, or (2) annual revenue of less than $100 million and (a) no public float, or (b) a public float of less than $700 million102. Moreover, this new timeline also extends the effective dates for all other entities to 2023Q1. The fourth version of the timeline arises from The Coronavirus Aid, Relief, and Economic Security Act (CARES), signed into law on March 27, 2020. Section 4014 of the Act offers banks the option to delay their CECL adoption date until the earlier of, when the COVID-19 national emergency ends, or December 31, 2020. Section 4014 affects only the entities that were scheduled to adopt CECL in 2020Q1 (i.e., the SEC filers that are not small reporting companies). 102 See the SEC website on https://www.sec.gov/smallbusiness/goingpublic/SRC for the more details on the small reporting companies. 180 The regulatory capital calculation aspect: when and how are banks required to include the impact of CECL into their regulatory capital calculations? Originally, banks were expected to incorporate the day-1 CECL adjustments into their regulatory capital calculations at the same time as they would adopt CECL for financial reporting purposes 103. However, on December 21st, 2018, bank regulators announced that banks would be given an option to phase in their day-1 adjustment to regulatory capital over three years. Specifically, when banks adopt CECL, they have an option to amortize CECL’s initial adjustment to their retained earnings for calculating their tier one regulatory capital. Further relaxation of CECL for regulatory capital purposes was issued in response to the COVID-19 outbreak. In March 2020, the banking regulators approved a new rule that gives large public banks several options to further delay when CECL adoption impacts their regulatory capital104. Under this new rule, the SEC filers who have adopted CECL in 2020Q1 can delay the initial CECL retained earnings adjustment to their regulatory capital for two years. After the two years, these SEC filers must either recognize the full adjustment immediately or amortize the adjustment over up to three years. There were some additional regulatory capital relaxations introduced for these 2020Q1 adopters under the CARES Act. These adjustments give the adopting banks the option to delay the recognition of a portion of loan loss provisions in calculating regulatory capital. Please see 103 See the OCC website on https://www.occ.treas.gov/news-issuances/news-releases/2018/nr-ia-2018-142.html for more details on CECL and regulatory capital. 104 See OCC website https://www.occ.treas.gov/news-issuances/bulletins/2020/bulletin-2020-30.html for more details on COVID-19 regulatory capital relief. 181 the Federal Register website for further details on the COVID-19-related relaxation of CECL for regulatory capital purposes.105 105 The Federal Register website is available at https://www.federalregister.gov/documents/2020/09/30/2020-19782/regulatory-capital-rule-revised-transition-of-the-current-expected-credit-losses-methodology-for. 182 A.2 Variable Definitions Variables Definitions Source CET1_CAP_RATIOi,t Common equity Tier 1 capital ratio for bank i at the end of quarter t = Common equity Tier 1 regulatory capitali,t Risk-weighted assetsi,t Y-9C items: bhcap859/bhcaa223 FR Y-9C106 T1_CAP_RATIOi,t Tier 1 capital ratio for bank i at the end of quarter t = Tier 1 regulatory capitali,t Risk-weighted assetsi,t Y-9C items: bhck8274/bhcka223 TOTAL_CAPITAL_RATIOi,t Total risk-based capital ratio for bank i at the end of quarter t = Total regulatory capitali,t Risk-weighted assetsi,t Y-9C items: sbhck3792/bhcka223 LEV_RATIOi,t Tier 1 leverage ratio for bank i at the end of quarter t = Tier 1 regulatory capitali,t Total assetsi,t Y-9C items: bhck8274/bhcka224 106See https://www.chicagofed.org/banking/financial-institution-reports/bhc-data. 183 Variables Definitions Source CLOSE_TO_CONS_ALLi Max � 4.5%CET1_CAP_RATIOi,t , 6%T1_CAP_RATIOi,t , 8%TOTAL_CAPITAL_RATIOi,t , 4%LEV_RATIOi,t�, averaged over the four quarters: 2015Q1–2015Q4, for