loan loss reserves, regulatory capital, and bank failures
TRANSCRIPT
Loan Loss Reserves, Regulatory Capital, and Bank Failures: Evidence from the 2008-2009 Economic Crisis
Jeffrey Nga [email protected]
Sugata Roychowdhuryb [email protected]
Abstract
This paper tests the influence of regulatory capital guidelines on the link between banks’ loan loss reserve decisions in 2007 and the risk of bank failure during the 2008-2009 economic crisis. Our primary finding is that larger loan loss reserve increases in 2007 are associated with an increased risk of bank failure in 2008-2009, but only for banks that are allowed to add back loan loss reserves as Tier 2 capital. The results suggest that the ability to add back loan loss reserves to regulatory capital creates the illusion of non-declining financial health, which allows banks to avoid taking the actions necessary to reduce failure risk. Our results thus challenge the notion that the buffer created by loan loss reserves lowers bank failure risk. Simultaneously they question the soundness of recent regulatory proposals to allow even more loan loss reserves to be added back in the calculation of regulatory capital. Keywords: bank failure, bank risk, regulatory capital, capital adequacy, loan loss reserves, loan loss provisions, loan charge-offs JEL Codes: G21, G28, G32, G38, M41, M48
First draft: January 27, 2010 Current draft: July 16, 2010
_____________________________ a. MIT Sloan School of Management, 50 Memorial Drive E52-325, Cambridge, MA 02142. b. Boston College, 140 Commonwealth Avenue, Fulton 520A, Chestnut Hill, MA 02142.
This paper has benefited from comments we received from Anne Beatty, Michelle Hanlon, S.P. Kothari, Ewa Sletten, Ross L. Watts, and from seminar participants at the University of Missouri and Boston College. We thank the Federal Deposit Insurance Corporation for responding to our queries on regulations regarding bank capital calculations. All remaining errors are ours.
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1. Introduction
This paper is motivated by recent concerns, prompted by the financial crisis of 2008-2009,
that regulatory capital guidelines on loan loss reserves can generate dysfunctional outcomes.
Under current guidelines, a bank can add back its loan loss reserves, which are cumulative
accrued loan losses, as Tier 2 regulatory capital, up to a maximum of 1.25% of a bank’s gross
risk-weighted assets. The treatment of loan loss reserves as capital has received considerable
attention in the wake of the financial crisis, with regulators actively debating its pros and cons.
For example, in speaking at the American Bankers Association meeting on March 17, 2010,
Comptroller of the Currency John Dugan argued for the relaxation of restrictions on the inclusion
of loan loss reserves as capital, to encourage banks to report adequate and timely reserves. A day
later at that same meeting, Federal Deposit Insurance Corporation (FDIC) Chairperson Sheila
Bair contested this view, opining that “letting more reserves count [towards capital] could
dramatically, in our view, dilute the quality of capital.”1 In light of this debate, we examine
whether regulatory capital guidelines influence the association between loan loss reserve
accounting and the risk of bank failure during the financial crisis.
Various hypotheses exist in the literature regarding the link between loan loss reserve
changes and bank failure risk. First, accounting accruals are expected to reflect contemporaneous
economic events that have implications for future cash flows (Dechow 1994). For banks,
increases in loan loss reserves reflect increases in the accrued expected losses in their loan
portfolios. Thus, banks with larger increases in loan loss reserves are expected to experience
more severe cash flow losses in their loan portfolios in the future and will thus be more likely to
fail, particularly during an economic crisis (the “troubled-bank” hypothesis). Second, a number
of academic studies (Elliott et al. 1991, Wahlen 1994, and Beaver and Engel 1996) indicate the
1 She also referenced the late L. William Seidman, a former chairman of the FDIC, in saying that the rationale for adding back reserves as capital was ambiguous.
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possibility that in practice, banks report larger loan loss provisions, thus increasing their loan loss
reserves, when they are financially stronger and expect better future performance. Hence, banks
with greater loan loss reserve increases signal financial strength and are more likely to survive
during a financial crisis (the “signal-of-strength” hypothesis).
We propose that the relation between failure risk and loan loss reserve decisions during a
period of worsening credit quality is likely to be more complex than has been suggested by either
the troubled-bank or signal-of-strength hypothesis. This is because we expect existing regulatory
guidelines to have a particularly pronounced and possibly dysfunctional effect on bank
managers’ incentives during times of deteriorating economic conditions. The dysfunctional
effect can arise because, according to current regulatory guidelines, loan loss reserve increases
during periods of worsening credit conditions do not necessarily result in a decline in regulatory
capital, and in fact can increase regulatory capital.2
Regulatory capital adequacy is a key metric that regulators rely on to judge a bank’s
solvency status (see Estrella et al. 2000), and increasing loan loss reserves can create an illusion
of improving solvency if they result in higher regulatory capital. This illusion of improving
financial health can lead to more lenient regulatory monitoring of banks with deteriorating loan
quality.3 The impression of financial health can also allow banks to avoid taking prudent actions
such as restricting risky lending, improving collection efficiency, etc., which would strengthen
their underlying financial position. Lenient regulatory monitoring and inaction in the presence of
underlying loan problems can threaten banks’ financial stability, particularly when an economic
2 The mechanics involved in the regulatory capital adequacy calculation are discussed in detail in Section 2.1. Briefly, Tier 1 capital consists primarily of shareholder equity. Loan loss provisions, which increase loan loss reserves and reduce net income, decrease shareholder equity net of tax. Since the entire loan loss reserve balance counts as Tier 2 capital, every dollar of the loan loss provision increases the sum of Tier 1 and Tier 2 capital by the tax rate times that dollar. This increase happens particularly over the range that Tier 2 capital is below the maximum allowable limit of 1.25% of gross risk-weighted assets. 3There exists a long history of bank regulators relying on low capital adequacy ratios to identify banks that require greater supervisory attention (Estrella et al. 2000).
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downturn occurs. This generates the following prediction: loan loss reserve increases in 2007 are
positively associated with bank failure risk during 2008-2009, but only among banks that are less
constrained in adding back loan loss reserves as Tier 2 capital. Importantly, we do not expect
loan loss reserve increases to exhibit a positive association with bank failure risk when the
former cannot be added back as Tier 2 capital; in that range, loan loss reserve increases via
provisions unambiguously reduce regulatory capital. We refer to our hypothesis as the
“regulatory-capital-effects” hypothesis.
For our analyses, we obtain bank failure data from Federal Deposit Insurance
Corporation (FDIC) press releases. Data on banks’ accounting information is obtained from the
call reports commercial banks file with the Federal Reserve, Federal Deposit Insurance
Corporation, and/or the Office of the Comptroller of the Currency. We also separately
investigate the two components of loan loss reserve changes, given their distinct balance sheet
and income statement effects: (i) loan loss provisions and (ii) loan charge-offs.4 Loan loss
provisions increase loan loss reserves; they reduce net income and hence, stockholders’ equity.
Loan charge-offs reduce loan loss reserves, and have no income statement effect.
We document that, after controlling for various economic determinants of bank failure,
loan loss reserve changes are positively associated with bank failure risk. More importantly, the
positive association between loan loss reserve changes and bank failure risk is restricted to banks
with loan loss reserves that are below the maximum limit allowable as Tier 2 capital. Above that
limit, higher loan loss reserve changes are not significantly associated with failure risk. The
results are as predicted by the regulatory-capital-effects hypothesis, and cannot be explained by
either the troubled-bank or signal-of-strength hypothesis. The troubled-bank hypothesis in
4 Loan loss provisions are used to adjust the loan loss reserve account to the desired balance. A loan charge-off decision involves identifying specific accounts in default and removing them from the books.
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particular cannot explain why loan loss reserve increases would be associated with higher bank
failure risk only when they are below the maximum limit allowable as Tier 2 capital.
In examining the components of loan loss reserve changes, we find that bank failure risk
is associated positively with loan loss provisions and negatively with loan charge-offs. In the
cross-sectional analyses, we document that failure risk’s positive (negative) association with loan
loss provisions (loan charge-offs) is more pronounced when banks are less constrained in
including loan loss reserves as capital. Since loan loss reserves increase with loan loss provisions
and decrease with charge-offs, the results are consistent with the regulatory-capital-effects
hypothesis and inconsistent with the signal-of-strength hypothesis. Further, the troubled-bank
hypothesis, which would predict that both loan loss provisions and charge-offs are associated
with increased failure risk, cannot explain the observed results.
Next, we investigate whether the association between failure risk and loan loss reserve
decisions is at least partially a result of managers’ accounting discretion.5 If managers exercise
discretion in loan loss reserve accounting in order to report higher capital, and if the upward-
managed capital allows managers to avoid addressing underlying problems, then bank failure
risk should be positively associated with managers’ discretionary loan loss provisions. Using
models for normal loan loss provisions and charge-offs, we find that bank failure risk is
positively associated with discretionary loan loss provisions and negatively with discretionary
charge-offs. Further, failure risk’s positive association with discretionary loan loss provisions,
and its negative association with discretionary charge-offs, is restricted to banks that are less
constrained in reporting loan loss reserves as Tier 2 capital.
In additional analyses, we first document preliminary evidence that banks’ risky activities
in the first quarter of 2008, including lending, appear to be positively associated with changes in 5 Evidence in the literature indicates that managers can actively use the discretion available to them in recording loan loss provisions to manage their reported regulatory capital upwards (Moyer 1990, Beatty et al. 1995, Ahmed et. al. 1999).
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Tier 2 capital, but negatively associated with changes in Tier 1 capital. This finding supports the
concern raised by our primary results: the ability of loan loss reserves to increase Tier 2 capital
engenders greater failure risk. Second, we measure bank failure severity using the cost of the
failure to the FDIC. We find that within the sample of failed banks, bank failure severity varies
positively with loan loss reserve changes. Thus, not only are loan loss reserve increases
associated with greater failure risk, they also contribute to more severe bank failures.
Recently, there has been considerable interest in banks’ loan loss accounting practices.
Beatty and Liao (2009) argue that more conservative loan loss provisioning practices appear to
make banks’ lending less sensitive to their capital adequacy during recessionary periods.
Bushman and Williams (2009) provide evidence suggesting that greater discretion in loan loss
provisioning weakens regulators’ ability to monitor and discipline banks. Our paper focuses on a
related but distinct issue: regulations regarding capital can themselves have dysfunctional
consequences on bank failure risk. In this respect, our study is related to a large body of literature
that examines the link between regulatory capital requirements and bank risk (Koehn and
Santomero 1980, Buser et al. 1981, Furlong and Keeley 1989, Saunders et al. 1990, Shrieves and
Dahl 1992, Diamond and Rajan 2000, Rime 2001, Laeven and Levine 2009). Specifically, we
document that regulatory capital guidelines that allow total capital to increase with loan loss
reserves can, during economic crises, contribute to heightened bank failure risk.
