Abstract
I study the association between bank financial reporting opacity, measured by delayed expected loan loss recognition, and the intervention decisions made by bank regulators. Examining U.S. commercial banks during the 2007–09 financial crisis, I find that delayed expected loan loss recognition is negatively associated with the likelihood of regulatory intervention (measured by either severe enforcement action or closure). This result is robust to using various specifications and research designs. In additional analyses, I find evidence suggesting that this association is driven by regulators exploiting financial reporting opacity to practice forbearance. My findings contribute to the extant literature on bank opacity, regulatory forbearance, and the consequences of loan loss provisioning by suggesting that delayed expected loan loss recognition affects regulatory intervention decisions.
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Notes
Similarly, Fischer and Verrecchia (2000) show that if investors are unable to fully unwind reporting bias, they will reduce their price response to reported fundamentals (e.g., earnings). Non-regulator bank stakeholders are likely to lack the training and private information necessary to unwind financial reporting opacity, especially relative to bank regulators (Stigler 1971; White 2012).
Prior work takes a more indirect approach, interpreting opaque reporting choices by banks (Huizinga and Laeven 2012) or changes in accounting standards (Blacconiere 1991; Blacconiere et al. 1991; Skinner 2008) as evidence that regulators may desire financial reporting opacity for forbearance purposes.
I discuss institutional details and prior research on U.S. bank supervision and intervention in Appendix A.
Forbearance and “too-big-to-fail” are distinct concepts. The latter refers to government bailouts and other support for systemically important institutions whose collapse could directly result in major problems for connected institutions. In contrast, regulators can practice forbearance on banks of any size, and the motives for such forbearance need not depend on the implications of the bank’s failure for the rest of the system. For example, it is widely accepted that regulators practiced forbearance on troubled thrifts during the U.S. savings and loan crisis in the 1980s, most of which would not have been considered “too big to fail” (Santomero and Hoffman 1998).
When the FDIC closes a bank, it sends a team to take over the bank’s operations. These teams can be large even for small banks. For example, the FDIC sent 80 agents to close a bank with 100 employees (Joffe-Walt 2009).
While forbearance can have benefits, it also has potential costs. If forbearance allows managers to increase risk-taking (“gambling for resurrection”), it could increase the ultimate cost of resolving the bank (Cole and White 2017). Furthermore, given the debtlike nature of their payoffs, uninsured creditors may prefer timely intervention in troubled banks, and they may run if they observe that the bank’s health is deteriorating (Diamond and Dybvig 1983). Ideally, regulators will balance the benefits of forbearance against these potential costs.
The OCC guide suggests that, to assess such tactics, examiners should pay attention to the loan loss reserve and how it relates to problem loans. The guide does not discuss the specific DELR measure that I employ in this study.
Regulators could affect provisioning behavior via less formal channels, such as conversations during on-site inspections. That said, enforcement actions can be used to address provisioning behavior.
While this discussion focuses on funding providers, other parties can inhibit forbearance. For example, politicians and the media can criticize regulators for a perceived lack of oversight. Furthermore, monitoring by one party can spark actions by another (e.g., political criticism of bank regulators could lead to depositor withdrawals).
Consistent with this idea, Rochet (2004) and Decamps et al. (2004) analytically show that if bondholders act as if a bank is in trouble (e.g., withdraw funds), regulators can be forced to intervene because forbearance becomes too costly for the regulator and/or government. While these models refer to bondholders, these bondholders are essentially providers of uninsured funding, similar to uninsured depositors.
There are two reasons why non-regulator bank stakeholders may not assume the worst when observing financial reporting opacity. First, as Fischer and Verrecchia (2000) show, if stakeholders are not fully aware of the manager’s reporting strategy, then reporting bias adds noise to the report, leading to an attenuated response. Second, some studies suggest that bank stakeholders value bank opacity because it allows deposits to act as a money-like security whose value does not fluctuate with the underlying assets, which in turn facilitates liquidity risk sharing and creates a medium of exchange without fear of adverse selection (Chen et al. 2021).
The alternative—not including controls for key financial indicators such as earnings and capitalization—would likely introduce omitted variable bias.
