bank tail risk 11-13-2012
TRANSCRIPT
Bank Earnings Management and Tail Risk during the Financial Crisis
Lee J. Cohen Assistant Professor of Finance
Finance Department University of Georgia
Marcia Millon Cornett Professor of Finance Finance Department Bentley University
Alan J. Marcus Mario J. Gabelli Professor of Finance
Finance Department Boston College
Hassan Tehranian Griffith Family Millennium Chair in Finance
Finance Department Boston College
November 2012
JEL classification: G01, G11, G21, G28, M40 Keywords: financial institutions, earnings management, crashes, financial crisis The authors are grateful to Jim Booth, Ozgur Demirtas, Atul Gupta, Jim Musumeci, Jun Qian, Sugata Roychowdhury, Ronnie Sadka, Phil Strahan, and seminar participants at Boston College for their helpful suggestions.
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Abstract
We show that a pattern of earnings management in bank financial statements has little bearing on
downside risk during quiet periods, but seems to have a big impact during a financial crisis.
Banks demonstrating more aggressive earnings management prior to 2007 exhibit substantially
higher stock market risk once the financial crisis begins as measured by the incidence of large
weekly stock price “crashes” as well as by the pattern of full-year returns. Stock price crashes
also predict future deterioration in operating performance. Bank regulators may therefore
interpret them as an early warning signs of impending problems.
Bank Earnings Management and Tail Risk during the Financial Crisis
1. INTRODUCTION
Bank investors have long been concerned with tail risk, i.e., extreme declines in a
bank’s stock price. The financial crisis of 2007-2009 only heightened this concern. While
regulators are more concerned with operating performance than stock prices per se, they too
must be concerned with dramatic stock price declines to the extent that such declines signal
deterioration in future performance (as we show below). Moreover, contingent capital
regulation with market value triggers also can make stock prices relevant to regulators.
While tail risk is determined in large part by bank financial policies such as the
composition of on- and off-balance-sheet asset and liability portfolios, the ability to assess that
risk also depends on bank reporting and accounting policies. For example, banks have
discretion in setting the level of several key income statement accounts such as provisions for
loan losses, and they can use that discretion to modulate the transparency, or opacity, of their
financial reports. While earnings management may not directly cause tail events, it
nevertheless may affect the best estimate of tail exposure conditional on observable bank
attributes. For example, Jin and Myers (2006) and several others have shown for industrial
firms that reductions in transparency are associated with increased tail risk. This paper asks
whether the association between earnings management, which may be used to obscure true
performance, and tail risk also characterizes banks, and, in particular, whether earnings
management predicted bank performance during the financial crisis.
Earnings management can increase the risk of extreme stock market returns if it limits
the availability of information about the firm. In Jin and Myers (2006), firm managers use
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their discretion to impede the flow of public information about firm performance. Managers
normally have an incentive to postpone the release of bad news, but in some circumstances
either that incentive or the ability to hide information collapses, leading to a sudden release of
accumulated negative information and a firm-specific stock price crash. In a more general
setting, even if earnings management is not strategically exploited by managers, it still might
result in fatter-tailed return distributions if it interrupts the steady flow of information to
outside investors. Discrete information events will be reflected in substantial stock price
movements. This should be true of financial as well as industrial firms.
Much of the earnings management literature for industrial firms has focused on the
manipulation of accruals: a pattern of departures from a simple statistical model of “normal”
accruals is taken as evidence of earnings management (Healy 1985, Dechow, Sloan, and
Sweeny 1995, Cohen, Dey, and Lys 2008). Hutton, Marcus, and Tehranian (2009) propose a
measure of earnings management based on abnormal accruals and find that it is in fact
associated with tail risk, suggesting that it does cause information to reach the market in
discrete episodes rather than diffusing steadily and continuously.
In light of widespread concern over tail risk in financial institutions as well as the
emerging literature linking financial statement opacity to crash risk for industrial firms, it is
interesting to know whether a measure of earnings management, appropriately defined for
banks, would similarly predict increased probability of tail risk, and in turn whether tail events
in stock prices can provide timely warnings of risk in operating performance. Of course,
accruals for banks reflect different considerations than those that drive accruals for industrial
firms. Earnings management in banks typically is measured by the proclivity to make
discretionary loan loss provisions or by discretionary realizations of security gains or losses.
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For example, Cornett, McNutt, and Tehranian (2009) estimate a measure of bank earnings
management using these variables and find that it exhibits the reasonable properties of being
positively related to CEO pay-for-performance sensitivity and inversely related to board
independence. Adopting a similar approach, we show in this paper that, like industrial firms,
banks also display a positive relation between apparent earnings management and tail risk.
However, in contrast to industrial firms, bank tail risk typically is not evident in “normal”
periods, and therefore is hard to evaluate even from long sample periods. Nevertheless,
earnings management seems to have a substantial association with tail risk in crisis periods.
This pattern poses a difficult challenge for regulators, who are concerned most of all about
large losses. Our results suggest that earnings management might usefully be considered a
reliable proxy for exposure to large losses during periods of financial stress.
The remainder of the paper is organized as follows. In Section 2, we briefly review the
literature on tail risk and earnings management. As part of this review, we discuss how
measures of earnings management for industrial firms must be modified for banks. Section 3
discusses our sample and data sources. Section 4 presents empirical results. We begin with an
analysis and justification of our measure of bank earnings management, and proceed to
demonstrate that this measure and downside risk appear to be positively related. Finally,
Section 5 concludes the paper, where we consider the policy implications for banks and their
regulators.
2. RELATED LITERATURE
2.1 Earnings management and crash risk
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Jin and Myers (2006) present a model in which lack of full transparency concerning
firm performance enables managers to capture a portion of the firm’s cash flows. To protect
their positions, managers may manage earnings by hiding temporary losses to avoid disclosing
negative performance. However, if performance is bad enough, managers may be unwilling or
unable to conceal any more losses. At this point, all of the previously unobserved negative
performance information becomes public at once, resulting in a firm-specific stock price
crash.1 Jin and Myers measure transparency using characteristics of the broad capital market in
which the firm is situated and find that cross-sectionally (i.e., across countries), less transparent
markets exhibit more frequent crashes. Hutton, Marcus, and Tehranian (2009) further test the
Jin and Myers model by developing a firm-specific measure of earnings management and show
that it predicts higher crash risk at the firm-specific level as well. Consistent with these results,
Kothari, Shu, and Wysocki (2009) provide evidence, based on voluntary management earnings
forecasts, that managers withhold bad news when possible.
A common measure of earnings management in industrial firms is based on
discretionary accruals from the modified Jones (1991) model (Dechow, Sloan, and Sweeney,
1995). Specifically, “normal” accruals are estimated from a simple statistical model based on
firm assets, property, plant and equipment, and change in sales. “Abnormal” or discretionary
accruals are the residuals between actual accruals and the predicted accruals from the modified
Jones model. Firms with consistently large discretionary accruals are deemed more likely to be
manipulating earnings, or at the very least, have less transparent financial statements. Healy
(1985) concludes that managers use discretionary accruals to manipulate bonus income. Sloan
(1996) shows that the market seems not to fully recognize the information content of accruals
management, and Dechow, Sloan, and Sweeney (1996) argue that patterns of large
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discretionary accruals can be used to detect earnings management. Cohen, Dey, and Lys
(2008) find that abnormal accruals tend to be larger when management compensation is more
closely tied to stock value. Finally, as noted above, Hutton, Marcus, and Tehranian (2009) find
that abnormally large discretionary accruals are associated with crash risk (which they define
as 3-sigma declines in stock price).