The findings in our paper have policy implications. By including loan loss reserves,
regulatory capital departs from the conventional notion of capital as a cushion of funds available
to partially insulate a bank against shocks to the value of deposit liabilities (see, for example,
Diamond and Rajan 2000). As noted earlier, in the wake of the economic downturn in 2008-
2009, bank regulators are considering increasing the limit to which loan loss reserves can count
towards Tier 2 capital. This proposal has received the support of banks and bank organizations
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such as the Georgia Bankers’ Association.6 Following intense bank lobbying at the time of the
issuance of Statement of Financial Accounting Standards (SFAS) 166 and 167 in late 2009,
regulators allowed “transitional relief” for banks from the 1.25% limit on Tier 2 capital.7 Our
findings, however, question the very rationale for considering loan loss reserves as Tier 2 capital.
At the very least, the results strike a cautionary note against expanding the add-back of loan
reserves to regulatory capital.
The rest of the paper is organized as follows. Section 2 discusses our setting. Section 3
describes our sample construction and data. Our results are presented in Section 4, and section 5
concludes.
2. Setting and Hypotheses
The economic crisis of 2008-2009 provides a rich setting for our paper because in
addition to raising questions about the role of loan loss reserves and their treatment as regulatory
capital, it is characterized by a significant number of bank failures. Bank failures are
unambiguously negative outcomes, and they facilitate the direct examination of the
consequences of increasing loan loss reserves, as well as the impact of regulatory capital
guidelines. Below, we discuss two critical institutional factors – (a) the calculation of regulatory
capital and (b) the process of a bank failure – before discussing our hypotheses.
6 In its comment letter to the Federal Reserve Board on October 15, 2009 relating to bank regulators’ proposed rule-making on risk-based capital guidelines and related issues, Discover Financial Services argues for the increase or elimination of the current cap on loan loss reserve amount eligible to qualify as Tier 2 capital (Federal Reserve Board, 2009). In an oral statement to the Domestic Policy Subcommittee of the U.S. House Oversight and Government Reform Committee on November 2, 2009, Joe Brennan, President and CEO of the Georgia Bankers Association argued for a higher limit on the loan loss reserves to be counted as regulatory capital. He stated that 76% of all Georgia banks were adversely affected by the restriction and that billions in capital among Georgia banks would be freed up to support more lending if the limit is suspended. 7 In the first two quarters after the implementation of SFAS 166 and 167 in November 2009, a banking organization, under certain conditions, is permitted to include without limit in Tier 2 capital the full amount of the loan loss reserves.
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2.1 Regulatory capital and loan loss reserves
The capital adequacy ratio, or the ratio of regulatory capital to risk-weighted assets, is the
metric most widely relied on by regulators to monitor bank solvency (Estrella et al. 2000). There
are two main sources of regulatory capital: Tier 1 and Tier 2. Tier 1 capital is “core” capital; it
includes shareholders’ equity (the primary component) and disclosed reserves. Tier 2 capital is
“secondary” capital; it includes general loss reserves, undisclosed reserves, and subordinated
term debt. The International Basel Committee requirements specify a minimum limit of 4% for
Tier 1 capital, and 8% for total capital.
In practice, Tier 2 capital consists primarily of loan loss reserves, with the restriction that the
latter is limited to 1.25% of gross-risk-weighted assets (GRWA).8 From an accounting
perspective, loan loss reserves recognize expected losses in the loan portfolio. That is, losses are
accrued when expected, as opposed to expensed when realized. Hence, loan loss reserves provide
a buffer to absorb declines in the value of the bank’s loan portfolio when the losses are realized
in the future. In part, the add-back of loan loss reserves to regulatory capital is meant to serve as
an incentive for the timely recording of loan losses (Wall and Koch 2000).
A simple example illustrates the role of loan loss reserve components in influencing
regulatory capital.9 Assume a bank increases its loan loss reserves by reporting a loan loss
provision of $100, and the statutory tax rate is 40%. This transaction, ceteris paribus, has two
effects on regulatory capital: i) a Tier 1 effect and ii) a Tier 2 effect. The loan loss provision
reduces after-tax income by $100*(1 - tax rate), or $60, which in turn reduces shareholders’
equity, and hence Tier 1 capital, by $60. Since banking capital regulations allow loan loss
8 Gross risk-weighted assets equal risk-weighted assets used in the computation of the capital ratios plus excess allowance for loan and lease losses plus allocated transfer risk reserve. The limit of 1.25% of gross risk-weighted assets on the amount of the loan loss reserves that a banking organization may include in tier 2 capital is a standard included in the first capital accord of the Basel Committee on Banking Supervision (Basel Accord). See Basel Committee on Banking Supervision, International Convergence of Capital Measurement and Capital Standards (1988), paragraph 21. 9 We thank the FDIC for confirming that our example is correct in representing the effect of the regulations.
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reserves to be considered as Tier 2 capital, Tier 2 capital increases by the provision amount of
$100. Total regulatory capital (the sum of Tier 1 and Tier 2) increases by $40 as a result of the
loan loss provision, that is, the tax rate times the provision amount. If loan loss reserves prior to
the provision were already equal to or greater than 1.25% of GRWA, the $100 provision in the
example would not increase Tier 2 capital. If loan loss reserves were below the 1.25% limit but
significantly close to it, it is possible that only a portion of the $100 loan loss provision would
count towards Tier 2 capital, not the entire amount.
Next, consider the effect of increasing a loan charge-off, assuming no loan loss provision. A
charge-off of $100 would reduce loan loss reserves by $100, ceteris paribus. Since charge-offs
do not affect the shareholders equity account, the sole effect of the charge-off would be to reduce
Tier 2 capital, and hence total regulatory capital, by $100 (to the extent that loan loss reserves
were within the maximum allowable limit).
The example highlights that loan loss provisions and loan charge-offs have significantly
distinct effects on regulatory capital. Furthermore, the effect of loan loss changes on regulatory
capital is dependent on the size of total available Tier 2 capital relative to the maximum limit
allowable under current regulations. Section 2.3 discusses the implications of these institutional
features for the relation between loan loss reserve changes and the likelihood of bank failure. The
process of such a failure is discussed below.
2.2 Bank failure
A bank failure is an extreme event involving the chartering authority or the Federal
Deposit Insurance Corporation (FDIC) “closing” the bank.10 Closing a bank involves shutting
10 The chartering authority for state-chartered banks is usually the state banking department; for national banks, the Office of the Comptroller of the Currency (OCC); and for federal savings institutions, the Office of Thrift Supervision (OTS). While it is much more common for the chartering authoring to close a bank, the FDIC has the authority, under the FDIC Improvement Act of 1991, to close any bank that it considers to be critically undercapitalized and that does not have a plan to restore capital to an adequate level.
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down its operations, re-distributing its assets and liabilities and, if necessary, paying off insured
depositors.
Generally, a bank is closed when the regulating authority determines that it is “critically
undercapitalized” and is deemed unable to meet its obligations to depositors and other creditors.
The key attribute determining undercapitalization is insolvency, which occurs when the bank’s
assets are worth less than its liabilities according to either book or market values.11 The Federal
Deposit Insurance Corporation Improvement Act (FDICIA) of 1991 requires regulators to close
banks before they reach book-value insolvency, since the market values of bank assets are
uncertain and typically, for troubled banks, below their book values.
In the event of a bank failure, the FDIC acts as a receiver and is in charge of the failure
resolution. FDICIA mandates the use of the least-cost resolution method for bank failures, the
objective of which is to minimize the present value of the net losses incurred by the FDIC. There
are two primary types of failure resolution methods: (1) purchase-and-assumption transactions
and (2) deposit pay-offs.
In a purchase-and-assumption transaction, a healthy bank acquires the failed bank by
purchasing “some or all” of the assets and assuming “some or all” of the liabilities. The FDIC
often provides assistance to the acquiring bank, e.g., in the form of loan-loss sharing agreements,
and then liquidates the remaining assets and liabilities, internalizing the cost of doing so. The
acquiring bank usually compensates the FDIC for the franchise value from the failed bank’s
established customer relationships, which helps reduce the insurer’s resolution cost. In a deposit-
payoff transaction, the FDIC pays the failed bank’s depositors the full amount of their insured
11 Another reason for bank failure is illiquidity, which occurs when a bank is unable to meet its current obligations as they come due. For example, when depositors are concerned that a bank is failing, they may withdraw their deposits and precipitate a liquidity crisis at the bank (bank run). Illiquidity appears to drive bank failures more commonly in the European Union. Because of deposit insurance and the U.S. Federal Reserve’s ability to provide liquidity, banks in the United States typically fail because they are insolvent as opposed to illiquid.
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deposits. Typically deposit payoffs are observed when no other bank is interested in assuming
the assets and liabilities of the failed bank.12
2.3 Hypotheses
Given the widespread acknowledgement of the role banks’ poor lending decisions played in
the 2008-2009 economic crisis, managers’ decisions regarding loan loss reserves have been
under considerable scrutiny. In particular, a key question has been whether increasing loan loss
reserves in the period leading up to the 2008-2009 economic downturn would have enabled
banks to better weather the crisis. Direct evidence on the effect of loan loss reserves on bank
failures is lacking.
A relation between loan loss reserves and the likelihood of bank failure is expected on
various grounds. Elliott et al. (1991), Whalen (1994) and Beaver and Engel (1996) argue that
banks are likely to make greater loan loss provisions (and increase their loan loss reserves) when
they expect to perform better in the future. In other words, greater loan loss provisions are a
signal of strength. Consistent with this “signal of strength” hypothesis, they document that loan
loss provisions are associated with contemporaneously positive market returns. However, these
findings have been subsequently questioned in the literature (see Ahmed et al. 1999). Further,
loan loss provisions and charge-offs recorded in periods close to an economic recession are
likely to reflect the accumulated effects of the risks assumed in past lending practices. This
“troubled bank” hypothesis suggests that banks report the loan loss provisions and charge-offs
simply because they are compelled to, as they contend with more negative realizations in the
12 Variations in the two primary methods exist. For example, in a deposit transfer transaction, the FDIC transfers the insured deposits to a healthy bank that is willing to be an agent of the FDIC. The depositors can either withdraw their deposits or keep them in the new bank. In a bridge transaction, the FDIC itself temporarily acquires the assets and liabilities and takes over the operations of the failed bank as it decides upon the least-cost resolution method. In a more significant departure, the FDIC can engage in an open-bank transaction, in which it provides financial assistance to the bank while it continues operations. Open-bank transactions are not classified as bank failures in our sample, since they are implemented when banks’ liquidity and/or solvency issues are perceived as temporary.