I focus on the recent 2007–09 financial crisis rather than past crises (e.g., the Great Depression, the savings and loan crisis) for two reasons: (1) the set of call report and state-level macroeconomic variables have greatly expanded over time, enabling me to better mitigate correlated omitted variable concerns; and (2) during other crises, non-closure interventions were not publicly disclosed, precluding their investigation.
Alternatively, one could examine this association in an international setting. This is a less promising path for several reasons. First, many countries have highly concentrated banking sectors, with few banks and even fewer regulatory interventions. Second, it is challenging to compare interventions across countries because they differ substantially. Third, differences in institutions across countries are likely correlated with financial reporting, making it difficult to isolate the association between financial reporting opacity and intervention. Finally, data on non-U.S. banks is generally lacking in quality and number of variables relative to call report data.
This follows the SNL classification scheme. Prior research shows that severe enforcement actions are an important form of regulatory intervention: they are associated with reductions in lending (Danisewicz et al. 2018; Kleymenova and Tomy 2020; Peek and Rosengren 1995; Peek and Rosengren 1996) and asset risk (Delis et al. 2017), increases in capital (Kleymenova and Tomy 2020), and negative consequences for the local economy (Danisewicz et al. 2018).
I investigate these two intervention types together in my main analyses for parsimony and because both represent serious interventions. In Section 5.2, I separately examine different intervention types.
For example, the St. Louis Federal Reserve’s financial crisis timeline starts in February 2007 (https://www.stlouisfed.org/financial-crisis/full-timeline). Intervention was less common before the crisis. For example, there were no bank closures in either 2005 or 2006.
Another benefit of DELR is that it can be measured for both public and private banks, in contrast to alternatives such as SEC restatements, which are only available for public banks. Most commercial banks are privately held.
I employ a 20-quarter window in order to balance two objectives: (a) having a sufficiently long time-series to estimate the component regressions, and (b) allowing banks to change their delayed loan loss recognition over time. For these regressions, bank-level variables are winsorized at the 1st and 99th percentiles within each quarter.
I do not include ΔGDP as a macroeconomic control here as it is only available at the state level from 2005.
I include DELR as a standalone variable, whereas some studies (e.g., Chen et al. 2021) interact DELR with bank fundamentals. The key difference is that other studies seek to examine how DELR affects stakeholder responses to bank fundamentals, and their findings suggest that DELR is a bank attribute associated with reduced stakeholder sensitivity to such fundamentals. This suggests that including DELR as a standalone variable will capture the regulator’s preference to forbear on banks for which the potential for stakeholder reactions to jeopardize forbearance is reduced.
The FDIC lists 325 banks that were closed during my sample period. Of these, 49 did not file call reports as of December 31, 2006, because they were savings banks or savings and loans (and therefore not commercial banks). Therefore, my closure sample begins with the remaining 276 commercial banks. Likewise, SNL has data on 1763 bank-level severe enforcement actions during my sample period, representing 1458 unique banks. But 209 did not file call reports as of December 31, 2006, because they were credit unions, savings banks, or savings and loans. An additional four banks filed call reports but received their enforcement action several years after filing their final call report; I exclude these enforcement actions from my analysis due to their unusual nature. Therefore, my severe enforcement action sample begins with the remaining 1245 unique commercial banks.
Continuous variables are winsorized at the 1st and 99th percentiles.
In untabulated analyses, I find that my inferences are unaffected if I instead cluster standard errors by bank, state, or regulatory combination (the OCC for nationally chartered banks, or the combination of the applicable state regulator with either the FDIC or Federal Reserve for state chartered banks).
It is important to note that this approach assumes that a given regulator’s leniency and/or policies equally affect all banks under its supervision, which may not be the case. Therefore, I cannot conclusively rule out this story. In an untabulated test, I find that including the Agarwal et al. (2014) leniency index does not affect my inferences.
The propensity scores are created by using the coefficients from a regression of High DELR on the full set of control variables. The matching is done without replacement and using a caliper of 0.001. My inferences are also robust to a PSM approach that matches on Intervention using all independent variables except DELR.