Clearly, measures of abnormal accruals from the Jones (1991) model need to be
modified for banks or other financial institutions that are not engaged in sales-based
businesses. Instead, the focus for banks typically tends to be on loan loss provisions or the
realizations of gains or losses on securities, both of which allow considerable management
discretion. Leeway in these variables may be used to smooth earnings (Beatty, Ke, and Petroni
2002) or to shore up regulatory capital (Beaver and Engel 1996, Ahmed, Takeda, and Thomas
1999). Notice that these goals conflict with transparency by making it more difficult for
outside analysts to discern the true financial condition of the firm. Such practices presumably
impede information flow, and it is at least conceivable that they also make information more
“lumpy,” particularly as the limits of accounting discretion are reached. In the next subsection,
we consider earnings management in banks more closely.
2.2 Earnings Management in Banks
Loan loss provisions are an expense item on the income statement, reflecting
management’s current assessment of the likely level of future losses from defaults on
outstanding loans. The recording of loan loss provisions reduces net income. Commercial bank
regulators view accumulated loan loss provisions, the loan loss allowance account on the
balance sheet, as a type of capital that can be used to absorb losses. A higher loan loss
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allowance balance allows the bank to absorb greater unexpected losses without failing.
Symmetrically, if the loan loss allowance is less than expected losses, the bank’s capital ratio
will overstate its ability to sustain unexpected losses.
In addition to loan loss provisions, banks also have discretion in the realization of
security gains and losses (Beatty, Chamberlain, and Magliolo 1995, Beatty Ke, and Petroni
2002). Unlike loan loss provisions, security gains and losses are relatively unregulated and
unaudited discretionary choices. It is unlikely that auditors, regulators, or shareholders will
subsequently take issue with a manager’s decision to sell an investment security that happens
to increase or decrease earnings. Thus, realized security gains and losses represent a second
way that management has been able to smooth or otherwise manage earnings.
More recently, however, evolving accounting rules, particularly SFAS 157 (which
took effect in November 2007), have increased scrutiny of earnings management achieved
through the recording of gains or losses in the securities portfolio. Fair value accounting
requires assets and liabilities to be listed on a firm’s balance sheet at current values. Thus,
bank earnings can be affected by security sales only to the extent that values have changed
over the very short term. As discussed below, our sample period runs from 1997 through
2009. Thus, the ability to manage earnings by strategically realizing securities gains or losses
decreases during the period of our analysis.2
Consistent with these considerations, previous studies have found that banks use both
loan loss provisions and securities gains and losses to manage earnings and capital levels.
Scholes, Wilson, and Wolfson (1990) find that capital positions play a role in banks’
willingness to realize gains on municipal bonds. Collins, Shackelford, Wahlen (1995), Beaver
and Engel (1996), and Ahmed, Takeda, and Thomas (1999) find that discretionary accruals are
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negatively related to capital, although Beatty, Chamberlain, and Magliolo (1995) reach the
opposite conclusion. Wahlen (1994) shows that managers increase discretionary loan loss
provisions when they expect future cash flows to increase. Finally, Beatty, Ke, and Petroni
(2002) find that public banks are more likely than private ones to use loan loss provisions and
realized securities gains and losses to eliminate small earnings decreases. By and large, both
loan loss provisions as well as the realization of securities gains and losses appear to be
opportunistically used to manage earnings. Indeed, earnings management may be used to
discreetly smooth earnings over time or to eventually take a “big bath,” i.e., report one drastic
earnings decline after hiding a series of smaller declines in previous years (Demski 1998, Arya,
Glover, and Sunder 1998), a pattern consistent with infrequent but large stock market declines.
3. DATA
The sample examined in this study includes all publicly traded banks headquartered in
the United States and operating during the 1997 through 2009 period. We use bank
characteristics measured in the decade prior to the financial crisis to predict tail risk in both the
pre-crisis period, 1997-2006, as well as the crisis period, 2007-2009. All accounting data are
obtained from the Y-9C consolidated Bank Holding Company (BHC) database, which
aggregates bank affiliates and subsidiaries to the bank holding company level for US domestic
banks, found on the Chicago Federal Reserve’s Website, www.chicagofed.org. Bank stock
return data are collected from the Center for Research in Security Prices (CRSP) data base.
Table 1 lists the number of publicly traded banks with available consolidated BHC data by year
in our sample. Our analysis includes a total of 4,112 bank-years.
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3.1. Discretionary loan loss provisions and security sales
Variation in bank earnings is driven predominately by the performance of the loan
portfolio. Loans over 90 days past due and still accruing interest as well as loans no longer
accruing interest are observable measures of the current loans at risk of default. While a
portion of the loan loss provisions set aside for these obviously “bad” loans will be standard
and non-discretionary, there is considerable room for judgment in the eventual losses that will
be realized on these as well as healthier loans. Banks therefore may manage earnings through
allowable discretion in the recording of loan loss provisions. In principle, each bank manager’s
basis for judgment with respect to these provisions is subject to periodic review by regulators.3
However, in practice, large banks in particular appear to have considerable discretion: Gunther
and Moore (2003) find that while there are many instances of regulator mandated revisions in
loan loss provisions, only six in their study involve banks with over $500 million in total assets
and only four involve banks that are publicly traded. In addition, as noted above, banks also
have had leeway to manage earnings through the discretionary realization of security gains and
losses, particularly prior to 2007 and the enactment of SFAS 157.
The challenge is to devise a measure of discretionary loan loss provisions and
discretionary realization of securities gains and losses and combine them into a measure of
earnings management. We employ the Beatty, Ke, and Petroni (2002) model of “normal” loan
loss provisions using OLS regressions that allow for both year and regional (specifically, eight
regional districts defined by the Comptroller of the Currency) fixed effects. We estimate the
model in the period ending in 2006, the last full year before the onset of the financial crisis.
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This ending date ensures that disruptions to normal bank behavior patterns elicited by the crisis
will not affect our estimates of normal reserving behavior. Their regression model is:4
LOSSit = αtr + β1LNASSETit + β2NPLit + β3LLRit + β4LOANRit + β5LOANCit + (1)
β6LOANDit + β7LOANAit + β8LOANIit + β9LOANFit + εit,
where:
i = bank holding company identifier;
t = year (1994 to 2006);
r = U.S. Office of the Comptroller of the Currency defined district number
αtr = Fixed effect for region and year
LOSS = loan loss provisions as a fraction of total loans;
LNASSET = the natural log of total assets;
NPL = nonperforming loans (includes loans past due 90 days or more and still accruing interest and loans in nonaccrual status) as a percentage of total loans;
LLR = loan loss allowance as a fraction of total loans;
LOANR = real estate loans as a fraction of total loans;
LOANC = commercial and industrial loans as a fraction of total loans;
LOAND = loans to depository institutions as a fraction of total loans;
LOANA = agriculture loans as a fraction of total loans;
LOANI = consumer loans as a fraction of total loans;
LOANF = loans to foreign governments as a fraction of total loans;
ε = error term.
The fitted value in equation (1) represents normal loan losses based on the
composition of the loan portfolio, and therefore, the residual of the regression is taken as the
“abnormal” or discretionary component of loan loss provisions.5 However, because equation
(1) models loan loss provisions as a fraction of total loans, while our measure of earnings
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management (defined below) is standardized by total assets, we transform the residual from
equation (1) and define our measure of discretionary loan loss provisions (DISC_LLPit) as:
DISC_LLPit = εit × LOANSit
ASSETSit (2)
where LOANSit = total loans and ASSETSit = total assets of bank i in year t.
To find discretionary realizations of gains and losses on securities, we again follow
Beatty, Ke, and Petroni (2002). We estimate the following OLS regression over the pre-crisis
period with time fixed effects. Their model of “normal” realized security gains and losses
(GAINSit) is:
GAINSit = αt + β1LNASSETit + β2UGAINSit + εit, (3)
where:
i = bank holding company identifier;
t = year (1994 to 2006);
GAINS = realized gains and losses on securities as a fraction of beginning-of-year
total assets (includes realized gains and losses from available-for-sale
securities and held-to-maturity securities);
LNASSET = the natural log of beginning-of-year total assets;
UGAINS = unrealized security gains and losses (includes only unrealized gains and
losses from available-for-sale securities) as a fraction of total assets at the
beginning of the year;
ε = error term.