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periods immediately preceding an economic downturn. This can potentially generate a positive
association of both loan loss provisions and charge-offs with the likelihood of bank failure.
Our paper proposes an association between loan loss reserve decisions and the risk of
bank failure based on the special circumstances that prevail during economic downturns. As a
bank’s loan quality deteriorates in the period leading up to a crisis, “building up” loan loss
reserves can generate dysfunctional incentives. Since loan loss reserves can be added back as
Tier 2 capital, they generate an illusion of non-declining capital adequacy and even of improving
capital adequacy despite deteriorating loan quality. The illusion of financial health due to higher
regulatory capital can induce complacency among banks, preventing them from responding to
underlying loan problems by restricting lending, improving collection efficiency, etc. Higher
capital adequacy also potentially allows banks to take actions that could exacerbate the
likelihood of failure, such as granting more risky loans than the given deteriorating economic
conditions justify. This possibility is derived from Shrieves and Dahl (1992), who document
evidence that banks with higher capital engage in riskier activities.
From a regulatory monitoring perspective, Buser et al. (1981) point out that regulation
generally permits banks with higher capital to make riskier investments. Further, the appearance
of higher total capital adequacy can lead to more lenient regulatory monitoring of these banks.
During an ensuing economic crisis, these complacent but nevertheless troubled banks then
experience more severe liquidity issues and asset value declines that ultimately drive their
failure.13
13 Note that as loan assets increasingly lose value during the economic downturn, loan loss provisions and hence loan loss reserves are expected to mount. At the time of failure, loan loss reserves are probably much beyond the 1.25% of risk weighted assets allowable as Tier 2 capital. Thus, the sole effect of earnings losses induced by the provisions is likely to be a reduction in total regulatory capital. Collectively, the earnings losses and declining capital adequacy could generate bank failure. See the illustration in Section 2.1.
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It is conceivable that bank managers actively use their accounting discretion to report
larger loan loss reserves, in order to avoid declines in total capital adequacy ratios.14 A number
of studies document evidence that banks use their discretion in loan loss provisions to manage
their capital ratios (Moyer 1990, Beatty et al. 1995, Ahmed et al. 1999). Generally, declining
capital ratios can be associated with increased financial costs such as higher deposit insurance
premiums, as well the political costs that arise from more severe regulatory scrutiny.15
Discretionary loan loss reserve accounting decisions potentially represent managers’ attempts to
overstate their financial health during deteriorating economic conditions in order to avoid these
costs. However, to the extent that such capital management bears deadweight costs (i.e., it
distracts managers from taking the real actions necessary to address fundamental weaknesses),
such banks are then at an increased failure risk in the event of an economic crisis.
Irrespective of whether large loan loss reserves induce complacency or are a result of
opportunistic accounting, we predict a positive association between loan loss reserve increases
and failure risk. Further, we expect this positive association to be concentrated among banks that
are less constrained in adding back loan loss reserves as Tier 2 capital.
H1: Loan loss reserve increases in 2007 are associated with a higher risk of bank failure
during 2008-2009, particularly when banks are less constrained in adding back loan loss
reserves to regulatory capital.
Finding evidence consistent with H1 would support the regulatory-capital-effects
hypothesis: loan loss reserve increases lead to increased bank failure risk because the regulatory
14 There already exists evidence that banks use their discretion in loan loss provisions to manage their capital ratios (Moyer 1990, Beatty et al. 1995, Ahmed et al. 1999). 15 As Kim and Kross (1998) report, greater regulatory scrutiny can involve more frequent audits by regulatory agencies and/or restrictions on capital distributions, on payments to bank managers and on asset growth.
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guidelines that allow loan loss reserves to be added back to regulatory capital generate
dysfunctional outcomes.
We further analyze the components of loan loss reserve increases. Ceteris paribus, loan
loss provisions increase loan loss reserves, while loan charge-offs reduce reserves. Consistent
with the regulatory-capital-effects hypothesis, we predict that bank failure risk is positively
associated with loan loss provisions and negatively associated with loan charge-offs. We also
predict that bank failure risk’s positive association with loan loss provisions and its negative one
with loan charge-offs are both more pronounced when banks are less constrained in adding back
loan loss reserves to regulatory capital.
To examine the role of bank managers’ discretion in using loan loss reserves to influence
regulatory capital, we test the association of discretionary components of loan loss provisions
and charge-offs with subsequent bank failure risk. Our predictions for these discretionary
components are similar to those for the total loan loss provisions and charge-offs.
Finally, the FDIC’s least-cost method of failure resolution, discussed in Section 2.2,
implies that the costs borne by the FDIC are likely to increase with the severity of a bank failure.
Banks whose asset values are more severely depressed relative to their liability values and those
that have more depleted franchise values would generate greater closing costs for the FDIC. To
the extent that loan loss reserve increases, by increasing regulatory capital, allow managers to
ignore underlying problems for a longer period, they are likely to exacerbate the severity of bank
failure. Thus, for the sample of failed banks, we examine whether loan loss reserve increases are
associated with the severity of bank failure.
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3. Sample construction
The timeline in our research design is shown below:
The National Bureau of Economic Research (NBER) identifies December 2007 as the
beginning of the recent recession. Our empirical analysis focuses on the association between loan
loss reserve changes and its components (loan loss provisions and net charge-offs) prior to the
economic crisis (i.e., in 2007) and the bank failures that occurred during the economic crisis (i.e.,
in 2008 and 2009).
3.1 Data on bank failures
We obtain data on bank failures from the Federal Deposit Insurance Corporation (FDIC)
website: http://www.fdic.gov. The FDIC, which is appointed as the receiver in the event of a
bank failure, makes public a press release that provides details about the bank at the time of the
failure, including actions being taken to deal with it. We collect the following information from
the press releases (available on the FDIC website): the name of the failed bank, the bank’s
estimated assets and deposits at the time of the failure, and the cost of the failure to the FDIC. As
an example, the press release for the failure of Corus Bank is provided in Appendix A. Corus
Bank’s failure date was September 11, 2009; its estimated assets and deposits at the time of
failure were both approximately $7 billion, and the cost of the failure to FDIC was assessed at
$1.7 billion.
2008 & 2009
Bank failures
(Data obtained from FDIC press releases)
2007
Measurement of loan loss reserve changes
(Data obtained from call reports)
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Table 1 provides descriptive information on the failure of commercial banks and thrifts
(which includes savings and loans associations and savings banks) from 2001 to 2009. While, for
the reasons discussed below, the focus of this paper is on the failure of commercial banks, we
also provide descriptive information on the failure of thrifts to provide a broader overview of
bank failures and to highlight the enormity of the problems facing the banking industry in
general. Failures of commercial banks and thrifts were relatively infrequent prior to the
economic recession. A total of 21 commercial banks and 4 thrifts failed between the seven years
from 2001 to 2007, compared to a total of 140 commercial banks and 24 thrifts during the
economic recession in 2008 and 2009.16 Available data indicates that the FDIC bore the large
costs that arose from bank and thrift failures in 2008 and 2009. For example, the total cost to the
FDIC insurance fund on account of failed commercial banks was $4.58 billion in 2008 and $24.1
billion in 2009. In fact, failure costs were significant enough to deplete the FDIC insurance fund
to the point of insolvency as of December 31, 2009.
In this paper, we focus on commercial banks because i) commercial banks and thrifts file
different regulatory reports, ii) detailed regulatory report data for individual commercial banks,
both private and public, are publicly available in a machine-readable form, and iii) the number
of failed commercial banks is significantly larger than the number of thrifts, facilitating wide-
sample empirical analyses.
3.2 Data from call reports
We obtain data on loan loss reserves, loan loss provisions, and net charge-offs, as well as
other accounting variables, from the call reports filed by commercial banks with the Federal
Reserve, the Federal Deposit Insurance Corporation, or the Office of the Comptroller of the
16 While not included in our analyses, we note that there were 86 more failures of commercial banks and thrifts by the end of June 2010. This suggests that many banks are still facing difficulties despite some indications of economic stabilization in 2010.
16
Currency. The data is available in machine-readable form at the Chicago Federal Reserve
website.17 We begin with the 8,098 call reports filed by banks for the fiscal year ending in
December 2007. To be included in our sample, the bank must have positive total assets and total
loans for the fiscal years ending in December 2006 and December 2007.18 To merge the bank
failure data with the call report data, we obtain the RSSD ID of the banks in the bank failure
dataset. The RSSD ID is the unique identifying number assigned by the Federal Reserve for all
financial institutions, main offices, and branches. This final sample consists of 7,463 commercial
banks, including 137 that subsequently failed during the 2008-2009 crisis.19 For brevity, we
henceforth use the term “banks” to refer to the commercial banks in our sample.
Table 2 presents the distribution of the 7,463 banks across the different states and regions
of the United States. The states with the most number of bank failures are Georgia, Illinois, and
California, with 29, 21, and 17 failures, respectively. In terms of the percentage of banks that
failed, the state with the highest percentage is Nevada, with 16.67% of its banks having failed in
2008 and 2009. From a regional perspective, while there were more bank failures in the south,
the percentage of all banks that failed is higher in the west. The uneven distribution of bank
failures across different states and regions is consistent with the fact there was significant
variation in the impact of the economic crisis across the United States.
Table 3 provides some descriptive information on the regulatory capital ratios and loan
loss reserves in 2007 for of the 7,463 banks. The mean total risk-based capital ratio and the Tier
1 risk-based capital ratio in 2007 are 17.28% and 16.18%, respectively. The inclusion of Tier 2
17 http://www.chicagofed.org/webpages/banking/financial_institution_reports/commercial_bank_data.cfm 18 We require data in 2006 and 2007 to construct our variables, in particular, those variables that measure changes from 2006 to 2007. 19 Three of the 140 failed banks were dropped from the sample because they did not file call reports in 2006. The three banks are Waterford Village Bank, Pacific National Bank, and Bank USA.
17
capital adds, on average, 1.097% of risk-weighted assets to the total risk-based capital ratio.20
The median total risk-based capital ratio and Tier 1 risk-based capital are 14.11% and 13.04%,
respectively. Given that the minimum required total risk-based capital ratio and the Tier 1 capital
ratio are, respectively, 8% and 4%, a typical bank in the sample meets the minimum
requirements, as expected. Based on the difference between the minimum requirements and the
means and medians of the actual ratios, one can also observe that the total risk-based capital ratio
requirement is more binding on average than the Tier 1 capital ratio requirement. As reported in
Table 3, Panel B, banks that failed in 2008-2009 had, in 2007, significantly lower Tier 1 but
significantly higher Tier 2 capital relative to banks that survived.