Additionally, in untabulated analyses I find that DELR exhibits low (< 0.1) correlations with key measures of bank health, such as the non-performing loans ratio, tier 1 capital ratio, or the level or change in the loan loss reserve, highlighting a benefit of the within-bank approach to measuring DELR.
I follow Oster (2019)‘s suggestion of setting R(max) to 1.3 times the R-squared from the fully specified regression. Furthermore, I conservatively assume that no control variables are fully observed. If I instead assume that certain key variables are fully observed (specifically Tier 1 Ratio, ROA, NPL, and Size), the Oster deltas increase to 2.56 and 252 in the panel and cross-sectional samples, respectively.
I find similar results in additional untabulated robustness tests: using probit regression, excluding commercial banks owned by non-U.S.-based holding companies, dropping all observations associated with banks that did not report a call report in the year before closure, including a measure of ΔHPI based on county-level housing price indices, and classifying the acquisition of Wachovia by Wells Fargo in 2008 as an intervention.
Including these additional variables removes banks that were closed in year t + 1 from the sample, as these banks would likely not file year-end call reports in year t+1. I only conduct this test using the panel sample; a similar test using the cross-sectional sample would eliminate all banks that did not file call reports in December 2010.
Beatty and Liao (2020) suggest an alternative approach to addressing this issue: using the addition to the nonaccrual asset in place of the change in non-performing loans. However, since additions to nonaccrual assets are not reported by commercial banks until Q3 2006, I cannot employ this approach. Beatty and Liao (2020) conclude that in the absence of data on additions to nonaccrual assets, one should use the change in non-performing loans (see p. 10 of their study), which I employ in my primary DELR measure.
In my primary analyses, I estimate DELR using the provision through the fourth quarter of year t, so that the timing of DELR matches the key metrics it impacts, such as capital ratios and earnings (which are also measured as of the fourth quarter of year t). However, this estimation requires ΔNPLt + 1, meaning that ΔNPL from the first quarter of year t+1 is used. If the bank’s intervention occurs in the first quarter of year t+1, there is an overlap between the measurement period of DELR and Intervention. I conduct two untabulated tests using the panel sample to examine whether the overlap is responsible for my findings. First, I exclude observations where intervention occurred in the first quarter of year t+1. Second, I re-estimate Eq. 1 measuring all bank-level independent variables as of the third quarter of year t (which eliminates the overlap). In each test, I find a strong negative association between DELR and intervention. Another potential issue is that requiring ΔNPL in the first quarter of year t+1 might result in the observation not being included in the analysis if the intervention resulted in the bank not filing a call report in the first quarter of year t+1. I find no such examples in the cross-sectional sample. In the panel sample, I find 20 observations that filed a call report in the fourth quarter of year t but not in the first quarter of year t+1. However, these observations had missing DELR values in prior quarters; thus, the missing call report was not responsible for their exclusion.
When the indicator variable is based on or is closely related to a continuous control variable, I remove the associated control variable from the regression in order to facilitate the interpretation of the indicator coefficient.
Chen et al. (2021) find that while opacity impairs depositor monitoring, the effect weakens during the early part of the financial crisis, which they attribute to depositors not attempting to distinguish between banks (i.e., not monitoring) during this period. In an untabulated analysis, I replicate their design in my sample, using High DELR as the measure of financial reporting opacity. I find that High DELR weakens the sensitivity of uninsured deposit flows to bank performance in my sample period, consistent with Chen et al. (2021)‘s main finding of opacity impairing depositor monitoring. Furthermore, this effect is weaker (stronger) in the earlier (later) part of my sample period, again consistent with their findings. My findings suggest that depositor monitoring, and thus the facilitating effect of DELR for forbearance, returned after the early crisis period. As noted above, this was also the period in which regulators were likely facing significant resource constraints and the DIF balance was at its lowest, suggesting that forbearance incentives had sharply increased. These findings are consistent with DELR facilitating forbearance by reducing stakeholder responses to bank fundamentals, and help explain why the DELR-intervention association is concentrated within the latter part of my sample period.
I find similar results when measuring state banking sector weakness using the deposit-weighted percentage of banks with a tier 1 capital ratio in close proximity to the 6% threshold for being considered well-capitalized.