The residual from equation (3) is taken as the discretionary component of realized
security gains and losses (DISC_GAINSit). Panel A of Appendix A summarizes the variables
used to find discretionary and nondiscretionary loan loss provisions and realized securities
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gains, Panel B reports descriptive statistics for all variables in equations (1) through (3), and
Panel C presents the results of the regressions in equations (1) and (3).
Note that higher levels of loan loss provisions decrease earnings, while higher levels of
realized securities gains and losses increase earnings. Accordingly, we define bank i’s
“discretionary earnings” in year t, DISC_EARNit, as the combined impact of discretionary loan
loss provisions and discretionary realization of securities gains or losses:
DISC_EARNit = DISC_GAINSit – DISC_LLPit (4)
High levels of DISC_EARN amount to under-reporting of loan loss provisions and/or higher
realizations of securities gains, which, ceteris paribus, increase income. Negative values for
DISC_EARN would indicate that loan loss provisions are over-reported and/or fewer security
gains are realized, both of which decrease reported operating income.
Panel A of Table 2 reports descriptive statistics for each variable in equation (4),
estimated over the pre-crisis period, 1997-2006.6 The average level of both discretionary loan
loss provisions and realized securities gains (both as a percent of assets) are measured as
departures from normal behavior (i.e., as regression residuals), and therefore by construction,
are virtually zero.7 However, there is meaningful variation in these numbers. Discretionary
loan loss provisions, DISC_LLP, in the pre-crisis period range from a first percentile value of
0.481 % to a 99th percentile value of 0.867% of assets, with a standard deviation (across
banks and time) of 0.257% of assets. The corresponding range for realized securities gains is
from 0.656% to 0.764% of assets, with a standard deviation of 0.281% of assets. The
standard deviation of discretionary earnings, DISC_EARN, is 0.370% of assets, indicating that
a non-trivial portion of the variation in reported bank performance (the standard deviation of
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bank ROA is 0.614%) is due to management’s discretionary accounting and security sales
choices.
Figure 1 plots the standard deviation across banks of DISC_EARN in each year.
Notice the dramatic increase in the cross-sectional standard deviation of discretionary earnings
in the 2007-2009 period. This may indicate that normal bank behavior as expressed in
equation (4) significantly changes during the crisis. We therefore will focus primarily on
patterns computed prior to 2007. The next section offers further evidence on accounting
discretion.
3.2 Earnings Management
Table 3 examines the time series properties of discretionary earnings, DISC_EARN, as
well as its two components, discretionary loan loss provisions, DISC_LLP, and discretionary
realizations of gains or losses on securities, DISC_GAINS. We regress each of these variables
on their own past values in the previous three years. We estimate the relation over the pre-
crisis period, 1997-2006, because Figure 1 suggests that the extreme events of the crisis years
might disrupt the patterns that characterized each bank in the previous decade.
Panel A of Table 3 shows that in the short-term (i.e., at a one-year lag), discretionary
earnings exhibit positive serial correlation, with a positive and statistically significant
coefficient (0.105) on the one-year lagged value. However, at longer lags of 2 or 3 years, this
relation reverses. The coefficients at these lags (0.054 and 0.150, respectively) are negative,
highly significant, and of considerably greater combined magnitude than the coefficient on the
one-year lag. When we decompose discretionary earnings into its two components, we find
precisely the same patterns (Panels B and C). Both discretionary loan loss provisions as well as
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discretionary realizations of securities gains or losses show the same positive serial correlation
at 1-year horizons, but negative and larger combined serial correlations at the 2 and 3-year
horizons. This pattern suggests that discretionary contributions to earnings due either to
“abnormal” loan loss provisions or to security sales show a reliable tendency to reverse in later
years.
If managers consistently employ unbiased estimates of future loan losses to determine
the proper level of current reserves, we would find no time-series dependence in the
discretionary loan loss series. The significant time series patterns that actually characterize the
data suggest that loan loss provisions are subject to strategic considerations. Managers may
use their discretion in choosing loan losses to paint some desired picture of the firm. But over
time, as accumulated loan loss provisions must be reconciled to actual loss experience, those
discretionary choices must be reversed. Similarly, the reversal patterns in realized gains or
losses on security sales suggest that managers selectively choose securities to sell based in part
on the contribution to current earnings, leaving them with a preponderance of offsetting gains
or losses on future sales.
The pattern revealed in Table 3 is highly reminiscent of the literature on discretionary
accruals that has been used to examine earnings management in industrial firms. There too we
observe some short-term momentum in discretionary accruals followed by reversals. For
example, Dechow, Sloan, and Sweeney (1996) examine the pattern of discretionary accruals
for known earnings manipulators, specifically, firms subject to enforcement actions by the
SEC. Discretionary accruals gradually increase as the alleged year of earnings manipulation
approaches and then exhibit a sharp decline. The initial increase in discretionary accruals is
consistent with manipulation to increase reported earnings; the decline, with the reversal of
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prior accrual overstatements. Our results on discretionary choices for banks similarly
demonstrate a pattern of reversals that undoes prior distortion of reported earnings.
Therefore, we define earnings management, EARN_MGT, as the three-year moving
sum of the absolute value of DISC_EARN. Although managers may prefer accounting choices
that increase earnings, following Hutton, Marcus, and Tehranian (2009), who look at earnings
management and crash risk in industrial firms, we use absolute values of discretionary earnings
rather than signed values. Both positive and negative abnormal earnings may indicate a
tendency to manage earnings: discretionary accounting choices that artificially enhance
reported earnings in one period eventually must be reversed. Like them as well, we use the
three-year moving sum (instead of a one-year value) to capture the multi-year effects of
discretionary choices because the moving sum is more likely to reflect sustained, underlying
bank policy.
EARN_MGT = |DISC_EARNt-1| + |DISC_EARNt-2| + |DISC_EARNt-3| (5)
We also break earnings management into its components, loan loss provisions and
realized securities gains and losses, to see whether one or the other of these sources of
discretionary behavior has greater association with tail risk. Therefore, we also evaluate the
following 3-year moving sums:
Loan loss management: LLP_MGT = |DISC_LLPt-1| + |DISC_LLPt-2| + |DISC_LLPt-3| (6)
Securities gains/losses management:
GAINS_MGT = |DISC_GAINSt-1| + |DISC_GAINSt-2| + |DISC_GAINSt-3| (7)
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Panel B of Table 2 presents descriptive statistics for these variables in the pre-crisis
years. The mean value of EARN_MGT (computed over the preceding three years, t3 to t1) is
0.601% of assets. The mean value of LLP_MGT is 0.430% of assets, and the mean of
GAINS_MGT is 0.380%.8 During the period, mean return on assets is 1.090%. Therefore,
these values are appreciable fractions of typical ROA.
3.3 Tail risk
We are ultimately concerned with tail risk, specifically, the impact of cross-sectional
variation in earnings management on the incidence of extreme negative returns. Therefore,
we need to net out that portion of returns attributable to common market factors and industry
effects. Bank-specific returns are defined as the residuals from an expanded index model
with both market and bank-industry factors. We estimate equation (8) each bank-year using
weekly data, and allow for nonsynchronous trading by including two lead and lag terms for
the market and industry indexes (Dimson 1979).9
rj,t = j + 1,jrm,t-2 + 2,jri,t-2+ ,jrm,t-1 + 4,jri,t-1 + 5,jrm,t + 6,jri,t + 7,jrm,t+1 +
8,jri,t+1+ 9,jrm,t+2 + 10,jri,t+2 + j,t (8)
where rj,t is the stock market return of bank j in week t, rm,t is the CRSP value-weighted
market index, and ri,t is the Fama-French value-weighted bank industry index. The residual
of equation (8), j,t, is the bank-specific return in each week. Our bank-specific crashes
therefore represent extreme price movements over and above those due to market-wide and
industry-wide events.