Finally, Table 3, Panel A demonstrates that in practice, Tier 2 capital is almost entirely
made up of loan loss reserves. Mean loan loss reserves scaled by risk-weighted assets used in
Tier 2 capital is 1.037%, relative to a mean Tier 2 capital ratio of 1.097%. Loan loss reserves
make up about 95% (1.037/1.097) of Tier 2 capital, about 6% (= 1.037 / 17.279) of total risk-
based capital, and 1.365% of total loans.
4 Bank failure and loan loss reserve accounting
4.1 Research design and descriptive statistics
In this paper, we rely on survival analyses that use hazard models to study the relation
between bank failure and loan loss reserve accounting. Hazard models are appropriate in our
context since they incorporate information about the time that elapses before an event (in our
case, a bank failure) occurs. Shumway (2001) demonstrates that, hazard models outperform
static models such as logit models in predicting bankruptcy.
20 Note that the allowable Tier 2 capital (i.e., the capital that can be considered as part of total capital) is the lesser of Tier 1 capital and Tier 2 capital. This constraint does not appear to matter. For example, in 2006 and 2007, the allowable Tier 2 capital is the same as the actual Tier 2 capital for all commercial banks in our sample.
18
Our tests rely on the widely used Cox proportional hazard model (Cox 1972; Cox and
Oakes 1984), which has the following form:
0 i ih( t ) h ( t )exp( X ) (2)
where Xi is a vector of explanatory variables and βi is a vector of coefficients.21 Note that the
hazard rate is the risk of failure at a certain point in time, conditional on survival until that point
in time. h0(t) represents the baseline hazard rate that is a function only of time, while exp(Xiβi)
represents the dependence of the hazard rate on a vector of explanatory variables X. In the Cox
model, the coefficient on the explanatory variable represents the proportional change in the
hazard rate for a one-unit change in the explanatory variable.
The focus of this paper is to examine how bank failure hazard is associated with changes
in loan loss reserves and its components: i) loan loss provisions and ii) net charge-offs. To
examine the above, we use the following hazard models:
h(t) = h0(t) exp (β1 CH_LLR +∑i βi CONTROLi + ε) (3)
h(t) = h0(t) exp (β1 LLP + β2 NCO +∑i βi CONTROLi + ε) (4)
The initiation date for the survival analysis is January 1, 2008, and the final date is December 31,
2009. h(t) is the hazard of bank failure at time t. t is the number of days from January 1, 2008 to
the failure date.
The independent variables of interest are CH_LLR, LLP, and NCO, which are,
respectively, the loan loss reserve changes, loan loss provisions and net charge-offs, expressed as
a percentage of the total loans. CONTROL is a set of control variables added to mitigate omitted
correlated variable bias: REAL_ESTATE_LOAN, NPL, ASSET_RISK, OVERHEAD,
INSIDER_LOAN, ROA, UNINSURED_DEPOSIT, LIQUIDITY, LEVERAGE, TOTAL_RBC,
21 Note that no explicit intercept parameter is estimated for the proportional hazards model. The log-likelihood function and survival function estimators are invariant with respect to translations of any of the independent variables, so no intercept is needed
19
SIZE, and REGION dummies. All the independent variables are measured at the end of 2007,
i.e., before the occurrence of the bank failures in 2008 and 2009.
If increases in loan loss reserves increase the risk of bank failure, we expect the
coefficients on CH_LLR in Eq. (3) to be positive and the coefficients on LLP and NCO in Eq. (4)
to be positive and negative, respectively. REAL_ESTATE_LOAN is loans and leases as a
percentage of average beginning and ending total assets. Exposure to real estate loans was a key
factor behind the financial difficulties that many banks faced during the crisis. We expect banks
with relatively more real estate loans to be at a greater risk of failure. NPL is non-performing
loans as a percentage of total loans; we expect banks with relatively greater NPL to exhibit
greater failure risk. ASSET_RISK is assets risk-weighted at 100% as a percentage of total risk-
weighted assets; we expect failure risk to be higher for banks with relatively riskier assets.
OVERHEAD is the non-interest expense as a percentage of total assets. On one hand,
banks with higher overheads are probably less efficient and therefore more likely to fail. On the
other hand, higher overheads (e.g., higher salaries and employee benefits) might be indicative
that managers are likely to prefer a quiet life and take less risk (Betrand and Mullainathan 2003).
This suggests that banks with higher overheads are less likely to fail. INSIDER_LOAN denotes
loans to executive officers, directors, principal shareholders, and their related interests as a
percentage of total assets. While more insider loans are a possible indicator of agency problems,
they can also induce in managers a greater commitment to a bank’s continued survival, and
hence a decreased preference for risk-taking. Thus, the sign of failure risk’s association with both
OVERHEAD and INSIDER_LOAN is ambiguous.
ROA is net income as a percentage of average beginning and ending total assets. We
expect more profitable banks to be less likely to fail. UNINSURED_DEPOSIT represents the
uninsured deposits as a percentage of the total deposits. The FDIC places a cap on maximum
20
insurable deposits. We expect banks with more uninsured deposits to be at greater failure risk
during times of crisis due to the greater possibility of “deposit runs”. LIQUIDITY denotes the
cash and balances due from depository institutions as a percentage of total deposits. Cash and
balances due from depository institutions provide liquidity during deposit withdrawals, which
tend to be higher during economic crises. Hence, a bank with higher LIQUIDITY is likely to face
fewer difficulties in meeting withdrawal requests, and be less likely to fail.
Higher LEVERAGE, or total liabilities as a percentage of total assets, is expected to be
associated with higher failure risk. TOTAL_RBC is the total risk-based capital ratio; it is
expected to be associated negatively with failure risk. SIZE is the natural logarithm of total assets
(with total assets in millions). From casual observation of the failed banks, it becomes apparent
that both smaller and large banks have failed. However, we control for size because it is an
important consideration when closing a bank, particularly in light of the possibility of
governmental support if the bank is “too big to fail”.
REGION2 is a dummy variable equaling one if a bank is in the Midwest region and zero
otherwise; REGION3 and REGION4 are defined analogously for the Southern and Western
regions respectively. By construction, the Northeast serves as the benchmark region. We include
region dummies to mitigate concerns that the empirical results are driven by heterogeneous
regional characteristics. Examples of such heterogeneity include differences in the expansion of
the property sector, or unemployment differences.22
Table 4, Panel A presents the summary statistics for the above variables used in the
regressions. All the continuous variables are winsorized at the 1st and 99th percentile, to mitigate
the effect of outliers. The mean value of FAIL is 0.018, or 1.8%, reflecting that 137 of the 7,463
commercial banks in our sample failed between January 2008 and December 2009. In 2007, the 22 We are not able to include state dummies because there are many states with no bank failures. Hence, it is not possible to examine how within-state variation in loan loss reserve accounting is associated with within-state variation in the risk of bank failure.
21
mean change in loan loss reserves, loss loan provisions and net charge-offs as a percentage of
total loans are 0.105%, 0.347% and 0.247%, respectively.
Table 4, Panel B presents a preliminary comparison of surviving banks (FAIL=0) and
failed banks (FAIL=1). Compared to the banks that did not fail in 2008 and 2009, the banks that
failed have greater increases in loan loss reserves, higher loan loss provisions and higher net
charge-offs in 2007. Further, 82.7% of failed banks’ total loans in 2007 were to the real estate
sector; the corresponding fraction for surviving banks was significantly lower, at 68.4%. This is
consistent with the fact that real estate exposure was a key driver of banks’ financial difficulties
during the crisis. Banks that failed also had more non-performing loans (NPL), had invested
more in relatively riskier assets (ASSET_RISK), and were less profitable (ROA). They also had
more uninsured deposits (UNINSURED_DEPOSIT), less cash to meet potential deposit
withdrawals (LIQUIDITY), higher leverage (LEVERAGE), and lower total risk-based capital
(TOTAL_RBC). Interestingly, they also tend to be larger in terms of total assets
(TOTAL_ASSETS), which is not consistent with the notion that regulators are more likely to
intervene to prevent big banks from failing.
4.2 Primary results
Table 5 presents the results of the hazard models that examine how the likelihood of bank
failures is associated with the accounting for loan loss reserves. Panel A presents the regression
coefficients along with associated standard errors. Panel B presents the hazard ratios.
In Panel A, the coefficients of interest are those on change in loan loss reserves in
Column I, and those on loan loss provisions and net charge-offs in Column II. Column I
examines the effect of increases in loan loss reserves on the likelihood of bank failure. The
coefficient on CH_LLR is positive and statistically significant at the 5% level, suggesting that
increases in loan loss reserves are associated with higher failure risk, consistent with H1.
22
In Column II, the coefficient on LLP is positive and statistically significant at the 5%
level, suggesting that higher loan loss provisions are associated with a higher likelihood of bank
failure. The coefficient on NCO is negative and statistically significant at the 10% level,
suggesting that higher net charge-offs are associated with a lower likelihood of bank failure.
Given that higher loan loss provisions and lower net charge-offs generate higher
regulatory capital, our results are consistent with the regulatory-capital-effects hypothesis. Loan
loss reserve increases, by increasing capital adequacy and creating an impression of financial
health even as loan quality deteriorates, increase bank failure risk during an economic crisis. Our
results are inconsistent with the signal-of strength hypothesis, which would predict that loan loss
reserve increases are associated with a reduced failure risk. They also cannot be explained by the
troubled-bank hypothesis, which predicts that both loan loss provisions and loan charge-offs are
associated positively with failure risk.
Panel B of Table 5 reports the hazard of bank failure in terms of the hazard ratio, which is
the effect of an explanatory variable on the hazard of bank failure. For each continuous
explanatory variable (i.e., all variables except the region dummies), we use a single-standard
deviation increase in the variable to assess its economic effect on bank failure risk.23 For each
region dummy, we compute the hazard of bank failure if a bank is in a particular region, as
opposed to the Northeast region.
The hazard ratio for CH_LLR in Column I indicates that a single-standard deviation
increase in loan loss reserves increases the risk of bank failure by 18.9% (= 118.9% - 100%). The
results in Column II indicate that a single standard deviation increase in loan loss provisions (as a
percentage of total loans) increases the risk of bank failure by 18.9%. In contrast, a single
23 To compute the hazard ratio for a one standard-deviation increase, we first multiply the coefficient on the explanatory variable from Table 4, Panel A by the standard deviation of the variable from Table 3, Panel A. We then take the exponential of this result. For example, the hazard ratio of CH_LLR is the exponential of coefficient of 0.507 on CH_LLR multiplied by its standard deviation of 0.341.