The interaction term coefficients are not statistically different across models; this appears to be driven by the relatively large standard error in the federally chartered subsample. Furthermore, the main effect of DELR is statistically significant (insignificant) within the federally (state-) chartered banks. I interpret this as suggesting that both regulators exploit DELR for forbearance, but that the motivation for such forbearance differs across regulators: the state banking sector is a primary factor for state regulators, whereas non-regional factors (such as resolution costs) motivate forbearance for national regulators.
The underlying assumption is that bank lending generally occurs in counties where the bank has branches. The FHFA does not report county-level ΔHPI when there are too few transactions to create the index, so I cannot create this variable for all bank-years in the panel sample.
Put differently, regulators may still prefer to forbear on banks with less non-regulator monitoring, but they should be less dependent on financial reporting opacity to facilitate such forbearance. For example, while regulators may desire to forbear on banks with significant insured deposits, opacity should be less important for facilitating such forbearance since insured depositors, whose claims are not at risk, are less likely to monitor the bank.
In my sample, on average approximately 11% of assets are funded by brokered deposits and non-deposit liabilities. Given that the average equity-to-asset and cash-to-asset ratios are 11 and 5.5%, respectively, runs by these funding providers could cause solvency and liquidity problems for banks.
I classify a bank as Private if I cannot link it or its holding company to a CRSP or CUSIP identifier (which signals the presence of traded equity or debt), either via the call reports or manual inspection. Since there are multiple potential sophisticated monitors (equity analysts, bond raters, hedge funds, the business press, etc.), this analysis instead examines banks that likely lack collective exposure to such monitoring. In untabulated analyses, I find that the DELR-intervention association is no longer significant in the presence of two specific sophisticated monitors (equity analysts and institutional owners), consistent with the tabulated findings.
I only examine bank-level enforcement actions in this analysis; personnel-level actions are excluded.
For example, a severe enforcement action may require that the regulator subsequently conduct an on-site inspection or additional off-site monitoring, and closing a bank requires the FDIC to send a team of employees.
A reading of a random selection of non-severe enforcement actions suggests that the issues addressed arose either during onsite inspections or via offsite monitoring, consistent with regulatory monitoring leading to their issuance.
I find similar results when using lagged time-varying bank covariates in the panel sample (results untabulated).
It is important to note that, despite this apparent benefit to DELR, banks and/or regulators may not uniformly seek to increase it for several reasons. First, prior research suggests that banking sectors with greater DELR have increased risk-taking (Bushman and Williams 2012); thus, there may be a cost for allowing greater DELR on average. Second, regulators may apply greater scrutiny to high DELR banks during non-crisis periods, while exploiting DELR during a crisis to avoid intervention. This is consistent with the intuition, from Morrison and White (2013), that regulators may prefer transparency ex ante, but opt for opacity during a crisis as it allows them to forbear secretly without creditors knowing. Finally, more sophisticated stakeholders, such as institutional investors, may be reluctant to provide capital or liquidity to high DELR banks. This is difficult to examine empirically in my sample but is consistent with prior research (Bushman and Williams 2015).
I follow prior research (Curry et al. 1999; Lucca et al. 2014) in using the term “enforcement actions” to collectively refer to the various formal actions that bank regulators can take against banks, including written agreements, cease-and-desist orders, prompt corrective action directives, and less severe interventions. Regulators can also issue enforcement actions against individuals, but these actions are not examined in this study.
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Acknowledgements
This paper is based on my dissertation, completed at the University of North Carolina at Chapel Hill. I greatly appreciate the guidance and support of my dissertation committee: Ed Maydew (chair), Robert Bushman, Jennifer Conrad, Wayne Landsman, Mark Lang, and Doug Shackelford. I also greatly appreciate guidance and support from Eva Labro. I thank Peter Easton (the editor), three anonymous referees, Jeff Abarbanell, John Barrios, Phil Berger, Dirk Black, Craig Chapman, Ted Christensen, Joshua Coyne, Lisa De Simone, Ferdinand Elfers (discussant), Joao Granja, Martin Jacob, Anya Kleymenova, Randall Kroszner, Christian Leuz, Valeri Nikolaev, Vivek Raval, Richard Sansing, Lorien Stice-Lawrence, Stephen Ryan, Kelly Wentland, Jaron Wilde, and workshop participants at Columbia University, Dartmouth College, the EAA Doctoral Colloquium, Harvard University, KU Leuven, New York University, Northwestern University, Rice University, Stanford University, the University of Chicago, the University of Michigan, the University of Minnesota, the University of North Carolina, the University of Pennsylvania, Yale University, and the CEPR-University of Amsterdam Workshop on Regulatory Forbearance for helpful comments. I thank Thomas Lambert for sharing his data on bank lobbying during the financial crisis.