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Summary statistics for worst-week bank-specific returns and residual risk in the pre-
crisis years appear in Panel C of Table 2. The average residual standard deviation of bank-
specific stock returns in this period is 3.098%. Figure 2 shows that this value is fairly
consistent over the pre-crisis period. Under the assumption that bank-specific returns are
normally distributed, the expected value of the worst-week bank-specific return in a sample of
52 weekly observations would be 2.26 standard deviations below the mean; with a mean of
zero and standard deviation of 3.098% for the pre-crisis period, this would imply an expected
worst-week return of 2.26 × 3.098% = 7.00%. In fact, the sample-average worst-week
return in the pre-crisis period is 7.138%, suggesting that, at least prior to the crisis, fat-tailed
distributions are not an issue.
However, Figure 2 demonstrates that residual standard deviations rise sharply with the
onset of the crisis. As bank-specific returns already control for market and industry
performance, this pattern indicates that banks are differentially affected by the crisis, leading to
greater within-industry dispersion of returns.
Part of the increase in cross-sectional dispersion, of course, is due to the sharp increase
in the incidence of banks that suffer a crash during the financial crisis. Table 4 presents annual
measures of crash propensity. We measure residual standard deviation for each bank in each
year.10 If returns in the coming year are normally distributed, only 0.1% of banks in any week
would be expected to exhibit bank-specific returns less than 3.09 standard deviations below
their mean value, and in any year, only 1 (1 0.001)52 = 0.0507 or 5.07% of banks would
experience a week with returns below this level. In fact, crash incidence exceeds this value.
Table 4 shows for each year the actual percentage of banks with firm-specific returns in at least
one week falling below this cutoff. The percentage in the pre-crisis years is generally between
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5% and 10%, but it balloons to 74.2% in 2008. The negative returns corresponding to these
crashes are quite large; in the pre-crisis period, the median bank-specific loss in a crash week is
roughly 3.39 times the weekly residual standard error from equation (8), or about 10.5%, while
in the 2007-2009 period the median loss in a crash week is 4.49 times the standard error.
4. EMPIRICAL RESULTS
4.1 Crash risk
Table 5 presents an analysis of the association between crash risk and earnings
management. The table reports probit (panel) regressions for the likelihood of a bank-specific
crash in any year. The dependent variable (indicating a crash) is assigned a value of 1 if in any
week in that year the bank-specific return is less than 3.09 times the bank-specific standard
deviation. The right-hand side variables of interest are EARN_MGT or, in alternative
specifications, its two components, LLP_MGT or GAINS_MGT. These are winsorized at their
1st and 99th percentile values. We also interact these explanatory variables with a financial
crisis dummy to allow them to have different effects during the crisis period. The additional
controls are total bank assets, bank capital ratio,11 and the Amihud (2002) measure of stock
illiquidity. Amihud’s measure equals the ratio of the absolute value of daily stock returns
divided by daily dollar trading volume, averaged over the year. Less liquid stocks may be
more prone to tail events, as they are less able to absorb sudden shifts in demand. The
regressions are estimated with year fixed effects.12
Column (1) of Table 5 employs EARN_MGT as the right-hand side variable, while
column (2) breaks out earnings management into its two component terms. In columns (1) and
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(2), the coefficients on these terms are fixed over the entire sample period. In these columns,
earnings management as a whole and more particularly discretionary loan loss provisions are
marginally significant. However, in columns (3) and (4), we introduce crisis-period interaction
terms that allow earnings management to have different effects in the pre- and post-crisis
periods. In this specification, there is no apparent relationship between earnings management
and crash likelihood in the pre-crisis years (the coefficients on EARN_MGT or its components
are all statistically insignificant at the 5% level in columns (3) and (4)), but the interaction
terms between the crisis dummy and both earnings management and loan-loss provisions are
statistically significant. For example, the EARN_MGT*CRISIS interaction term in column (3)
has a t-statistic of 2.219 and a coefficient of 22.699. Most of the power of total EARN_MGT
clearly comes from management of loan loss provisions rather than from securities gains or
loss management. The LLP_MGT*CRISIS interaction term in column (4) has a t statistic of
3.627 with a positive coefficient, 62.140. In contrast, securities gains or losses management
apparently has little relation to crash propensity. Even in the crisis, it is statistically
insignificant, with the GAINS_MGT*CRISIS term receiving a t-statistic of only 0.234. This
may be due to the fact that (as discussed above), during the latter part of our sample period,
SFAS 157 significantly limited the ability of banks to use security portfolio gains and losses as
a tool to manage earnings.
The economic impacts in Table 5 equal the increase in crash probability during the year
corresponding to an increase in each variable from the tenth percentile of the sample
distribution to the 90th percentile. This is analogous to a shift of the right-hand side variable
from the middle of the first quintile of its distribution to the middle of the fifth quintile, and
thus is comparable to a common “(5) (1) difference.” The impact of EARN_MGT during the
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crisis is economically large, 8.88%, and the impact of LLP_MGT is even higher, 16.43%. The
latter value is between one-fifth and one-quarter of the unconditional probability of a crash in
the crisis years (see Table 4). By way of comparison, Hutton, Marcus, and Tehranian (2009)
find that a comparable increase in earnings management in their sample of industrial firms
increases crash likelihood by around one sixth of the unconditional probability of a crash.
Crash sensitivities for this sample of banks are thus a bit stronger than the corresponding
values for industrial firms.
The control variables, total assets, capital, and liquidity, all are statistically insignificant
in explaining crash likelihood. In sum, it appears from Table 5 that banks engaging in greater
earnings management are more likely to experience crashes during the crisis, even though such
crash risk does not make itself evident in the pre-crisis years.
Table 6 presents similar regressions, but instead of a 0-1 crash indicator on the left-
hand side, we use a 0-1 jump indicator, where a jump is defined as an increase in stock price of
a least 3.09 standard deviations. This allows us to test whether bank earnings management is
related to skewness (specifically, negative crashes) or kurtosis (fat tails on both sides of the
return distribution). Table 6 is notable for what it does not show. With only one exception,
neither earnings management nor either of its components is significant in any of the
specifications. We conclude from these results that while crash risk is reliably higher for banks
that manage earnings more aggressively, jump potential is not.
To test this more formally, we compute chi-square tests for the equality of the
regression coefficients on EARN_MGT in predicting crash versus jump probabilities.13 The
chi-square statistic for such equality is 25.3, which allows us to reject the hypothesis of
equality at a 0.00% level. A similar test for the (joint) equality of the coefficients on the
20
components of earnings management, LLP_MGT and GAINS_MGT, yields a chi-square of
14.7 and a p-value of 0.07%.
4.2 Stock price crashes versus operational risk
As we acknowledge above, regulators may be less concerned with stock price risk per
se than with the underlying operating performance of a bank. Moreover, stock price
movements may reflect variables such as short-term fluctuations in risk premia that do not
reflect on operational performance. On the other hand, large stock price drops may in fact
signal a market expectation of deteriorating performance, and this would concern regulators.
We address these issues by examining full-year stock price performance, implied volatilities,
and changes in bank return on assets.
