Bank Earnings Management and Tail Risk during the Financial Crisis
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LEE J. COHEN MARCIA MILLON CORNETT ALAN J. MARCUS HASSAN TEHRANIAN Bank Earnings Management and Tail Risk during the Financial Crisis 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 mar- ket 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 perfor- mance. Bank regulators may therefore interpret them as early warning signs of impending problems. JEL codes: G01, G11, G21, G28, M40 Keywords: financial institutions, earnings management, crashes, financial crisis. BANK INVESTORS HAVE LONG been concerned with tail risk, that is, extreme declines in a bank’s stock price. The financial crisis of 2007–09 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. 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. LEE J. COHEN is Assistant Professor of Finance, Finance Department, University of Georgia (E-mail: firstname.lastname@example.org). MARCIA MILLON CORNETT is Professor of Finance, Finance Department, Bentley Uni- versity (E-mail: email@example.com). ALAN J. MARCUS is Mario J. Gabelli Professor of Finance, Finance Department, Boston College (E-mail: firstname.lastname@example.org). HASSAN TEHRANIAN is the Griffith Family Mil- lennium Chair in Finance, Finance Department, Boston College (E-mail: email@example.com). Received January 25, 2012; and accepted in revised form November 6, 2012. Journal of Money, Credit and Banking, Vol. 46, No. 1 (February 2014) C 2014 The Ohio State University
172 : MONEY, CREDIT AND BANKING 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 ex- posure 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 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 informa- tion 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 manage- ment 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 2005). 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. 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
LEE J. COHEN ET AL. : 173 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 1, 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 2 discusses our sample and data sources. Section 3 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 4 concludes the paper, where we consider the policy implications for banks and their regulators. 1. RELATED LITERATURE 1.1 Earnings Management and Crash Risk 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 discre- tionary accruals from the modified Jones (1991) model (Dechow, Sloan, and Sweeney 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 nondecreasing 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.
174 : MONEY, CREDIT AND BANKING 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 discretionary accruals can be used to detect earnings management. Cohen, Dey, and Lys (2005) 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. 1.2 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 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.
LEE J. COHEN ET AL. : 175 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 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 and 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,” that is, report one drastic earnings decline after hiding a series of smaller declines in previous years (Arya, Glover, and Sunder 1998, Demski 1998), a pattern consistent with infrequent but large stock market declines. 2. 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 precrisis period, 1997–2006, and the crisis period, 2007–09. 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 U.S. domestic banks, found on the Chicago Federal Reserve’s 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.
176 : MONEY, CREDIT AND BANKING TABLE 1 NUMBER OF COMMERCIAL BANKS IN THE SAMPLE 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 NOTE: 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). website, www.chicagofed.org. Bank stock return data are collected from the Center for Research in Security Prices (CRSP) database. 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. 2.1 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 nondiscretionary, 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 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 reporting bank has the intent and ability to hold for the foreseeable future or until maturity or payoff.”
LEE J. COHEN ET AL. : 177 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. 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 + β1 LNASSETit + β2 NPLit + β3 LLRit + β4 LOANRit + β5 LOANCit + β6 LOANDit + β7 LOANAit + β8 LOANIit + β9 LOANFit + εit , (1) where i = bank holding company identifier; t = year (1994 to 2006); r = U.S. Office of the Comptroller of the Currency defined district number; α tr = 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 com- position of the loan portfolio, and therefore, the residual of the regression is taken 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, Ke, and Petroni 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.
178 : MONEY, CREDIT AND BANKING 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 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 LOANSit DISC LLPit = εit × , (2) ASSETSit 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 precrisis period with time fixed effects. Their model of “normal” realized security gains and losses (GAINSit ) is GAINSit = αt + β1 LNASSETit + β2 UGAINSit + 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 Table A1 in the Appendix summarizes the variables used to find discretionary and nondiscretionary loan loss provisions and realized securities 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 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 2007–09 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.