bank i. CLOSE_TO_CONS_T1i 6%T1_CAP_RATIO, averaged over the four quarters: 2015Q1–2015Q4, for bank i. MORE_CONSTRAIN_ALLi Indicator variable equal to 1 if CLOSE_TO_CONS_ALLi is greater than its median and equal to 0 otherwise. MORE_CONSTRAIN_T1i Indicator variable equal to 1 if CLOSE_TO_CONS_T1i is greater than its median and equal to 0 otherwise. EQUITY_TO_ASSETSi,t Total equityi,t Total assetsi,t for bank i at the end of quarter t. Y-9C items: bhck3210/bhck2170 dEQi Total equityi,2018Q2 Total assetsi,2018Q2 − Total equityi,2015Q4 Total assetsi,2015Q4 , for bank i. Y-9C items: bhck3210/bhck2170 CASH&MKTSECi,t-1 Cashi,t−1 + Marketable securitiesi,t−1 Total assetsi,t−1 for bank i at the end of quarter t-1. Y-9C items: (bhck0081+bhck0395+bhck0397+bhck3545)/bhck2170 TOT_RE_LOANSi,t-1 Long-term real estate loans, which is equal to the sum of residential real estate loans (RES_RE_LOANSi,t-1) and commercial real estate loans (COM_RE_LOANSi,t-1) 184 Variables Definitions Source ∆TOT_RE_LOANSi,t TOT_RE_LOANSi,t − TOT_RE_LOANSi,t−1 TOT_RE_LOANSi,t−1 for bank i during quarter t. RES_RE_LOANSi,t-1 Residential real estate loans for bank i at the end of quarter t-1 = Loans secured by residential propertiesi,t−1 Total assetsi,t−1 Y-9C items: (bhdm1797+bhdm5367+bhdm5368+bhdm1460)/bhck2170 ∆RES_RE_LOANSi,t RES_RE_LOANSi,t − RES_RE_LOANSi,t−1 RES_RE_LOANSi,t−1 for bank i during quarter t. COM_RE_LOANSi,t-1 Commercial real estate loans for bank i at the end of quarter t-1 = Loans secured by nonfarm nonresidential propertiesi,t−1 Total assetsi,t−1 Y-9C items: (bhckf160+bhckf161)/bhck2170 ∆COM_RE_LOANSi,t COM_RE_LOANSi,t − COM_RE_LOANSi,t−1 COM_RE_LOANSi,t−1 for bank i during quarter t. COM_NRE_LOANSi,t-1 Commercial non-real estate loans for bank i at the end of quarter t-1 = Commercial and industrial loansi,t−1 Total assetsi,t−1 Y-9C items: (bhck1763+bhck1764+bhck1590)/bhck2170 ∆COM_NRE_LOANSi,t COM_NRE_LOANSi,t − COM_NRE_LOANSi,t−1 COM_NRE_LOANSi,t−1 for bank i during quarter t. 185 Variables Definitions Source CONSUMER_LOANSi,t-1 Loans and leases to individuals (including credit cards, other revolving credit plans, automobile loans, other consumer loans, and leases to individuals) for bank i at the end of quarter t-1 = Loans and leases to individualsi,t−1Total assetsi,t−1 Y-9C items: (bhckb538+bhckb539+bhckk137+bhckk207+bhckf162)/bhck2170 ∆CONSUMER_LOANSi,t Consumer loansi,t − Consumer loansi,t−1 Consumer loansi,t−1 for bank i during quarter t. DEPOSITSi,t-1 Depositsi,t−1Total liabilitiesi,t−1 for bank i at the end of quarter t-1. Y-9C items: (bhdm6631+bhdm6636+bhfn6631+bhfn6636)/bhck2948 TOT_LOANSi,t-1 Total loansi,t−1Total assetsi,t−1 for bank i at the end of quarter t-1. Y-9C items: bhck2122/bhck2170 ∆LOANSi,t Total loansi,t − Total loansi,t−1 Total loansi,t−1 for bank i during quarter t. TOTAL_ASSETSi,t-1 ($ Bill) Total assets in $ billion for bank i at the end of quarter t-1. Y-9C items: bhck2170 SIZEi,t-1 Log value of TOTAL_ASSETSi,t-1. 186 Variables Definitions Source NPLi,t-1 Non−performing loansi,t−1 Total loansi,t−1 for bank i at the end of quarter t-1. Y-9C items: (bhck1407+bhck1403+bhck3506+bhck3507)/bhck2122 ROA i,t-1 Income before discontinued operations i,t−1Average total assetsi,t−1 for bank i during quarter t-1. Y-9C items: (1) bhck4300/(bhck2170+lag(bhck2170)/2) for the first quarter in each sample year, or (2) (bhck4300 − lag(bhck4300))/(bhck2170+lag(bhck2170)/2) for the second, third, and fourth quarter in each sample year. TRADING_ASSETS i,t-1 Trading assetsi,t−1Total assetsi,t−1 for bank i at quarter t-1. Y-9C items: bhck3545/bhck2170 PUBLICi Indicator variable equal to 1 if bank i has equity traded on a stock exchange and equal to 0 otherwise. This variable is time-invariant at the bank level because banks that change their public listing status during my sample period are dropped from the sample. New York Fed107 SBLi,c,t Amount of new small business loans in $ thousand originated by bank i in county c during year t. FFIEC website108 107 See