First, we consider the relation between earnings management and annual returns (rather
than weekly crashes) during the crisis. Stock returns over a full year may be more reflective of
persistent underlying conditions than weekly returns, even if they are extreme. We calculate
each bank’s annual firm-specific return by compounding its weekly idiosyncratic returns. We
then rank banks by earnings management and assign each bank to an earnings management
quintile. Finally, we compute the difference in the average annual firm-specific return between
the banks in the upper and lower earnings management quintiles. As shown in Figure 3, annual
returns also support the hypothesis that earnings management is associated with greater
downside risk during the crisis years, and that this risk differential is substantial. That is, until
the crisis, the difference in the average annual firm-specific returns between banks in the upper
and lower earnings management quintiles is generally small, and statistically insignificant. But
in 2008 and 2009, the difference spikes. The most aggressive earnings managers underperform
21
the least aggressive ones by highly substantial margins, 7.0% and 13.0% in those two years.
These large economic differences are also highly statistically significant, with p-values of 0.6%
and 0.2% in the two years. Interestingly, the other years in which bank earnings management is
correlated with underperformance are the years of financial turbulence corresponding to the
final run-up and then collapse of the dot-com sector in the 1999-2001 period.
These results reinforce the conclusion of the probit regressions that earnings
management is related to substantial downside exposure, not to fat tails more generally. They
also imply that crash weeks are not as a rule followed by a stock price recovery. High earnings
management banks show greater crash risk during the crisis as well as considerable sustained
underperformance throughout the crisis. The consistency of the crash risk probit regressions
and these annual return differentials suggest that the stock price declines reflect a reassessment
of underlying bank prospects rather than short-lived financial market fluctuations due, for
example, to high-frequency variation in risk premia.
Further corroboration for the view that the higher rate of stock price crashes for more
aggressive earnings managers is due to a reassessment of their prospects is found in the positive
correlation between earnings management and loan loss provisions during the crisis years. That
correlation is 0.324, indicating that loan loss provisions during the crisis increased with the
aggressiveness of earnings management as measured before the crisis. In other words,
compared to less aggressive earnings managers, aggressive banks revealed during the crisis
greater negative information about the quality of their loan portfolios. In contrast, earnings
management and loan loss provisions in the pre-crisis years are nearly unrelated, with a
correlation coefficient of only 0.035.14
22
To examine whether the greater stock price decline of high-earnings management banks
documented in Figure 3 might be related to increases in risk and therefore risk premia, Table 7
examines the increase in implied volatility from the pre-crisis period to the crisis period as a
function of bank earnings management.15 If the implied volatilities of aggressive earnings
managers increase by more than those of less aggressive ones, it is possible that their
differential stock price declines might be related to differential changes in risk premium. For
example, perhaps aggressive earnings managers pre-crisis were not as good at managing risk (or
took on more risk), which was revealed during the crisis. Or perhaps the market feared that
more opaque banks were hiding more bad news during the crisis. If implied volatility is not
associated with earnings management, however, it becomes more plausible that the greater
stock price declines of high earnings management banks during the crisis reflect expected
deterioration of operating performance.
Because most banks do not have traded options, the sample size in Table 7 is small,
and therefore we group banks into earnings management terciles rather than quintiles. The
mean implied volatility of at-the-money call options for each group is computed pre-crisis
(January 31, 2007) and mid-crisis (January 30, 2009). Table 7 shows that all implied
volatilities increased during the crisis. For the bottom tercile, implied volatility increased
from 0.199 pre-crisis to 0.815 mid-crisis, while for the top tercile, implied volatility
increased from 0.178 to 0.929. However, there is no evidence that more aggressive earnings
managers demonstrate greater increases in implied volatility. The difference-in-difference
statistic in Panel A (for total earnings management) indicates that more aggressive banks
have slightly higher increases in implied volatility over the period, but by a statistically
insignificant amount (t-statistic = 1.597). Panel B focuses on the loan loss provision
23
component of earnings management, which is most predictive of both crash risk and annual
underperformance during the crisis. By this measure, more aggressive earnings managers
have a lower increase in implied volatility. For the bottom tercile, implied volatility
increases from 0.180 pre-crisis to 0.929 mid-crisis, while for the top tercile, implied volatility
increases from 0.195 to 0.730. The difference-in-difference t-statistic in Panel B is 3.546,
significant at better than 1%. This result is at odds with a risk premium-based explanation of
the comparatively poor stock price performance of the more aggressive earnings managers.
Gains management (Panel C) shows the opposite pattern, but again at a statistically
insignificant level. In sum, changes in implied volatility are not related to stock price
performance during the crisis, which strengthens the case that the stock price performance is
more related to operating performance than to variation in risk premium.
Finally, to focus on a measure of more direct interest to bank regulators, we examine
whether banks that experience a crash are more likely to suffer a deterioration in future
operating performance. Specifically, for each year, we first classify banks as having
experienced a crashed or not. Then we compute the average change in return on assets (net
income/assets) for crashers versus non-crashers. The change in ROA is measured as ROA in
the year following the crash minus ROA in the year preceding the crash. By skipping the
year of the crash itself, we avoid any confounding effects of stock price crashes that might
result from announcements concerning operating performance during the crash year.
Comparing average changes in ROA for crashers versus non-crashers provides a difference
in differences across that particular year. Repeating this procedure for each year of the
sample, we can perform a Fama-MacBeth estimate of the average differential change in
performance across crashing and non-crashing banks.
24
The results, reported in Table 8, show that banks that experience a crash are more
likely to suffer a deterioration in future operating performance. The first line of Table 8
reports the difference-in-differences results. Crashers in any year demonstrate an average
incremental deterioration in ROA of 11 basis points relative to non-crashers across the year.
The t-statistic is 1.95, but with only 12 degrees of freedom, the p-value is 7.1%. When we
stratify crashers further, however, the results are more substantial. For example, in the
second line of the table we order crashing banks in each year by the size of their crash and
focus on the half of the banks with the more severe crashes. These banks demonstrate a
much higher difference-in-differences deterioration of ROA compared to non-crashers: 22
basis points compared to only 11. Here, the t-statistic is 1.90, with a p-value of 9.1%.
Finally, we cut the crashing sub-sample in half again and look at only the most severe quarter
of the crashers. The difference-in-differences deterioration in ROA again rises substantially,
to 44 basis points, with a t-statistic of 2.29 and a p-value of 2.7%. While the small number of
observations in this exercise generally impedes statistical significance, these results
nevertheless indicate that bank-specific crashes seem to predict a deterioration of bank
operating performance in the year following a crash; tellingly, more extreme crashes predict
greater deterioration. Moreover, the differences in differences are economically large. A
typical bank ROA even in good times is less than 1.5%. So, a swing of between 11 to 44
basis points is substantial. Thus, stock market crashes seem to provide useful early warning
signs of coming deterioration in operating performance, and, in turn, capital adequacy.
While regulators would have to wait a full year to observe ROA directly, it appears that
future changes in ROA can be inferred from stock price crashes today.
25
5. CONCLUSION
While earnings management has little apparent predictive significance for downside
risk during the pre-crisis period, it is highly predictive of such risk during the crisis. Downside
risk for banks exhibiting greater earnings management in the pre-crisis period is substantially
greater during the crisis years. The association of earnings management with crash risk is
economically large, with a magnitude that approaches one quarter of the unconditional
probability of a crash. Management of loan loss provisions appears to be far more important
than discretionary choices in the realization of gains or losses on security holdings. This
downside risk is evident from full-year returns as well as extreme weekly returns, implying that
these results do not reflect merely short-term stock price fluctuations. Neither do variations in
risk and risk premia as reflected in implied volatility seem to be the cause of this phenomenon.
Moreover, crashes in stock prices predict deterioration in operational performance.
The challenging policy implication of these results for regulators, investors, and risk
managers is that tail risk does not seem to be evident in pre-crisis data—we do not observe
the downside risk associated with earnings management until a crisis. This, of course, limits
the use of past rate of return data to assess risk of extreme returns. Nevertheless, these
results indicate that a history of earnings management, whatever its motivation, helps to
predict bank performance during a crisis. Therefore, even if that tail risk has not been
manifest during “normal” periods, one might reasonably look at earnings management as
predictive of exposure to tail events.