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Appendices
Appendix 1. Commercial bank supervision and intervention in the United States
1.1 Commercial bank supervision in the United States
U.S. banks are subject to regulatory supervision, in which individual institutions have their safety and soundness assessed. The purpose is to maintain the stability of both the institutions and the overall banking sector (Federal Deposit Insurance Corporation 2003; Federal Reserve 2005). In the supervisory process, regulators gather information to assess whether the bank is operating in an unsafe or unsound condition and whether it complies with laws and regulations. Based on their assessment, regulators may require that corrective actions be taken (Goldsmith-Pinkham et al. 2016).
U.S. bank supervision involves both on-site examinations and off-site monitoring (Federal Reserve 2005). The former are usually conducted either every 12 or 18 months, depending on bank size (Agarwal et al. 2014). For problem institutions, inspections can occur more frequently and delve more deeply. Off-site monitoring primarily involves analyzing financial data. Banks are required to file Reports of Condition and Income (“call reports”) each quarter, which include a detailed balance sheet and income statement. Regulators use call report data to identify problem institutions, which then receive further attention (Federal Reserve 2005). Thus, the regulator depends, at least partially, on privately held data that is unobservable to creditors, other bank stakeholders, and the public.
Supervision is carried out by both federal and state banking regulators. During the recent financial crisis, there were four U.S. national bank supervisors: the Federal Deposit Insurance Corporation (FDIC), the Federal Reserve, the Office of the Comptroller of the Currency (OCC), and the Office of Thrift Supervision (OTS). Furthermore, there are individual banking regulatory agencies in each state. State regulators supervise state-chartered banks, whereas the OCC supervises banks with national charters. The state-chartered banks are subject to state regulatory as well as national supervision: the Federal Reserve oversees banks that are Federal Reserve members, and the FDIC oversees the non-Fed member banks. For state-chartered banks, on-site inspections are often rotated between federal and state regulators.
1.2 Bank regulatory intervention in the United States
Supervisory actions are typically triggered by on-site examinations but can also occur when the regulator becomes aware of bank problems through the call reports. Some interventions are less serious and not made public; these include matters requiring attention (MRAs) and matters requiring immediate attention (MRIAs) (Goldsmith-Pinkham et al. 2016). Other informal supervisory actions include memorandums of understanding, commitments, and board resolutions. These actions are usually precursors to formal intervention; their goal is to notify bank management about concerns with practices or soundness before a formal intervention becomes necessary. Formal interventions are typically referred to as “enforcement actions” and can include cease-and-desist orders, formal written agreements, and prompt corrective actions.Footnote 49 They are usually legally enforceable through the court system and are published by the regulator. The most severe of these restrict bank operations, such as dividend payments or loan creation, until the bank fixes certain problems, including low capital levels or risky lending practices. Less severe enforcement actions include call report infractions, fines for unsafe or unsound practices, or memorandums of understanding (to the extent that they are published).
When a bank’s condition becomes sufficiently unsound (e.g., insolvency), regulators are tasked with its timely closing. This process differs from the bankruptcy of a non-banking corporation in that insolvent banks can continue to operate by issuing new deposits to fund old liabilities (Brown and Dinç 2011). The bank’s primary regulator (either the state regulator or the OCC) makes the closure decision. When it does, it notifies the FDIC, the regulator responsible for resolving failed banks (Federal Deposit Insurance Corporation 2003). The FDIC then devises a plan for resolving the bank and sends personnel to maintain its day-to-day operations. Eventually, the FDIC either arranges for a healthier bank to acquire the troubled one or liquidates it, paying off all insured and some portion of uninsured deposits.