LEE J. COHEN ET AL. : 179 TABLE 2 SUMMARY STATISTICS FOR BANKS, PRECRISIS YEARS: 1997–2006 Mean Median Std dev 1st%ile 99th%ile Observations Panel 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 NOTE: 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. gains or losses: DISC EARNit = DISC GAINSit −DISC LLPit . (4) High levels of DISC_EARN amount to underreporting of loan loss provisions and/or higher realizations of securities gains, which, ceteris paribus, increase income. Nega- tive values for DISC_EARN would indicate that loan loss provisions are overreported 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), es- timated over the precrisis period, 1997–2006.6 The average level of both discretionary loan loss provisions and realized securities gains (both as a percent of assets) are mea- sured 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 6. The exclusion of 2007–09 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). 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 3-year moving sums of lagged values.
180 : MONEY, CREDIT AND BANKING 2.0% 1.8% 1.6% 1.4% 1.2% 1.0% 0.8% 0.6% 0.4% 0.2% 0.0% 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 FIG. 1. Standard Deviation of Discretionary Earnings. NOTE: Cross-sectional standard deviation of discretionary earnings, DISC_EARN, across the sample of banks in each year. Discretionary earnings equal discretionary realization of securities gains or losses minus discretionary loan loss provisions, each expressed as a percentage of total assets. numbers. Discretionary loan loss provisions, DISC_LLP, in the precrisis period range from a 1st 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 corre- sponding 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 nontrivial portion of the variation in reported bank performance (the standard deviation of 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 2007–09 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. 2.2 Earnings Management Table 3 examines the time-series properties of discretionary earnings, DISC_EARN, as well as its two components, discretionary loan loss provi- sions, 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 3 years. We estimate the relation over the precrisis period, 1997–2006, be- cause Figure 1 suggests that the extreme events of the crisis years might disrupt the patterns that characterized each bank in the previous decade.
LEE J. COHEN ET AL. : 181 TABLE 3 TIME-SERIES BEHAVIOR OF COMPONENTS OF DISCRETIONARY BANK EARNINGS 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 gains/losses 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 NOTE: 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, Ke, and Petroni 2002). The models of “normal” loan loss provisions and realized securities gains and losses are contained in the Appendix. These regressions are estimated with bank and year fixed effects. Panel A of Table 3 shows that in the short term (i.e., at a 1-year lag), discretionary earnings exhibit positive serial correlation, with a positive and statistically significant coefficient (0.105) on the 1-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 1-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 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 deter- mine 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 con- siderations. 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.
182 : MONEY, CREDIT AND BANKING 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 discre- tionary 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 manip- ulation to increase reported earnings: the decline, with the reversal of 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 3-year moving sum of the absolute value of DISC_EARN. Although managers may prefer account- ing 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 ac- counting choices that artificially enhance reported earnings in one period eventually must be reversed. Like them as well, we use the 3-year moving sum (instead of a 1-year value) to capture the multiyear 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) Panel B of Table 2 presents descriptive statistics for these variables in the precrisis years. The mean value of EARN_MGT (computed over the preceding 3 years, t − 3 to t − 1) is 0.601% of assets. The mean value of LLP_MGT is 0.430% of assets, and
LEE J. COHEN ET AL. : 183 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. 2.3 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 esti- mate 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 r j,t = α j + β1, j rm,t−2 + β2, j ri,t−2 + β3, j rm,t−1 + β4, j ri,t−1 + β5, j rm,t + β6, j ri,t + β7, j rm,t+1 + β8, j ri,t+1 + β9, j rm,t+2 + β10, j ri,t+2 + ε 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), εj,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. Summary statistics for worst-week bank-specific returns and residual risk in the precrisis 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 precrisis 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 precrisis 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 precrisis 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. 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.