23
standard deviation increase in net charge-offs (as a percentage of total loans) decreases the risk
of bank failure by 14.6% (= 100% - 85.4%).
Among bank characteristics, the most economically significant variable is asset risk
(ASSET_RISK). A single standard-deviation increase in risk-weighted assets increases failure
risk by 85.9%. This is consistent with the notion that investment in risky assets was a significant
contributor to banking problems during the crisis. Almost as economically significant is the
exposure to real estate. A single standard-deviation increase in REAL_ESTATE_LOAN increases
the risk of bank failure by 83.6%; this result is consistent with the notion that the collapse of the
real estate market was a key driver of banks’ financial problems. Finally, we also observe
economically significant effects for liquidity and risk-based capital. A standard-deviation
increase in LIQUIDITY and TOTAL_RBC reduces the risk of bank failure by 41.7% and 58.5%,
respectively.
The first-order drivers of bank failure appear to be, as expected, the fundamental
economic characteristics of the bank. Importantly, even after controlling for these first-order
factors, loan loss reserve changes have a statistically and economically significant positive
association with bank failure risk. Further analyses of the components of loan loss reserve
changes indicate that failure risk is positively associated with loan loss provisions and negatively
associated with net charge-offs. Collectively, the results so far are more consistent with the
regulatory-capital-effects hypothesis than with the signal-of-strength or troubled-bank-
hypotheses.
Further evidence in support of the regulatory-capital-effects hypothesis is obtained when
we rely on the regulatory constraint that the maximum loan loss reserves allowable as Tier 2
capital are limited to 1.25% of gross risk-weighted assets. This implies cross-sectional variation
in the extent to which banks are constrained in reporting a given amount of loan loss reserves as
24
regulatory capital. We exploit this cross-sectional variation to test whether the positive
association between bank failure risk and loan loss reserve changes is less pronounced when
banks are more constrained in reporting loan loss reserves as regulatory capital. For this test, Eq.
(3) is adapted as follows:
H(t) = h0(t) exp (β1 CH_LLR x CONSTRAINT + β3 CH_LRR
+ β3 CONSTRAINT +∑i βi CONTROLi + ε) (5)
To examine the components of loan loss reserve changes, we adapt Equation (4) as
follows:
H(t) = h0(t) exp (β1 LLP x CONSTRAINT + β2 NCO x CONSTRAINT +
β3 LLP + β4 NCO + β5 CONSTRAINT +∑i βi CONTROLi + ε) (6)
All variables in Eq. (5) and Eq. (6), other than CONSTRAINT, are as defined in Eq. (3)
and Eq. (4). CONSTRAINT represents two proxies for the extent to which a bank is constrained
in adding back loan loss reserves to capital in 2007. Both proxies are based on the bank’s loan
loss reserve balance as a percentage of gross-risk weighted assets at the end of 2006, LLR2006.
Our first proxy, CONSTRAINT1, is a binary zero/one variable set equal to one if the
bank’s LLR2006 is above the maximum allowable limit of 1.25% of GWRA, zero otherwise.
Thus, when CONSTRAINT1 = 1, additional loan loss reserve increases in 2007 arising from
higher loan loss provisions and/or lower net charge-offs do not increase regulatory capital in
2007 (assuming no change in GWRA in 2007).
To construct our second proxy CONSTRAINT2, banks with LLR2006 below 1.25% of
GWRA are sorted into two equal-sized groups based on increasing LLR2006. Banks with
LLR2006 above 1.25% of GWRA constitute the third group. Thus, CONSTRAINT2 classifies
banks into a total of 3 groups based on increasing LLR2006.24 The tercile ranks are scaled such
24 Within our sample of 7,463 banks, 2,631 banks fall into the third group with LLR2006 above 1.25% of GWRA.
25
that the ranks range from zero to one. Thus, CONSTRAINT2 captures cross-sectional variation in
the constraint below the 1.25% cutoff. In particular, the further LLR2006 is below the maximum
limit allowable as capital, the less constrained the bank is in adding back to regulatory capital
any increase in loan loss reserves in 2007.
The coefficient on the interaction term CH_LLR x CONSTRAINT in Eq. (5) captures how
the association of bank failure hazard with CH_LLR varies with constraints on the add-back of
loan loss reserves to regulatory capital. The coefficients on LLP x CONSTRAINT and NCO x
CONSTRAINT in Eq. (6) can be interpreted analogously.
Table 6 presents the results of the cross-sectional analyses. In Columns I and II, the
cross-sectional analyses are performed with CONSTRAINT1. The results indicate that the effect
of an increase in loan loss reserves on bank failure risk is significantly lower when banks are
constrained in adding back loan loss reserves to capital in 2007. In particular, the coefficient on
CH_LLR x CONSTRAINT1 is negative and significant at a 5% level. The coefficients imply that,
among banks whose loan loss reserves are below the 1.25% limit, a single standard-deviation
increase in loan loss reserves increases is associated with an increase in failure hazard of 27.9%.
The association is statistically significant at the 1% level. In contrast, the association between
loan loss reserve increases and failure risk is statistically insignificant among banks whose loan
loss reserves exceed the maximum limit allowable as capital.
Column II reports results with the components of loan loss reserve changes. We find that
the positive association between loan loss provisions and bank failure risk observed in Table 5 is
concentrated among banks whose loan loss reserves are below the maximum limit allowable as
capital. The coefficients also suggest a stronger negative association between loan charge-offs
and the failure risk among banks with loan loss reserves below the 1.25% limit relative to those
above this limit. However, the coefficients on either NCO or NCO x CONSTRAINT1 are not
26
statistically significant at conventional levels. We next turn to the analysis with CONSTRAINT2,
which allows for more variation in the extent to which banks are constrained in reporting loan
loss reserves as capital, and hence is expected to provide more power than CONSTRAINT1.
Columns III and IV presents the cross-sectional analyses with CONSTRAINT2. The
results are largely similar to those in Columns I and II, with one important exception. Unlike
results in Column II, the coefficient on NCO in Column IV is significantly negative at the 1%
level, and that on NCO x CONSTRAINT2 is significantly positive at the 5% level. The
coefficients imply a significant negative association between loan charge-offs and the failure risk
among banks that are the least constrained in adding back loan loss reserves to regulatory capital,
and an insignificant association among banks that are the most constrained.
4.3 Abnormal loan loss provisions and abnormal net charge-offs
The results thus far (Sections 4.3 and 4.4) can be interpreted as evidence of the
potentially dysfunctional consequences of reporting large loan loss provisions without
accompanying charge-offs during times of economic crisis. The contribution of loan loss
reserves to regulatory capital can create ex post incentives for managers to delay taking actions
that would avoid future financial instability. However, it is also possible that the results are a
consequence of managers’ actively exploiting their discretion in loan loss accounting to
artificially inflate regulatory capital when banks are financially troubled. To explore this
possibility, we rely on models for normal loan loss provisions and net charge-offs to identify
their discretionary components (e.g., Wahlen 1994, Ahmed et al. 1999, Beatty et al. 2002).25
The model for “normal” loan loss provisions and net charge-offs is as follows:
25 We do not use these models in our main analyses for a number of reasons. First, there do not appear to be consistent models of loan loss provisions and net charge-offs that are used across different papers and results could be sensitive to the choice of the models. Second, it is possible that banks engage in “real activities” management (Roychowdhury 2006). For example, banks could discretionally classify more of their loans as non-performing and this will, by the construction of existing models in the literature, increase (decrease) the amounts of “normal” (“abnormal”) loan loss provisions and net charge-offs.
27
LLP = β0 + β1 CH_NPL + β2 LLR + β3 LOAN + β4 LOANR + β5 LOANOTH + εLLP (7)
NCO = β0 + β1 CH_NPL + β2 LLR + β3 LOAN + β4 LOANR + β5 LOANOTH + εNCO (8)
where CH_NPL is the change in non-performing loans as a percentage of total loans from 2006 to
2007, LLR is the loan loss reserves as a percentage of total loans at the beginning of 2007, LOAN
is the loans as a percentage of total assets in 2007, LOANR is real estate loans as a percentage of
total loans in 2007, and LOANOTH is the sum of commercial and industrial loans, loans to
depository institutions, agricultural loans, loans to individuals, and loans to foreign governments
as a percentage of total loans in 2007.26 To assess managers’ discretionary choices, we extract
the “abnormal” components of loan loss provisions (ABN_LLP) and of net charge-offs
(ABN_NCO) from the residuals ε of Eq. (7) and Eq. (8), respectively.
Table 7 presents the results of regressions (7) and (8). The results indicate that loan loss
provisions and net charge-offs are higher when there are increases in non-performing loans and
higher beginning loan loss reserves. As expected, banks with more loans as a percentage of total
assets also have higher loan loss provisions and net charge-offs. These results are consistent with
Beatty et al. (2002). Interestingly, we find that more real estate loans as a percentage of total
loans is associated with lower loan loss provisions and net charge-offs. This result appears to
reflect the notion that prevailed prior to the current economic crisis—that real estate loans are
typically “safe loans” due to the perceived low likelihood of a downturn in the real estate market.
Next, we examine the effect of abnormal levels of loan loss provisions and net charge-
offs on bank failure risk by replacing LLP and NCO in Eq. (4) and Eq. (6) with ABN_LLP and
ABN_NCO, respectively. Table 8 reports the regression results with ABN_LLP and ABN_NCO.
Similar to the results in Table 5, we find in Column I that bank failure risk increases with
26 Unlike Beatty et al. (2002), who include each loan type in LOANOTH individually in the decomposition regression, we use the sum of the loan types because there is almost no cross-sectional variation in some of the loan types. For 2007, the average LOANR and LOANOTH is 68.68% and 18.58%, respectively. This means that the loan types in LOANR and LOANOTH make up most of the loan portfolios of the banks in our sample.
28
abnormal loan loss provisions and decreases with net charge-offs. Further, in cross-sectional
analyses we find evidence similar to Table 6: failure risk’s positive association with abnormal
loan loss provisions and its negative association with abnormal net charge-offs is significantly
more pronounced among banks that are less constrained in adding back loan loss reserves to
regulatory capital.
The evidence is consistent with the possibility that bank managers use their accounting
discretion to inflate regulatory capital via loan loss reserve changes in 2007. To the extent that
capital inflation is more likely among banks that are in financial trouble, and that capital inflation
allows managers to avoid taking the actions necessary to address underlying problems, such
banks were more likely to fail during the crisis in 2008-2009.