The Federal Deposit Insurance Corporation Improvement Act of 1991 (FDICIA) requires that U.S. bank regulators take prompt corrective action to resolve bank problems, which is intended to limit regulators’ ability to delay intervention. However, U.S. regulators still have some flexibility about when to intervene in an institution (Edwards 2011; Government Accountability Office 2011). For example, during my sample period, banks were generally considered undercapitalized if they had a Tier 1 capital ratio of less than 4%. However, regulators could close banks with capital ratios above this level if the banks were deemed unsafe for other reasons. Further, a regulator could allow a bank with a Tier 1 ratio below this level to continue operating if it entered into a written agreement with the regulator detailing how it would remedy its capital situation. Enforcement actions, because they address issues with some subjectivity, generally allowed regulators more flexibility with intervention, relative to closures.
Prior research on regulatory intervention determinants has primarily focused on bank closures, likely because they represent the most significant intervention type. This research generally finds that the important determinants of closure are related to bank health, including capitalization, profitability, and loan portfolio quality (Wheelock and Wilson 2000; Balla et al. 2015). Several studies document additional determinants during the 2007–09 financial crisis, including real estate market exposure (Antoniades 2015), local economic and housing market conditions (Aubuchon and Wheelock 2010), dividend payments to insiders (Henderson et al. 2015), loan loss reserve capital add-backs (Ng and Roychowdhury 2014), and competition among local banks (Akins et al. 2016). Evidence on the determinants of enforcement actions, an important pre-closure intervention type, is less extensive. Prior work suggests that lower profitability and loan portfolio quality (Curry et al. 1999), issues with capitalization and liquidity (Goldsmith-Pinkham et al. 2016), and state-level competition (Akins et al. 2016) are associated with the incidence of enforcement actions.
Appendix 2. Variables
1.1 Mapping control variables into OCC red flag categories
1.1.1 Rapid growth
Rapid or aggressive growth is a sign that a bank may have increased its exposure to risk and therefore its susceptibility to a sudden change in economic conditions. I include several control variables that capture the red flags for this category. Loan Growth will be higher for banks that aggressively expand lending activity. Brokered Deposits captures the bank’s reliance on brokered deposits, which are often used when a bank’s desire to grow outstrips its ability to attract local deposits. When assessing a bank experiencing rapid growth, the OCC also considers the bank’s capitalization (Tier 1 Ratio), profitability (ROA and ΔROA), lending standards (NPL and ΔNPL), off-balance-sheet exposures (e.g., Unused Commitments), and allowance for loan and lease losses (ALLL) adequacy (e.g., LLR, NCO to LLP Ratio), among other factors.
1.1.2 Macroeconomic conditions
Prior banking crises have shown a correlation between bank performance and local economic conditions (OCC 2001). I include the change in the state housing price index (ΔHPI) since a substantial portion of bank assets are invested in real estate loans. I include the change in the deposit-weighted county-level unemployment rate and the change in state gross domestic product (ΔUnemp and ΔGDP, respectively) to capture the economic conditions facing individuals and businesses. Finally, to capture other factors that might affect the stability of local banks, I include State Closures as a proxy for the health of the local banking sector.
1.1.3 Management oversight
Management is the dominant factor in bank performance (OCC 2001). Poor management oversight can lead to poor loan underwriting, poor risk management, and a greater probability of regulatory intervention. Since ex ante oversight is difficult to observe, I measure it using an outcome-based approach. First, I include ROA and ΔROA, since poor quality management should be less profitable and exhibit lower earnings growth on average. I also include ROA Volatility, as poor management oversight could lead to fluctuations in profitability. Banks with such oversight are likely to have lower quality loan portfolios (NPL and ΔNPL). These banks may also be more likely to need to restate their call reports (Restatement). However, poor management oversight might mean that accounting errors are not revealed, leading to fewer call report restatements. Finally, I include variables that capture prior enforcement actions aimed at the bank (Prior EA) or its personnel (Prior Personnel EA). The former variable is likely associated with remediation in poor management quality, whereas the latter may be a contemporaneous indicator of management problems. An additional benefit of including these control variables is that they mitigate the risk that my findings are driven by regulators intervening into troubled banks, which affects both subsequent financial reporting choices (i.e., provisioning timeliness) and subsequent regulatory intervention decisions.