While we do not have any direct evidence on the mechanism underlying the association
between earnings management and downside risk, it seems reasonable to speculate that banks
26
that massage earnings have more to hide. They may easily deny the market relevant
information during quiet periods. However, when a crisis strikes and stresses become more
evident, the negative information revealed about them may lead to a more substantial revision
of market perception about their operating performance with consequent impact on their stock
prices.
27
APPENDIX A: MODELS OF LOAN LOSS PROVISIONS AND REALIZED SECURITY GAINS AND LOSSES
The Beatty, Ke, and Petroni (2002) model presented in equation (1) applies to
incremental loan loss provisions in any year. As a model of “normal” loss provisions, it
gives us a basis for detecting abnormal provisions, and that is the major requirement for our
empirical model. Nevertheless, taking a broader view, it is worth pointing out that their
equation is broadly consistent with a model in which firms target the level of loan loss
reserves. Suppose that total loan loss reserve targeted by each firm, T(LLR), can be
described by the following equation (with variable definitions on the following page):
T(LLRit) = tr + β1LNASSETit + β2NPLit + β4LOANRit + β5LOANCit + β6LOANDit +
β7LOANAit + β8LOANIit + β9LOANFit + it (A1)
We cannot observe T(LLRit) directly, but we do observe actual loss provisions in
each year, and these provisions presumably will fluctuate in the same direction as changes in
T(LLRit) as firms make at least partial adjustments to theoretically desirable levels of loan
loss reserves. Therefore, one may interpret equation (1) as a transformed version of (9)
together with a partial adjustment model.
Under this interpretation, the negative serial correlation estimated for in Table 3 has
an interesting implication. It suggests that a negative residual is likely to be followed by a
positive one. Therefore, a currently under-provisioned firm is likely to be overprovisioned
within a short period of time. One reading of this result is that if a bank does not have
enough accounting flexibility to use further under-provisioning to reach its target net income,
28
it may take an entirely different approach and drastically overprovision (i.e., “take a bath” in
current earnings) to regain its ability to under-reserve in future periods.16
Table A1 provides variable definitions, summary statistics, and regression results for
the Beatty, Ke, and Petroni (2002) OLS regression models explaining loan loss provisions
and realized security gains and losses for a sample of large bank holding companies over the
period 1994-2006. Data are obtained from Bank Holding Company Performance Reports
(FRY-9), the Chicago Fed’s merger databases and the Center for Research in Securities
Prices (CRSP). The following regressions are estimated:
LOSSit = αtr + β1LNASSETit + β2NPLit + β3LLRit + β4LOANRit + β5LOANCit + β6LOANDit
+ β7LOANAit + β8LOANIit + β9LOANFit +εit and
GAINSit = αt + β1LNASSETit + β2UGAINSit + εit.
29
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33
FOOTNOTES
1. In the Jin and Myers model, insiders can actually divert cash flow to themselves. This
would be difficult in the banking context, but even here, managers can increase their
compensation by artificially meeting earnings targets. Of special interest is the
possibility that a history of non-decreasing earnings that induces investors to view the
bank as low risk may increase the stock price and the value of equity-based
compensation. This risks a sudden dramatic change (and a stock price crash) if the bank
is later forced to report a decrease in earnings, as in Jin and Myers.
2. In addition, in March 2009, ASC320 required financial institutions to recognize other
than temporary impairment (OTTI) on their available for sale (AFS) and held to maturity
(HTM) portfolios. If the loss is considered temporary, the adjustment is reported in other
comprehensive income and may be subsequently recovered if the value of the investment
returns. However, if management considers the loss other-than-temporary, the loss is
charged to operations and subsequent recoveries of fair value are not recorded in earnings
until the investment is sold. Banks’ treatment of OTTI could be viewed as a way of
obscuring their performance.
3. Managerial judgment must be based on a “reviewable record” as noted in the Chicago
Federal Reserve’s Micro Data Reference Manual’s data dictionary in its description of
Item BHCK4230: Provision for Loan and Lease Losses. The item should “…include the
amount needed to make the allowance for loan and lease losses … adequate to absorb
expected … losses, based upon management's evaluation of the loans and leases that the
34
reporting bank has the intent and ability to hold for the foreseeable future or until
maturity or payoff.”
4. Cornett, McNutt, and Tehranian (2009) also employ the Beatty, Ke, and Petroni
(2002) model. However, they use the level of nonperforming loans on the right-
hand side, whereas Beatty et al. use the change in nonperforming loans. We
experimented with both specifications, and found that it made no difference to our
results. We present the results using levels, as it allows our sample to begin a year
earlier.
5. This approach is analogous to the common use of the modified Jones model to derive
“normal” accruals for industrial firms and the use of residuals from that model as a
measure of discretionary accruals. Our procedure differs for the crash years, however.
As noted above, we apply the coefficients estimated through 2006 to bank data in 2007-
2009 to estimate discretionary loan loss provisions during those years. Therefore,
disruptions to bank activities during the crash will not distort our estimates of “normal”
bank behavior.
6. The exclusion of 2007-2009 from these summary statistics explains why there are 4,112
banks in Table 1, but only 3,267 observations in Table 2. Also, while the behavioral
equations (1) and (3) are estimated over the 1994-2006 period, the final sample period
does not begin until 1997 because some of the variables used in the following regression
analysis entail 3-year lagged values (see below).
35
7. The average value is not precisely zero because, while we estimate equations (1) and (3)
over the 1994-2006 period, the final sample begins in 1997 as the earnings management
variables are defined as three-year moving sums of lagged values.
8. These values do not add up because the absolute value of a sum is not the sum of
absolute values.
9. Results using only one lead and lag of weekly returns were nearly identical.
10. Once we reach the crisis years, however, bank-specific crashes will have large impacts
on the estimate of cross-sectional dispersion in that year. To avoid the resulting
overestimate in residual standard deviation, we set residual standard deviation for 2007-
2009 equal to the firm’s average value in the pre-crisis years.
11. The capital ratio for each bank is defined as (Tier 1 capital allowable under the risk-
based capital guidelines) / (average total assets net of deductions), as reported on the
bank's consolidated Y-9C Report. In turn, total bank assets equal all foreign and
domestic assets reported on each bank's consolidated Y-9C Report.
12. Including bank fixed effects would result in biased coefficient estimates (Stata 2009, p.
410), so we exclude them in Table 5. Nevertheless, in unreported regressions, we
experimented with bank fixed effects and found that they had almost no impact on our
estimates.
13. This test requires that the coefficients for jumps versus crashes be nested in a single
regression framework which allows us to impose a constraint that the coefficients are
equal. Therefore, we compute these chi-square statistics using a multinomial probit
regression allowing for 3 states: crashes, jumps, or neither event.
36
14. Symmetrically with the positive correlation between earnings management and loan loss
provisions in the crisis years, one may have expected negative correlation in the pre-
crisis years, with more aggressive earnings managers understating potential loan loss
exposure. If high earnings management banks made riskier loans, however, (which
seems to be the case based on loan loss provisions during the crisis) the lack of
correlation between loan loss provisions and earnings management would imply that
there was in fact under-reserving in the pre-crisis years, since those riskier loans should
have elicited higher reserves.
15. We collect these implied volatilities from the OptionMetrics database. We average the
implied volatilities of the closest-to-the-money 30-day call and put options.
16. We thank the referee for this observation.
37
Table 1: Number of commercial banks in the sample
This table lists the distribution of the sample bank holding companies (BHCs) by
year. All accounting data are obtained from FFIEC Call Reports databases found on
the Chicago Federal Reserve’s Website (www.chicagofed.org).