4.4 Additional analyses
4.4.1 Regulatory capital and banks’ risky activities
In Sections 4.2 and 4.3, we observe that banks with higher loan loss reserves are more
likely to fail only when these reserves can be added back as Tier 2 capital. This is predicted by
our hypothesis that banks are less likely to take actions such as restricting risky activities when
the loan loss reserve add-back keeps their capital adequacy from declining. In this section, we
undertake some preliminary analysis of the association between regulatory capital changes and
changes in the risky activities undertaken by banks.
In the context of the 2008-2009 crisis, banks started failing in the first quarter of 2008.
Bank failures significantly intensified during the second quarter of 2008; consequently, at that
point, the banks most severely affected by the crisis begin to drop from our sample. To obtain a
sense of the influence of regulatory capital on bank actions during the crisis, we measure changes
in the banks’ risky activities in the first quarter of 2008 (from January 1, 2009 to March 31,
29
2009). We use three different measures to capture changes in the banks’ risky activities: (a)
increases in total loans, (b) increases in loans risk-weighted at 50% or above, hereafter risky
loans, and (c) increases in all assets risk-weighted at 50% or above, hereafter risky assets.
Changes in the capital ratios are measured from the fiscal period ending in December 2007 to
that ending in December 2008.
Table 9 reports the correlations between changes in capital ratios, loan loss reserve
changes scaled by risk-weighted assets, and changes in risk-taking in the first quarter of 2008.
Since loan loss reserves are the primary component of Tier 2 capital, changes in both are highly
positively correlated ( = 0.872) Changes in Tier 1 capital exhibit a negative and significant
correlation with both changes in Tier 2 capital and in loan loss reserves. This is expected; an
increase in loan loss reserves via loan loss provisions decreases Tier 1 and increases Tier 2
capital as long as the latter is below 1.25% of GRWA.
In terms of the correlations between changes in capital and changes in risk-taking, we
first observe that increases in Tier 1 capital in 2007 are negatively associated with all three
measures of increased risk-taking in 2008: increases in total loans, increases in risky loans, and
increases in risky assets. This is probably because banks with greater increases in Tier 1 capital
(e.g., through raising additional capital as a buffer) generally followed more prudent policies and
reduced their risky activities in early 2008 due to better assessments of the impending crisis.
More importantly, we find that both Tier 2 capital increases and loan loss reserve increases are
associated positively with increased risk-taking. For example, the correlation coefficient between
the change in Tier 2 capital and the change in risky loans is a statistically significant 0.029.
Overall, the correlations suggest the possibility that the ability to add back loan loss reserve
increases in 2007 as Tier 2 capital made banks less likely to reduce and possibly even increase
their risky activities in 2008, thereby increasing the risk of failure.
30
4.4.2 Severity of bank failure analyses
Finally, for the sample of banks that failed in 2008 and 2009, we examine the association
between the severity of bank failure and changes in the loan loss reserve and its components. To
do so, we make use of the following regression specifications:
FDIC_COST = β0 + β1 CH_LLR + ∑i βi CONTROLi + ε (9)
FDIC_COST = β0 + β1 LLP + β2 NCO + ∑i βi CONTROLi + ε (10)
FDIC_COST = β0 + β1 ABN_LLP + β2 ABN_NCO + ∑i βi CONTROLi + ε (11)
where FDIC_COST is the estimated cost of bank failure to the FDIC, scaled by total assets. Note
that the cost to the FDIC is higher the more severe the bank failure. The bank failure is more
severe when the value of bank’s assets relative to its liabilities is lower, and/or the value of the
bank’s deposit franchise is more depleted.
In addition to the control variables used in earlier specifications for failure hazard, we
include DEPOSITS as a control variable. DEPOSITS represent the total deposits at the time of
the failure scaled by total assets at the time of the failure (according to the FDIC press release).
The cost of bank failure can depend on compensating bank depositors for their insured deposits
(to the extent that the banks’ existing net assets are insufficient to cover these deposits). The
larger the size of the deposit base, the greater the likely compensation to depositors; hence we
control for deposits. All other variables are as defined earlier, with continuous variables
winsorized at the 1st and 99th percentiles to mitigate the effect of outliers.
Table 10 reports the results. The sample used for the analyses is constrained to only those
failed banks for which the FDIC provides the cost estimates. In particular, while there are 137
failed banks in our sample, the FDIC provides cost estimates for 136 banks. In Column I, the
coefficient on CH_LLR is positive and statistically significant, suggesting that failed banks with
31
larger loan loss reserve increases cost the FDIC insurance fund more upon failure. A single
standard-deviation increase in CH_LLR is associated with an increase in the cost of bank failure
of 2.4% (= 1.214 x 0.020 x 100%) of total assets.27 Given that the average total assets for our
sample of 136 failed banks is $887.92 million, a single standard-deviation increase in CH_LLR
costs the FDIC insurance fund an additional $21.31 million (= 0.024 x 887.92).
In Column II, the coefficient on LLP is positive and statistically significant at the 1%
level, while that on NCO is negative and statistically significant at the 10% level. Thus, the cost
of bank failures in 2008 and 2009 are associated positively with loan loss provisions and
negatively with net charge-offs. We present similar results with ABN_LLP and ABN_NCO in
Column III. Overall, the results in the section show that loan loss reserve decisions could be
associated with significant problems for the financial system during an economic crisis, not only
in terms of the incidence of bank failures, but also in their severity.
5. Conclusion
We exploit the 2008-2009 economic crisis to test the effects of adding back loan loss
reserves to regulatory capital on the future financial instability of banks. Our setting offers a
unique opportunity to observe financial instability in the form of bank failures. We document
that banks’ loan loss reserve increases in 2007 are associated positively with their failure risk in
2008-2009. More importantly, this positive association is observed only for banks that are less
constrained in adding back loan loss reserves to regulatory capital, suggesting that the treatment
of loan loss reserves as Tier 2 capital bears some potentially dysfunctional consequences that can
threaten bank survival during an economic downturn.
27 The standard deviation of CH_LLR within the sample of 136 failed banks is 1.214.
32
In examining the components of loan loss reserve changes, we find that bank failure risk
is influenced positively by loan loss provisions and negatively by loan charge-offs. The above
relations are all driven by banks that are less constrained in adding back loan loss reserves to
regulatory capital. Further, managerial discretion in loan loss accounting appears to play a
significant role in influencing the association of failure risk with both loan loss provisions and
loan charge-offs. Our findings are particularly important in the light of the recent debate on
whether to treat loan loss reserves as Tier 2 regulatory capital. They challenge the notion that the
buffer created by loan loss reserves lowers the risk of bank failure. The results also highlight that
opportunistic accounting decisions by bank managers can lead to adverse outcomes during a
crisis. Finally, our results indicate that the more specific analysis involved in reporting loan loss
charge-offs is probably more beneficial for a bank’s continued health than are loan loss
provisions.
On a broader note, our paper identifies a regulatory setting where there is a departure of
regulation from economic and accounting principles, in particular in the treatment of loan loss
reserves as Tier 2 capital. We provide evidence that such a departure could have adverse
consequences for the banking sector, in terms of the risk of bank failure. Current proposals under
consideration by regulators include calls to increase, even eliminate, the limit on the amount of
loan loss reserves that can be added back as regulatory capital. In this context, our findings are
important not just from a policy review perspective, but also from a policy-making perspective.
33
References
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35
Appendix A Example of FDIC press release on bank failure
MB Financial Bank, National Association, Chicago, Illinois, Assumes All of the Deposits of Corus Bank, National Association, Chicago, Illinois
FOR IMMEDIATE RELEASE September 11, 2009
Media Contact: LaJuan Williams-Dickerson Office (202) 898-3876 Email: [email protected]
Corus Bank, National Association, Chicago, Illinois, was closed today by the Office of the Comptroller of the Currency, which appointed the Federal Deposit Insurance Corporation (FDIC) as receiver. To protect the depositors, the FDIC entered into a purchase and assumption agreement with MB Financial Bank, National Association, Chicago, Illinois, to assume all of the deposits of Corus Bank, N.A.
The eleven branches of Corus Bank will reopen on their next normally scheduled business day as branches of MB Financial Bank. Depositors of Corus Bank will automatically become depositors of MB Financial Bank. Deposits will continue to be insured by the FDIC, so there is no need for customers to change their banking relationship to retain their deposit insurance coverage. Customers should continue to use their existing branches until MB Financial Bank can fully integrate the deposit records of Corus Bank.
This evening and over the weekend, depositors of Corus Bank can access their money by writing checks or using ATM or debit cards. Checks drawn on the bank will continue to be processed. Loan customers should continue to make their payments as usual.
As of June 30, 2009, Corus Bank had total assets of $7 billion and total deposits of approximately $7 billion. MB Financial Bank will pay the FDIC a premium of 0.2 percent to assume all of the deposits of Corus Bank. In addition to assuming all of the deposits of the failed bank, MB Financial Bank agreed to purchase approximately $3 billion of the assets, comprised mainly of cash and marketable securities. The FDIC will retain the remaining assets for later disposition. The FDIC plans to sell substantially all of the remaining assets of Corus Bank in the next 30 days in a private placement transaction.
Customers who have questions about today's transaction can call the FDIC toll-free at 1-800-823-5017. The phone number will be operational this evening until 9:00 p.m., Central Daylight Time (CDT); on Saturday from 9:00 a.m. to 6:00 p.m., CDT; on Sunday from noon to 6:00 p.m., CDT; and thereafter from 8:00 a.m. to 8:00 p.m., CDT. Interested parties can also visit the FDIC's Web site at http://www.fdic.gov/bank/individual/failed/corus.html.
The FDIC estimates that the cost to the Deposit Insurance Fund (DIF) will be $1.7 billion. MB Financial Bank's acquisition of all the deposits was the "least costly" resolution for the FDIC's DIF compared to alternatives. Corus Bank is the 90th FDIC-insured institution to fail in the
36
nation this year, and the sixteenth in Illinois. The last FDIC-insured institution closed in the state was Platinum Community Bank, Rolling Meadows, on September 4, 2009.
# # #
Congress created the Federal Deposit Insurance Corporation in 1933 to restore public confidence in the nation's banking system. The FDIC insures deposits at the nation's 8,195 banks and savings associations and it promotes the safety and soundness of these institutions by identifying, monitoring and addressing risks to which they are exposed. The FDIC receives no federal tax dollars – insured financial institutions fund its operations.
FDIC press releases and other information are available on the Internet at www.fdic.gov, by subscription electronically (go to www.fdic.gov/about/subscriptions/index.html) and may also be obtained through the FDIC's Public Information Center (877-275-3342 or 703-562-2200). PR-168-2009
37
Appendix B Call report: Reporting changes in loan loss reserves
The image part with relationship ID rId15 was not found in the file.