https://newyorkfed.org/research/banking_research/datasets.html. 108 FFIEC stands for Federal Financial Institutions Examination Council. The website is available at https://www.ffiec.gov/cra/default.htm. 187 Variables Definitions Source LOG_SBLi,c,t Log value of SBLi,c,t. LONG_TERM_LOANSi,t-1 Amount of loans with five years or greater of remaining life scaled by total assets for bank i at the end of quarter t-1. If bank i owns multiple bank subsidiaries, then this variable is measured at the bank i level by taking a weighted average according to each subsidiary’s total assets. Call Report data variable names: LNOT5T15+LNRS5T15+LNRSOV15+LNOTOV15 Call Reports data109 ∆LONG_TERM_LOANSi,t LONG_TERM_LOANSi,t − LONG_TERM_LOANSi,t−1 LONG_TERM_LOANSi,t−1 for bank i during quarter t. REG_LENi Index of regulatory leniency, calculated as follows: (1) If a bank is state-chartered, I use the regulatory leniency index constructed by Agarwal et al. (2014). The index is measured as the average difference between the federal regulator and each state regulator in the bank regulatory ratings assigned to the same bank. (2) If a bank is federally chartered, I follow Costello et al. (2019) and set bank i‘s regulatory leniency index to zero as these banks are only monitored by federal regulators. Amit Seru’s website110 109 See https://www.fdic.gov/foia/ris/. 110 See https://aseru.people.stanford.edu/data-and-discussions. 188 Variables Definitions Source If bank i owns multiple bank subsidiaries, then this variable is measured to the bank i level by taking the weighted average of each subsidiary’s regulatory leniency index. The weights are equal to each subsidiary’s total assets. HIGH_REG_LENi Indicator variable equal to 1 if REG_LENi is greater than its median and equal to 0 otherwise. 189 Appendix B Appendix for Chapter 3 B.1 Variable definitions All continuous variables are winsorized at 1% and 99%. Main variables (alphabetically ordered) have_rat An indicator variable set to 1 if a firm has an S&P credit rating, and zero otherwise have_rat (hat) Estimated probability that a firm has a credit rating MF_all_dum An indicator variable set to one for firms that issue at least one management guidance regarding its quarterly performances (e.g., EPS, sales, earnings before interest and tax, etc.) during each quarter, and zero otherwise MF_all A count variable measuring the total number of management guidance items issued during each quarter NYSE An indicator set to one if a firm i is listed on the New York Stock Exchange, and zero otherwise Post An indicator variable set to one if a firm-quarter is after the implementation of Volcker Rule in 2014, and zero before 2014 Ratio_of_Rated_Firms The percentage of firms in the same three-digit SIC industry that have a credit rating. Rating_indicator A vector for the indicators of different credit ratings at the end of the year 2013. For example, credit rating variable A is one of the elements in this vector. The variable A equals one if a firm has a credit rating A+, A, or A- at the end of the year 2013. And the same logic applies to other credit rating classes. SP500 An indicator set to one if a firm i is included in the S&P 500 index, and zero otherwise Controls (ordered as the sequence they appear in regression tables.) LnMV Natural logarithm of beginning of the quarter market value of firm equity (in millions). MTB Beginning of quarter market-to-book ratio. ROA Current quarter's return on assets, computed as the ratio of earnings before extraordinary items to total assets at the beginning of the fiscal quarter. LOSSDUM An indicator variable set to one for firms that report losses in the previous quarter BHAR Buy-and-hold size-adjusted return measured over the three months prior to the beginning of the fiscal quarter. The size adjustment is based on the return on a portfolio of NYSE/AMEX/NASDAQ stocks in the same size-decile (market capitalization) as the sample firm. BETA Equity beta for the previous fiscal quarter. 