Year BHCs 1997 289 1998 283 1999 304 2000 312 2001 325 2002 346 2003 362 2004 354 2005 367 2006 325 2007 299 2008 279 2009 267
Total 4,112
38
Table 2: Summary statistics for banks, pre-crisis years: 1997-2006 This table provides summary statistics for all variables used in the analysis. DISC_EARN = discretionary
earnings as a percent of total assets = DISC_GAINS – DISC_LLP, DISC_LLP = discretionary loan loss
provisions as a percent of total assets, DISC_GAINS = discretionary realized security gains and losses as a
percentage of total assets. Return on assets = net income in year t/total assets at end of year t.
EARN_MGT = |DISC_EARNt-1| + |DISC_EARNt-2| + |DISC_EARNt-3|
LOAN_MGT = |DISC_LLPt-1| + |DISC_LLPt-2| + |DISC_LLPt-3|
GAINS_MGT = |DISC_GAINSt-1| + |DISC_GAINSt-2| + |DISC_GAINSt-3| Panel C statistics on bank performance are summary statistics of annual data for the sample of banks
pooled across years. Worst-week return is lowest bank-specific return over the course of each fiscal year.
Residual standard deviation is the standard error of regression residuals from the estimation of an index
model regression, equation (8), of bank returns against the return on the CRSP value-weighted market
index and the Fama-French bank industry index. Each regression is estimated for each bank using weekly
observations for the year.
Mean Median Std dev 1st %ile 99th %ile ObservationsPanel A: Descriptive statistics on discretionary earnings variables DISC_EARN (%) -0.014 0.004 0.370 -1.185 0.858 3,267 DISC_LLP (%) -0.004 -0.023 0.257 -0.481 0.867 3,267 DISC_GAINS (%) -0.018 -0.023 0.281 -0.656 0.764 3,267 Return on assets (%) -0.014 0.004 0.370 -1.185 0.858 3,267 Panel B: Descriptive statistics on earnings management variables EARN_MGT (%) 0.601 0.392 0.991 0.069 3.876 3,267 LLP_MGT (%) 0.430 0.299 0.501 0.044 2.630 3,267 GAINS_MGT (%) 0.380 0.227 0.902 0.029 2.845 3,267 Panel C: Descriptive statistics on bank stock market performance Worst-week return (%) -7.138 -6.398 3.596 -19.778 -2.317 3,267 Residual standard deviation (%)
3.098 2.883 1.257 1.172 7.612 3,267
39
Table 3: Time series behavior of components of discretionary bank earnings
Each component of earnings management is regressed on its own lagged values.
Observations are annual over the period 1997-2006. Discretionary items are estimated as
residuals from equations that predict loan loss provisions and realized securities gains and
losses based on bank characteristics (see Beatty et al., 2002). The models of “normal” loan
loss provisions and realized securities gains and losses are contained in Appendix A. These
regressions are estimated with bank and year fixed effects.
Dependent Variable Explanatory variable Coefficient t-statistic
Panel A: Discretionary earnings DISC_EARN DISC_EARN(-1) 0.105 5.65 DISC_EARN(-2) -0.054 -2.79 DISC_EARN(-3) -0.150 -7.61 Observations 3,267 Fixed effects Y
Panel B: Discretionary loan loss provisions DISC_LLP DISC_LLP(-1) 0.210 11.30 DISC_LLP(-2) -0.093 -4.80 DISC_LLP(-3) -0.111 -5.70 Observations 3,267 Fixed effects Y
Panel C: Discretionary securities DISC_GAINS DISC_GAINS (-1) 0.027 1.40 DISC_GAINS (-2) -0.084 -4.18 DISC_GAINS (-3) -0.196 -9.67
Observations 3,267 Fixed effects Y
40
Table 4: Incidence and average magnitude of weekly crashes
The percentage of crashes equals the fraction of banks with at least one week in the year with
bank-specific returns less than 3.09 standard errors below the mean. The mean (median)
crash is the average (median) across banks of the weekly stock return during crash weeks,
expressed as a multiple of the bank-specific standard deviation.
Year Percentage
crashes
Mean crash (measured in
standard deviations)
Median crash (measured in
standard deviations) 1997 3.8% -3.40 -3.19 1998 8.1% -3.42 -3.32 1999 11.2% -3.46 -3.28 2000 12.8% -3.68 -3.55 2001 7.7% -3.42 -3.27 2002 10.4% -3.52 -3.52 2003 10.8% -3.65 -3.43 2004 9.3% -3.56 -3.45 2005 10.6% -3.60 -3.51 2006 11.7% -3.51 -3.34 2007 21.7% -4.00 -3.66 2008 74.2% -5.61 -4.63 2009 81.6% -5.89 -5.18
41
Table 5: Crash incidence as a function of bank earnings management Probit regressions, with dependent variable equal to 1 if the lowest bank-specific return in the year is worse than 3.09 standard deviations below zero. Sample period = 1997-2009. Bank specific returns are calculated as residuals from estimation of an index model regression, equation (8), of weekly bank returns against the return on the CRSP value-weighted market index and the Fama-French bank industry index. Earnings management variables are winsorized at 1st and 99th percentile values. Regressions are estimated with year fixed effects. Economic magnitude equals the predicted change in the probability of a crash week occurring during the year given a change in the right-hand side variable from the 10th percentile in the sample distribution to the 90th percentile. (1) (2) (3) (4)EARN_MGT 8.215* 2.566 (t-statistic) (1.935) (0.415) (economic magnitude) 0.01969 0.00655 EARN_MGT * CRISIS 22.699** (2.219) 0.08882 LLP_MGT 11.860** -1.280 (2.052) (-0.148) 0.02088 -0.00269 GAINS_MGT -0.529 2.390 (-0.067) (0.252) -0.00078 0.00437 LLP_MGT * CRISIS 62.140*** (3.627) 0.16432 GAINS_MGT * CRISIS -3.405 (-0.234) -0.00644 Total Assets(t 1) 0.088 0.119 0.070 0.114 (0.243) (0.328) (0.193) (0.304) 0.00036 0.00049 0.00033 0.00058 Capital Ratio(t 1) -1.475 -1.080 -2.099 -0.526 (-1.175) (-0.882) (-1.387) (-0.410) -0.00772 -0.00566 -0.01251 -0.00344 Amihud measure(t 1) -0.782 -0.825 -0.615 -0.596 (-0.973) (-1.028) (-0.763) (-0.728) -0.00410 -0.00563 -0.00478 -0.00508 Firm fixed effects N N N N Year fixed effects Y Y Y Y N 4,112 4,112 4,112 4,112 Pseudo R-square 0.278 0.278 0.280 0.282
*significant at 10% level; **significant at 5% level; ***significant at 1% level
42
Table 6: Jump incidence as a function of bank earnings management Probit regressions, with dependent variable equal to 1 if the lowest bank-specific return in the year is more than 3.09 standard deviations above zero. Sample period = 1997-2009. Bank specific returns are calculated as residuals from estimation of an index model regression, equation (8), of weekly bank returns against the return on the CRSP value-weighted market index and the Fama-French bank industry index. Earnings management variables are winsorized at 1st and 99th percentile values. Regressions are estimated with year fixed effects. Economic magnitude equals the predicted change in the probability of a jump week occurring during the year given a change in the right-hand side variable from the 10th percentile in the sample distribution to the 90th percentile. (1) (2) (3) (4)EARN_MGT 2.807 1.134 (t-statistic) (0.899) (0.318) (economic magnitude) 0.01036 0.00398 EARN_MGT * CRISIS 9.191 (1.287) 0.04950 LLP_MGT 10.488** 7.590 (2.127) (1.391) 0.02841 0.02039 GAINS_MGT -8.715 -10.263 (-1.320) (-1.352) -0.01979 -0.02397 LLP_MGT * CRISIS 22.761 (1.375) 0.07696 GAINS_MGT * CRISIS 11.886 (0.961) 0.02876 Total Assets(t 1) -0.587*** -0.553*** -0.597*** -0.575*** (-3.891) (-3.913) (-3.931) (-4.206) -0.00370 -0.00349 -0.00384 -0.00376 Capital Ratio(t 1) 0.044 0.242 -0.190 0.189 (0.029) (0.196) (-0.115) (0.141) 0.00035 0.00195 -0.00156 0.00158 Amihud measure(t 1) 0.554 0.412 0.644 0.545 (0.665) (0.500) (0.766) (0.653) 0.00446 0.00433 0.00689 0.00594 Firm fixed effects N N N N Year fixed effects Y Y Y Y N 4,112 4,112 4,112 4,112 Pseudo R-square 0.278 0.278 0.280 0.282
*significant at 10% level; **significant at 5% level; ***significant at 1% level
43
Table 7: Increase in implied volatility of banks as a function of earnings management and
its components
In this table, we rank banks by earnings management and then assign them to terciles. The mean
implied volatility of at-the-money call options for each group is computed pre-crisis (January 31,
2007) and mid-crisis (January 30, 2009). The difference-in-difference is positive when high
earnings management banks demonstrate greater increases in implied volatility than low earnings
management banks.