38
Appendix C Variable definitions
The image part with relationship ID rId16 was not found in the file.
39
The image part with relationship ID rId17 was not found in the file.
40
Table 1 Bank failures by year
This table provides information on bank and thrift failures by calendar year from 2001 to 2009. Panel A (B) shows the failure of commercial banks (thrifts) from 2001 to 2009.
Panel A: Failure of commercial banks
Year FailuresTotal
Assets ($m)Total
Deposits ($m)Bank failures
with FDIC cost infoTotal
Cost ($m)
2001 3 58.6 51.6 3 4.62002 10 2,656.4 2,291.6 4 361.92003 3 961.2 903.2 2 135.62004 3 150.8 140.1 3 14.12005 0 0.0 0.0 . .2006 0 0.0 0.0 . .2007 2 102.5 89.2 1 3.02008 20 17,963.8 14,898.6 19 4,580.52009 120 119,175.1 97,596.8 120 24,100.9
Panel B: Failure of thrifts
Year FailuresTotal
Assets ($m)Total
Deposits ($m)Bank failures
with FDIC cost infoTotal
Cost ($m)
2001 1 2,300.0 1,600.0 0 .2002 1 52.0 40.0 0 .2003 0 0.0 0.0 . .2004 1 12.3 9.8 0 .2005 0 0.0 0.0 . .2006 0 0.0 0.0 0 0.02007 1 2,500.0 2,300.0 1 110.02008 5 401,694.6 224,332.1 5 12,842.02009 19 51,709.1 39,844.6 18 12,174.8
41
Table 2 Within-sample distribution of commercial bank failures across the United States This table shows the distribution of commercial bank failures in 2008 and 2009 within our sample of 7,493 commercial banks. This sample of 7,493 banks is the number of commercial banks in existence at the end of 2007 that have the data needed to compute the variables used in our analysis of the relation between loan loss reserve accounting and bank failure (see Table 4). Within each parenthesis, the number of bank failures is indicated on the left; the total number of banks is indicated on the right.
New England Middle Atlantic
Connecticut (0 / 46) New Jersey (2 / 86)Maine (0 / 27) New York (0 / 131)Massachusetts (0 / 159) Pennsylvania (0 / 198)New Hampshire (0 / 17)Rhode Island (0 / 8)Vermont (0 / 14)
East North Central
Indiana (1 / 121) Iowa (0 / 371) Nebraska (1 / 236)Illinois (21 / 612) Kansas (4 / 338) North Dakota (0 / 93)Michigan (4 / 148) Minnesota (7 / 417) South Dakota (1 / 84)Ohio (0 / 186) Missouri (4 / 330)Wisconsin (1 / 268)
South Atlantic East South Central West South Central
Delaware (0 / 23) Alabama (2 / 143) Arkansas (1 / 142)District of Columbia (0 / 6) Kentucky (0 / 185) Louisiana (0 / 138)Florida (12 / 257) Mississippi (0 / 92) Oklahoma (1 / 252)Georgia (29 / 319) Tennessee (0 / 181) Texas (6 / 612)Maryland (0 / 55)North Carolina (2 / 92)South Carolina (0 / 68)Virginia (0 / 100)West Virginia (0 / 62)
Mountain Pacific
Arizona (4 / 45) Montana (0 / 74) Alaska (0 / 5)Colorado (3 / 140) Utah (2 / 59) California (17 / 261)Idaho (0 / 15) Nevada (5 / 30) Hawaii (0 / 7)New Mexico (0 / 46) Wyoming (1 / 40) Oregon (3 / 36)
Washington (3 / 88)
Region 1: Northeast (2 / 686 = 0.29% )
Region 2: Midwest (44 / 3204 = 1.37% )
West North Central
Region 3: South (53 / 2727 = 1.94% )
Region 4: West (38 / 846 = 4.49% )
42
Table 3 Capital ratios and loan loss reserves This table provides information on the capital ratios and loan loss reserves of banks in our sample of 7,463 banks. Panel A presents the summary statistics for all banks. In Panel B, tests of differences between non-failed and failed banks are conducted. Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Panel A: Summary statistics for all banks Variable Mean Std P25 Median P75
Total risk-based capital ratio -Total capital scaled by risk-weighted assets 17.280 25.094 11.680 14.110 18.610
Components of total risk-based capital ratio:Tier 1 capital scaled by risk-weighted assets 16.184 25.099 10.590 13.040 17.530Tier 2 capital scaled by risk-weighted assets 1.097 0.448 0.912 1.119 1.252Deductions for total risk-based capital scaled by risk-weighted assets 0.001 0.032 0.000 0.000 0.000
Loan loss reserves used in Tier 2 capital scaled by risk-weighted assets 1.037 0.247 0.897 1.103 1.251
Loan loss reserves scaled by gross risk-weighted assets 1.211 0.819 0.894 1.098 1.358
Excess loan loss reserves scaled by gross risk-weighted assets 0.183 0.733 0.000 0.000 0.122
43
Panel B: Univariate comparison of non-failed banks and failed banks
FAIL = 0 FAIL = 1
Number 7,326 137
Total risk-based capital ratio -Total capital scaled by risk-weighted assets 17.384 11.713 -5.671 ***
Components of total risk-based capital ratio:i) Tier 1 capital scaled by risk-weighted assets 16.291 10.474 -5.818 ***ii) Tier 2 capital scaled by risk-weighted assets 1.094 1.240 0.146 ***iii) Deductions for total risk-based capital scaled by risk-weighted assets 0.001 0.000 -0.001
Loan loss reserves used in Tier 2 capital scaled by risk-weighted assets 1.035 1.134 0.099 ***
Loan loss reserves scaled by gross risk-weighted assets 1.202 1.727 0.525 ***
Excess loan loss reserves scaled by gross risk-weighted assets 0.175 0.602 0.427 ***
Difference
44
Table 4 Descriptive statistics
This table provides some descriptive statistics of the variables used in the analysis of bank failure. Panel A provides the summary statistics for the banks in our sample of 7,463 banks. Panel B presents univariate comparisons between non-failed and failed banks. All the various have been winsorized at the 1st and 99th percentile within the year. The definitions of all the variables are found in Appendix C. In Panel B, tests of differences between non-failed and failed banks are conducted. Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Panel A: Summary statistics for all banks
Mean Std P25 Median P75Main variables
FAIL 0.018 0.134 0.000 0.000 0.000CH_LLR 0.105 0.341 -0.026 0.053 0.190LLP 0.347 0.570 0.043 0.173 0.397NCO 0.247 0.481 0.011 0.091 0.261
Control variables
REAL_ESTATE_LOAN 68.681 19.597 57.689 72.321 83.048NPL 2.511 2.582 0.769 1.795 3.360ASSET_RISK 57.009 17.225 45.304 58.191 69.595OVERHEAD 3.019 1.079 2.374 2.869 3.449INSIDER_LOAN 1.444 1.650 0.248 0.890 2.055ROA 0.859 0.888 0.514 0.918 1.294UNINSURED_DEPOSIT 0.405 0.156 0.295 0.382 0.490LIQUIDITY 5.317 4.814 2.812 3.935 5.895LEVERAGE 88.722 4.166 87.515 89.898 91.363TOTAL_RBC 16.661 7.656 11.680 14.110 18.610TOTAL_ASSETS ($b) 0.469 1.424 0.063 0.134 0.308
45
Panel B: Univariate comparison of non-failed banks and failed banks
FAIL = 0 FAIL = 1 FAIL = 0 FAIL = 1
Number 7,326 137 7,326 137
Main variables
CH_LLR 0.096 0.560 0.464 *** 0.051 0.437 0.386 ***LLP 0.331 1.161 0.830 *** 0.168 0.873 0.706 ***NCO 0.241 0.567 0.327 *** 0.090 0.268 0.178 ***
Control variables
REAL_ESTATE_LOAN 68.418 82.738 14.320 *** 72.088 84.297 12.208 ***NPL 2.431 6.812 4.381 *** 1.762 5.688 3.926 ***ASSET_RISK 56.731 71.875 15.145 *** 57.906 72.944 15.038 ***OVERHEAD 3.019 3.017 -0.002 2.869 2.804 -0.066INSIDER_LOAN 1.446 1.339 -0.108 0.891 0.783 -0.109ROA 0.874 0.053 -0.821 *** 0.925 0.419 -0.506 ***UNINSURED_DEPOSIT 0.404 0.460 0.056 *** 0.380 0.447 0.067 ***LIQUIDITY 5.359 3.090 -2.269 *** 3.964 2.374 -1.590 ***LEVERAGE 88.694 90.241 1.547 *** 89.875 91.195 1.320 ***TOTAL_RBC 16.749 11.916 -4.833 *** 14.180 10.820 -3.360 ***TOTAL_ASSETS ($b) 0.461 0.878 0.416 *** 0.132 0.273 0.142 ***
Difference DifferenceMeans Medians
46
Table 5 Bank failure and loan loss reserve changes
This table presents Cox proportional hazard regressions that analyze the relation between bank failure and loan loss reserve changes. The sample consists of 7,463 banks. The definitions of all the variables are found in Appendix C. The t-statistic of each coefficient is provided in brackets below the coefficient. Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Panel A: Hazard regression results
I II
CH_LLR 0.507**[2.37]
LLP 0.304**[1.98]
NCO -0.328*[-1.90]
REAL_ESTATE_LOAN 0.032*** 0.031***[4.74] [4.44]
NPL 0.209*** 0.215***[9.17] [9.32]
ASSET_RISK 0.036*** 0.036***[4.09] [4.07]
OVERHEAD -0.145 -0.146[-1.58] [-1.57]
INSIDER_LOAN -0.106* -0.117*[-1.74] [-1.89]
ROA -0.349*** -0.395***[-3.44] [-3.37]
UNINSURED_DEPOSIT 0.195 0.197[0.33] [0.33]
LIQUIDITY -0.099** -0.112**[-2.22] [-2.49]
LEVERAGE 0.107** 0.098**[2.55] [2.28]
TOTAL_RBC -0.113** -0.115**[-2.23] [-2.26]
TOTAL_ASSETS ($b) 0.101** 0.104**[2.18] [2.24]