184 : MONEY, CREDIT AND BANKING 8% Residual standard deviation of weekly return 7% 6% 5% 4% 3% 2% 1% 0% 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 FIG. 2. Standard Deviation of Bank-Specific Weekly Rates of Return. NOTE: 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. TABLE 4 INCIDENCE AND AVERAGE MAGNITUDE OF WEEKLY CRASHES 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 NOTE: The percentage of crashes equals the fraction of banks with at least 1 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. 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 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
LEE J. COHEN ET AL. : 185 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 1 week falling below this cutoff. The percentage in the precrisis years is generally between 5% and 10%, but it balloons to 74.2% in 2008. The negative returns corresponding to these crashes are quite large; in the precrisis 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 2007–09 period the median loss in a crash week is 4.49 times the standard error. 3. EMPIRICAL RESULTS 3.1 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 (2), the coefficients on these terms are fixed over the entire sample period. In these columns, earnings management as a whole and more particularly dis- cretionary loan loss provisions are marginally significant. However, in columns (3) and (4), we introduce crisis-period interaction terms that allow earnings management deviation, we set residual standard deviation for 2007–09 equal to the firm’s average value in the precrisis 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.
186 : MONEY, CREDIT AND BANKING TABLE 5 CRASH INCIDENCE AS A FUNCTION OF BANK EARNINGS MANAGEMENT (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 R2 0.278 0.278 0.280 0.282 NOTE: 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. *significant at 10% level; **significant at 5% level; ***significant at 1% level. to have different effects in the pre- and postcrisis periods. In this specification, there is no apparent relationship between earnings management and crash likelihood in the precrisis 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 statisti- cally 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 propen- sity. Even in the crisis, it is statistically insignificant, with the GAINS_MGT * CRISIS
LEE J. COHEN ET AL. : 187 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 10th 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 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 in- significant in explaining crash likelihood. In sum, it appears from Table 5 that banks engaging in greater earnings management are more likely to experience crashes dur- ing the crisis, even though such crash risk does not make itself evident in the precrisis 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 probabili- ties.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) equal- ity of the coefficients on the components of earnings management, LLP_MGT and GAINS_MGT, yields a chi-square of 14.7 and a p-value of 0.07%. 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 three states: crashes, jumps, or neither event.
188 : MONEY, CREDIT AND BANKING TABLE 6 JUMP INCIDENCE AS A FUNCTION OF BANK EARNINGS MANAGEMENT (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 R2 0.278 0.278 0.280 0.282 NOTE: 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. *significant at 10% level; **significant at 5% level; ***significant at 1% level. 3.2 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
LEE J. COHEN ET AL. : 189 14% 13.0% 12% 10% 8% 7.0% 6% 4% 3.9% 2.4% 2% 1.7% 1.3% 1.2% 0.8% 1.0% -0.1% 0.1% 0.3% 0.3% 0% 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 -2% p-val 0.028 0.819 0.029 0.019 0.004 0.836 0.111 0.199 0.072 0.468 0.598 0.006 0.002 FIG. 3. Earnings Management and Annual Returns. NOTE: In each year, banks are ranked by earnings management and sorted into five 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. The p-values are presented for the difference between the fifth and first quintile annual returns in each year. 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 the least aggressive ones by highly substantial margins, 7.0% and 13.0% in those 2 years. These large economic differences are also highly statistically significant, with p-values of 0.6% and 0.2% in the 2 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 man- agement 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.
190 : MONEY, CREDIT AND BANKING TABLE 7 INCREASE IN IMPLIED VOLATILITY OF BANKS AS A FUNCTION OF EARNINGS MANAGEMENT AND ITS COMPONENTS Mean implied Mean implied volatility volatility Jan. 31, 2007 N Jan. 30, 2009 N Difference t-stat (p-value) Panel A 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) Panel B 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) Panel C 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) NOTE: 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 precrisis (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. 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 pro- visions during the crisis years. That correlation is 0.324, indicating that loan loss provisions during the crisis increased with the aggressiveness of earnings manage- ment as measured before the crisis. In other words, compared to less aggressive earnings managers, aggressive banks revealed during the crisis greater negative in- formation about the quality of their loan portfolios. In contrast, earnings management and loan loss provisions in the precrisis years are nearly unrelated, with a correlation coefficient of only 0.035.14 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 precrisis 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 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 precrisis 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 underreserving in the precrisis 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.
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