A.
Mean implied
volatility Jan. 31 2007 N
Mean implied
volatility Jan. 30 2009 N Difference t-stat (p-value)
EARN_MGT [bottom tercile] 0.199 12 0.815 12 0.616 EARN_MGT [top tercile] 0.178 18 0.929 18 0.751
Diff-in-diff 0.135 1.597 (0.121)
B.
Mean implied
volatility Jan. 31 2007 N
Mean implied
volatility Jan. 30 2009 N Difference t-stat (p-value)
LLP_MGT [bottom tercile] 0.180 17 0.929 17 0.749 LLP_MGT [top tercile] 0.195 15 0.730 15 0.535
Diff-in-diff 0.213 3.546 (0.0013)
C.
Mean implied
volatility Jan. 31 2007 N
Mean implied
volatility Jan. 30 2009 N Difference t-stat (p-value)
GAINS_MGT [bottom tercile] 0.193 9 0.806 9 0.612 GAINS_MGT [top tercile] 0.191 23 0.953 23 0.762
Diff-in-diff 0.150 1.638 (0.112)
44
Table 8: Decline in operating performance for crashing versus non-crashing banks
This table reports the average change in ROA for banks with crashes in a given year versus the average
change in ROA for non-crashing banks. Change in ROA is computed as ROA in the year following a
crash minus ROA in the year preceding a crash. The difference in differences is computed for each year
of the sample. Averages and standard deviations are calculated across the years in the sample period.
The table reports Fama-MacBeth statistics with 12 degrees of freedom.
Difference in differences t-statistic p-value
All crashing banks 0.11% 1.95 7.1%
50% most severe crashing banks 0.22% 1.90 9.1%
25% most severe crashing banks 0.44% 2.27 2.7%
45
Table A1: Bank Earnings Management
Panel A: Variables used to find discretionary and nondiscretionary accruals
LOSS = loan loss provisions as a percentage of total loans
NPL = nonperforming loans (loans past due 90 days or more and still accruing interest and loans in nonaccrual status) as a percentage of total loans LLR = loan loss allowance as a percentage of total loans
LOANR = real estate loans as a percentage of total loans
LOANC = commercial and industrial loans as a percentage of total loans
LOAND = loans to depository institutions as a percentage of total loans
LOANA = agriculture loans as a percentage of total loans
LOANI = consumer loans as a percentage of total loans
LOANF = loans to foreign governments as a percentage of total loans
GAINS = realized security gains and losses (includes realized gains and losses from available-for-sale securities and held-to-maturity securities) as a percentage of total assets UGAINS = unrealized gains and losses (includes unrealized gains and losses from available-for-sale securities) as a percentage of total assets ASSETS = total assets (in billions of dollars)
LOANS = total loans (in billions of dollars)
Panel B: Descriptive statistics on variables used to calculate discretionary and nondiscretionary loan loss provisions and realized securities gains or losses, 1997-2009
Mean Median Std Dev 1st %ile 99th %ile Observations
LOSS (%) 0.61 0.32 1.03 -0.11 5.35 4,112
LOANR (%) 69.11 72.07 17.39 7.68 97.40 4,112
LOANC (%) 17.32 14.88 11.51 0.59 59.94 4,112
LOAND (%) 0.17 0.00 1.19 0.00 2.79 4,112
LOANA (%) 0.94 0.11 2.10 0.00 10.63 4,112
LOANI (%) 8.53 5.59 9.01 0.05 41.86 4,112
LOANF (%) 0.02 0.00 0.20 0.00 0.57 4,112
NPL (%) 0.82 0.29 1.84 0.00 7.65 4,112
LLR (%) 1.45 1.32 0.63 0.46 4.12 4,112
GAINS (%) 0.02 0.01 0.52 -1.07 0.84 4,112
UGAINS (%) 0.04 0.03 1.47 -1.85 1.92 4,112
ASSETS ($ billion)
21.9 1.61 131.0 0.23 482.0 4,112
LOANS ($ billion)
11.2 1.04 58.4 0.13 216.0 4,112
46
Panel C: Regression results, sample period 1994-2006
Independent Variable
Loan loss provisions Coefficient Estimate
(t-value)
Realized security gains/losses
Coefficient Estimate (t-value)
LNASSET -0.00003 0.00005 (-0.38) (1.49) NPL 0.1871 (3.75)*** LLR 0.2692 (5.64)*** LOANR -0.0043 (-3.06)*** LOANC 0.0027 (1.21) LOAND -0.0055 (-0.65) LOANA -0.0094 (-2.20)** LOANI 0.0045 (2.93)*** LOANF -0.074 (-1.11) UGAINS 0.2554 (2.04)** Bank-years 5,701 5,710 Adjusted R2 .510 .1618 ** Significant at the 5% level. *** Significant at the 1% level. Standard errors are clustered by firm.
Figure 1
Standard deviation of discretionary earnings
Cross sectional standard deviation of discretionary earnings, DISC_EARN, across the sample of
banks in each year. Discretionary earnings equals discretionary realization of securities gains or
losses minus discretionary loan loss provisions, each expressed as a percentage of total assets.
0.0%
0.2%
0.4%
0.6%
0.8%
1.0%
1.2%
1.4%
1.6%
1.8%
2.0%
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
48
Figure 2
Standard deviation of bank-specific weekly rates of return.
Standard error of regression residuals from estimation of index model regression, equation (8), of
bank returns against the return on the CRSP value-weighted market index and the Fama-French
bank-industry index. Each regression is estimated for each bank using weekly observations for
the year. The residual standard deviations are averaged across banks in each year.
0%
1%
2%
3%
4%
5%
6%
7%
8%
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Res
idua
l sta
ndar
d de
viat
ion
of w
eekl
y re
turn
49
Figure 3
Earnings management and annual returns
In each year, banks are ranked by earnings management and sorted into 5 quintiles. The
difference in annual firm-specific returns between quintile (1) and quintile (5) banks are
presented for each year. Annual firm-specific returns are computed by compounding weekly
idiosyncratic returns within bank-years. A positive value indicates that the most aggressive
earnings-management quintile outperformed the least aggressive quintile. p-values are presented
for the difference between the fifth and first quintile annual returns in each year.
p-val .028 .819 .029 .019 .004 .836 .111 .199 .072 .468 .598 .006 .002
1.2%
-0.1%
2.4%
3.9%
1.7%
0.1%
1.3%0.8% 1.0%
0.3% 0.3%
7.0%
13.0%
-2%
0%
2%
4%
6%
8%
10%
12%
14%
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009