REGION2 1.660** 1.678**[2.27] [2.28]
REGION3 1.555** 1.598**[2.13] [2.18]
REGION4 2.510*** 2.510***[3.37] [3.36]
Dependent variable is H(t), hazard of bank failure at time t
47
Panel B: Hazard ratios
CH_LLR 1.189LLP 1.189NCO 0.854REAL_ESTATE_LOAN 1.872 1.836NPL 1.716 1.742ASSET_RISK 1.859 1.859OVERHEAD 0.855 0.854INSIDER_LOAN 0.840 0.824ROA 0.733 0.704UNINSURED_DEPOSIT 1.031 1.031LIQUIDITY 0.621 0.583LEVERAGE 1.562 1.504TOTAL_RBC 0.421 0.415TOTAL_ASSETS ($b) 1.155 1.160REGION2 5.259 5.355REGION3 4.735 4.943REGION4 12.305 12.305
48
Table 6 The role of regulatory capital constraints This table presents Cox proportional hazard regressions that analyze the role of regulatory capital constraints on the relation between bank failure and loan loss reserve changes. The definitions of all the variables are found in Appendix C. The t-statistic of each coefficient is provided in brackets below the coefficient. Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
I II III IV
CH_LLR x CONSTRAINT -0.644** -0.603*[-2.05] [-1.68]
LLP x CONSTRAINT -0.601** -0.996*** [-2.13] [-3.12]NCO x CONSTRAINT 0.449 0.979**
[1.17] [2.28]CH_LLR 0.721*** 0.827***
[2.96] [2.79]LLP 0.392*** 0.797***
[2.59] [3.93]NCO -0.268 -0.758***
[-1.43] [-2.81]CONSTRAINT -0.124 -0.085 -0.101 0.033
[-0.47] [-0.30] [-0.33] [0.10]REAL_ESTATE_LOAN 0.033*** 0.033*** 0.032*** 0.032***
[4.74] [4.67] [4.72] [4.57]NPL 0.216*** 0.223*** 0.216*** 0.227***
[9.58] [9.75] [9.53] [9.93]ASSET_RISK 0.039*** 0.041*** 0.039*** 0.040***
[4.33] [4.46] [4.33] [4.42]OVERHEAD -0.137 -0.155 -0.128 -0.147
[-1.46] [-1.64] [-1.37] [-1.55]INSIDER_LOAN -0.104* -0.103* -0.102* -0.103*
[-1.71] [-1.67] [-1.66] [-1.68]ROA -0.371*** -0.423*** -0.355*** -0.407***
[-3.62] [-3.63] [-3.47] [-3.51]UNINSURED_DEPOSIT 0.333 0.377 0.304 0.371
[0.56] [0.62] [0.51] [0.61]LIQUIDITY -0.098** -0.108** -0.096** -0.103**
[-2.22] [-2.42] [-2.17] [-2.34]LEVERAGE 0.138*** 0.135*** 0.139*** 0.121***
[3.21] [3.04] [3.20] [2.65]TOTAL_RBC -0.090* -0.091* -0.093* -0.105**
[-1.79] [-1.79] [-1.83] [-2.02]TOTAL_ASSETS ($b) 0.104** 0.106** 0.101** 0.096**
[2.25] [2.29] [2.17] [2.04]
REGION2 1.749** 1.747** 1.680** 1.668**
[2.39] [2.37] [2.30] [2.27]
REGION3 1.524** 1.551** 1.494** 1.473**
[2.08] [2.11] [2.04] [2.00]
REGION4 2.513*** 2.504*** 2.496*** 2.461***
[3.36] [3.34] [3.34] [3.29]
Dependent variable is H(t), hazard of bank failure at time tCONSTRAINT1 CONSTRAINT2
49
I II III IV
CH_LLR x CONSTRAINT 0.077 0.224 + CONSTRAINT [0.28] [0.87]
LLP x CONSTRAINT -0.209 -0.199 + CONSTRAINT [-0.74] [-0.87]
NCO x CONSTRAINT 0.181 0.221 + CONSTRAINT [0.52] [0.81]
CONSTRAINT1 CONSTRAINT2
50
Table 7 Decomposition of loan loss provisions and net charge-offs
This table presents the ordinary least squares regressions that decompose loan loss provisions and net charge-offs to obtain abnormal loan loss provisions and abnormal net charge-offs, respectively. The sample of consists of 7,463 banks. The definitions of all the variables are found in Appendix C. The t-statistic of each coefficient is provided in brackets below the coefficient. Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable is LLP Dependent variable is NCO
Intercept 0.063 -0.108**[1.12] [-2.28]
CH_NPL 0.079*** 0.035***[25.03] [13.15]
LLR 0.124*** 0.203***[11.97] [23.12]
LOAN 0.006*** 0.003***[13.59] [8.71]
LOANR -0.004*** -0.002***[-8.36] [-5.49]
LOANOTH -0.001* 0.001*[-1.96] [1.80]
Observations 7463 7463Adjusted R-square (%) 12.12 11.17
51
Table 8 Bank failure, abnormal loan loss provisions, and abnormal charge-offs
This table presents Cox proportional hazard regressions that analyze the relation between bank failure, abnormal loan loss provisions, and abnormal net charge-offs. The definitions of all the variables are found in Appendix C. The t-statistic of each coefficient is provided in brackets below the coefficient. Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
CONSTRAINT1 CONSTRAINT2I II III
ABN_LLP 0.287* 0.386** 0.801***[1.79] [2.42] [3.69]
ABN_NCO -0.321* -0.273 -0.792***[-1.72] [-1.36] [-2.67]
ABN_LLP x CONSTRAINT -0.666** -1.062***[-2.06] [-2.98]
ABN_NCO x CONSTRAINT 0.478 1.046**[1.11] [2.17]
CONSTRAINT -0.281 -0.213[-1.25] [-0.82]
REAL_ESTATE_LOAN 0.031*** 0.033*** 0.031***[4.50] [4.65] [4.59]
NPL 0.223*** 0.232*** 0.235***[10.44] [10.90] [11.00]
ASSET_RISK 0.037*** 0.041*** 0.041***[4.20] [4.56] [4.51]
OVERHEAD -0.15 -0.155* -0.145[-1.62] [-1.65] [-1.55]
INSIDER_LOAN -0.115* -0.103* -0.104*[-1.85] [-1.68] [-1.69]
ROA -0.399*** -0.432*** -0.417***[-3.41] [-3.74] [-3.66]
UNINSURED_DEPOSIT 0.215 0.358 0.352[0.36] [0.60] [0.59]
LIQUIDITY -0.113** -0.109** -0.104**[-2.53] [-2.44] [-2.36]
LEVERAGE 0.101** 0.136*** 0.122***[2.36] [3.08] [2.71]
TOTAL_RBC -0.114** -0.092* -0.105**[-2.24] [-1.80] [-2.03]
TOTAL_ASSETS ($b) 0.103** 0.103** 0.093*[2.22] [2.23] [1.96]
REGION2 1.675** 1.756** 1.685**[2.28] [2.38] [2.29]
REGION3 1.598** 1.568** 1.495**[2.18] [2.13] [2.03]
REGION4 2.518*** 2.529*** 2.486***[3.37] [3.37] [3.32]
Dependent variable is H(t), hazard of bank failure at time t
52
ABN_LLP x CONSTRAINT -0.280 -0.261
+ CONSTRAINT [-0.89] [-1.05]
ABN_NCO 0.204 0.254 + CONSTRAINT [0.52] [0.83]
53
Table 9 Correlations between changes in capital ratios and changes in risk-taking This table presents the Pearson correlations between changes in capital ratio and changes in risk-taking. CH_TIER1_RW is the change in Tier 1 capital scaled by total risk-weighted assets, CH_TIER2_RW is the change in Tier 2 capital scaled by total risk-weighted assets, CH_LLR_RW is the change in loan loss reserves scaled by total risk-weighted assets, CH_LOANS is the change in loans as a percentage of total assets, CH_LOAN_RISK is the change in loans at 50% and 100% risk weights as a percentage of total loans. CH_ASSET_RISK is the change in assets at 50% and 100% risk weights as a percentage of total assets. This exploratory analysis of the association between changes in capital ratio and changes in risk-taking is limited to changes that occur in the first quarter of 2008, which is the first quarter after the recession began. Significance levels are based on two-tailed tests. The p-value for each correlation is indicated in italics.
CH_TIER2_RW CH_LLR_RW CH_LOANS CH_LOAN_RISK CH_ASSET_RISK
CH_TIER1_RW -0.208 -0.213 -0.108 -0.119 -0.079
<.0001 <.0001 <.0001 <.0001
CH_TIER2_RW 0.872 0.028 0.029 0.027
<.0001 0.015 0.012 0.021
CH_LLR_RW 0.030 0.031 0.031
0.010 0.009 0.007
CH_LOANS 0.959 0.845
<.0001 <.0001
CH_LOAN_RISK 0.854
<.0001
54
Table 10 FDIC cost and loan loss reserve changes This table presents the ordinary least squares regressions that analyze, for the banks that have failed, the relation between the FDIC cost of bank failure, loan loss provisions, and net charge-offs. The sample of consists of 136 banks that failed in 2008 and 2009. The definitions of all variables are found in Appendix C. The t-statistic of each coefficient is provided in brackets below the coefficient. Significance levels are based on two-tailed tests. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
I II III
Intercept -0.386 -0.212 -0.245[-0.74] [-0.41] [-0.48]
CH_LLR 0.020**[2.09]
LLP 0.044***[3.22]
NCO -0.031*[-1.94]
ABN_LLP 0.043***[3.01]
ABN_NCO -0.029*[-1.67]
REAL_ESTATE_LOAN 0.000 0.000 0.000[0.36] [0.22] [0.12]
NPL -0.001 0.000 0.000[-0.39] [-0.32] [0.20]
ASSET_RISK 0.001 0.001 0.001[1.22] [0.62] [0.76]
OVERHEAD -0.004 -0.001 -0.001[-0.49] [-0.13] [-0.18]
INSIDER_LOAN -0.012* -0.011* -0.011*[-1.91] [-1.75] [-1.85]
ROA 0.007 0.011 0.011[0.96] [1.46] [1.43]
UNINSURED_DEPOSIT 0.014 0.006 0.006[0.25] [0.10] [0.11]
LIQUIDITY 0.000 -0.002 -0.002[-0.13] [-0.54] [-0.64]
LEVERAGE 0.002 0.000 0.000[0.36] [-0.02] [0.08]
TOTAL_RBC 0.001 -0.002 -0.001[0.13] [-0.38] [-0.26]
TOTAL_ASSETS ($b) -0.011** -0.011** -0.011**[-2.15] [-2.14] [-2.17]
DEPOSITS 0.449*** 0.438*** 0.450***[3.69] [3.69] [3.77]
REGION2 0.034 0.076 0.061[0.44] [0.99] [0.79]
REGION3 0.053 0.094 0.083[0.69] [1.22] [1.08]
REGION4 0.032 0.072 0.060[0.40] [0.91] [0.75]
Observations 136 136 136Adjusted R-square (%) 26.34 29.92 29.11
Dependent variable is FDIC_COST