190 CFVOL Cash flow volatility, computed as the standard deviation of quarterly operating cash flows over the prior two years. RETVOL Stock return volatility, computed as the standard deviation of daily gross stock returns over the three months prior to the beginning of the quarter. LnAF Natural logarithm of one plus the number of analysts at the beginning of the quarter. (Data on the number of analysts following are obtained from I/B/E/S. If a firm is not covered by I/B/E/S, the number of analysts variable is coded as zero.) HERFIN Beginning of the quarter Herfindahl index estimated based on firm sales in an industry defined by the Fama–French classification. It is calculated by squaring each firm's share of industry revenues. LEV Beginning of the quarter long-term debt divided by total assets. INST Percentage of shares owned by institutional investors in the previous quarter. (Data on institutional ownership are obtained from Thomson-Reuters Institutional Holdings (13F) Database. If a firm is not covered by the database, the institutional ownership variable is coded as zero.) 191 B.2 Disclosure frequency of different quarterly management forecast items for treatment and control firms in pre-Volcker and post-Volcker eras Treatment: Firms With Credit Rating Control: Firms W/O Credit Rating Capital IQ Item name Description Overall Pre-Volcker 2012Q1-2013Q4 (n = 5,011) Post-Volcker 2015Q1-2016Q4 (n = 4,985) Pre-Volcker 2012Q1-2013Q4 (n = 10,218) Post-Volcker 2015Q1-2016Q4 (n = 10,151) All types 27.7% 30.7% 32.4% 25.9% 25.6% EPS EPS_EXCL EPS with some adjustments (e.g., excluding Amortization of Intangible Assets) 17.4% 20.2% 21.0% 15.9% 15.8% EPS_GAAP Regular EPS EPS_GROWTH EPS growth Sales REVENUE Sales 18.8% 14.0% 16.7% 20.2% 20.7% Earnings related EBITDA Earnings before interest, tax, depreciation, and amortization 6.8% 5.1% 6.8% 6.5% 7.9% EBIT Earnings before interest and tax NI Net income excluded executive compensation NI_GAAP Net income by GAAP 192 Treatment: Firms With Credit Rating Control: Firms W/O Credit Rating Capital IQ Item name Description Overall Pre-Volcker 2012Q1-2013Q4 (n = 5,011) Post-Volcker 2015Q1-2016Q4 (n = 4,985) Pre-Volcker 2012Q1-2013Q4 (n = 10,218) Post-Volcker 2015Q1-2016Q4 (n = 10,151) Gross Margin% GROSS_MARGIN Gross Margin% 5.7% 3.9% 4.6% 6.0% 6.7% Effective Tax Rate % EFFECTIVE_TAX Effective Tax Rate % 6.7% 8.6% 9.8% 5.8% 5.2% Others DPS Dividends per share 6.9% 9.9% 9.6% 5.3% 5.7% INTEREST_EXP interest expenses DISTRIBUTABLE_CASH distributable cash DA Depreciation & Amortization CASH_OPER Cash From Operations CAPEX Capital Expenditure MAINT_CAPEX Maintenance Capital Expenditure FCF free cash flow SAME_STORE Same-Store Sales Growth % 193 B.3 Summary Statistics of disclosure variables for firms in each rating category Credit ratings Post-Volcker N Obs Variable Mean AAA No 40 MF_all_dum 0.075 MF_all 0.075 Yes 36 MF_all_dum 0.139 MF_all 0.139 AA No 139 MF_all_dum 0.396 MF_all 1.050 Yes 138 MF_all_dum 0.362 MF_all 0.906 A No 713 MF_all_dum 0.383 MF_all 0.865 Yes 703 MF_all_dum 0.437 MF_all 0.977 BBB No 1699 MF_all_dum 0.323 MF_all 0.834 Yes 1688 MF_all_dum 0.341 MF_all 0.900 BB No 1586 MF_all_dum 0.303 MF_all 0.867 Yes 1574 MF_all_dum 0.314 MF_all 0.966 B No 827 MF_all_dum 0.210 MF_all 0.430 Yes 838 MF_all_dum 0.211 MF_all 0.575 CCC No 7 MF_all_dum 0.571 MF_all 2.571 Yes 8 MF_all_dum 0.625 MF_all 1.500 Firms without a credit rating No 10218 MF_all_dum 0.259 MF_all 0.732 Yes 10151 MF_all_dum 0.256 MF_all 0.754 MF_all_dum denotes an indicator variable set to one for firms that issue at least one management forecast in the current quarter, and zero otherwise. MF_all denotes a count variable measuring the total number of management forecasts issued in each quarter.
Thesis/Dissertation
eng
Business Administration - Accounting
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Begley, Joy
Post-2008-crisis bank reforms : studies on the real effects of loan loss accounting and the role of banks as information intermediaries
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