1.1.4 Risk management
Banks with poor-quality risk management systems may take excessive risks, making them more likely to experience regulatory intervention during the crisis. My primary proxy for risk management is ROA Volatility; banks with poor risk management should experience larger fluctuations in profitability. Since banks’ risk management is responsible for overseeing loan underwriting, banks with poor risk management processes are more likely to have loans become nonperforming (NPL and ΔNPL). I include Noninterest Revenue, as a bank with greater emphasis on non-lending activities (such as securities trading) may be at greater risk of a risk management failure, although revenue from non-lending activities may smooth out fluctuations in lending revenues. Finally, better capitalized banks (Tier 1 Ratio) may be better insulated from risk management failures, and larger banks (Size) may have both more extensive risk management processes and more exposure to potential risk management failures.
1.1.5 Off-balance-sheet exposures
There are three major sources of off-balance-sheet exposures for U.S. commercial banks: unused credit lines, securitizations with recourse, and derivatives. Each source carries different risks for the bank. For example, unused credit lines expose banks to liquidity risk during a crisis if there is a run (via drawdowns) by credit line borrowers. I capture overall exposure to unused credit lines with Unused Commitments. Since most of my sample banks do not engage in securitization or derivatives, I used indicator variables to capture involvement in these activities (Securitization Activity and Derivatives Activity, respectively). Finally, larger banks (Size) are more likely to engage in securitization and invest in derivatives.
1.1.6 Asset quality
If a bank’s loan portfolio quality suddenly deteriorates, this could threaten the bank’s solvency, necessitating regulatory intervention. I measure loan portfolio quality using NPL and ΔNPL, which capture the level and change, respectively, in non-performing loans. I also include a measure of the bank’s focus on real estate lending (Real Estate Loans), since one of the key developments during the financial crisis was the collapse of the real estate market. Regulators also examine ALLL (e.g., LLR, NCO to LLP Ratio) and macroeconomic (e.g., ΔHPI) red flags when assessing asset quality. Finally, poor asset quality is a greater concern for poorly capitalized banks (Tier 1 Ratio), as these banks are less able to absorb unexpected losses.
1.1.7 Allowance for loan lease losses (ALLL) adequacy
If a bank fails to ensure the adequacy of its ALLL, it can be subject to regulatory intervention. Since regulators often compare the nonperforming loans ratio to the ALLL to assess the sufficiency of the latter to cover current and expected loan losses, I include variables for them: LLR, the bank’s loan loss reserve (also known as the ALLL), and NPL, the bank’s non-performing loans ratio. Additionally, I include NCO to LLP Ratio, which captures whether the bank is replenishing the ALLL through provisioning after it is reduced by charge-offs. Finally, when assessing the adequacy of the ALLL, regulators will likely take into account recent changes in loan portfolio quality (ΔNPL) as well as the bank’s ability to absorb unexpected losses (Tier 1 Ratio).
1.1.8 Liquidity
While the likelihood of a run by insured depositors is low, banks can encounter liquidity problems if uninsured depositors run (either through withdrawal or failure to rollover) or if borrowers unexpectedly draw down on credit lines. This in turn can necessitate regulatory intervention. I capture the bank’s stock of liquid assets by including Cash to Deposits Ratio. I capture exposure to risky (i.e., more run-prone) funding with Uninsured Deposits and Brokered Deposits, as uninsured and brokered deposits are more likely to run from the bank in a crisis. I capture exposure to sudden credit line drawdowns with Unused Commitments. Finally, I capture the mismatch in the bank’s balance sheet with Income Gap.
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Gallemore, J. Bank financial reporting opacity and regulatory intervention. Rev Account Stud 28, 1765–1810 (2023). https://doi.org/10.1007/s11142-022-09674-4
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DOI: https://doi.org/10.1007/s11142-022-09674-4
Keywords
- Regulatory intervention
- Bank closure
- Enforcement actions
- Forbearance
- Loan loss provisioning
- Banking crises