Can the Equity Markets Help Predict Bank Failures? 

Timothy J. Curry* 

Peter J. Elmer** 

Gary S. Fissel* 

July, 2004

Working Paper 2004-03 

*Senior Financial Economist, Division of Insurance and Research, Federal Deposit Insurance 

Corporation (FDIC), Washington, DC 20429 

**Reston, Virginia 20190 

The views expressed here are those of the authors and do not necessarily reflect those of the 

FDIC or its staff. The authors would like to thank Audrey Clement for extensive assistance; and 

Gerald Hanweck, Andrew Davenport, anonymous referees, and the participants in the 2004 

Washington Area Finance Association meetings and the 2004 Western Economic Association 

International meetings for helpful comments.
Abstract 

The paper examines the informational content of market data when these data are incorporated 

into traditional models that predict bank failures. To assess whether financial markets can provide

timely information about firm distress, we first examine the pre-failure behavior of market variables 

over long periods before failure. The univariate results document distinct patterns of declining 

prices, negative returns, declining dividends, and rising return volatility several years before failure. 

Several other market-related measures, however, such as trading volume and share turnover, 

show no clear trend. Next we test for the contribution of market variables in relation to Call Report

variables in the prediction of bank failures over the 1989–1995 period. The findings show that 

selected market variables like equity prices, returns, and volatility of returns add important

information to the identification of failed institutions beyond the information contained in 

quarterly accounting data. In-sample and out-of-sample tests show that the use of market data 

does improve the sample forecast of bank failure. 

JEL Codes: G21, G28 

Keywords: Bank Failure, Market information and Off-site Surveillance Models 

 1
Introduction 

Failure prediction represents a special interest in banking because of the regulated nature of 

the industry as well as the federal safety net provided by deposit insurance. In this regard, bank 

supervisors depend on “traditional” models to forecast bank failure; these models are based on 

financial data obtained from quarterly Reports of Income and Condition (Call Reports).1

 In 

recent years, at the behest of the U.S. Congress, international regulatory bodies, and the 

academic community, bank regulators have been exploring whether data obtained from the 

securities markets (debt and equity) can be used to supplement failure-prediction models. To the 

extent that financial markets are efficient and market price and return movements for securities 

can be used to anticipate events including failure, then bank regulators might apply information 

embedded in market prices and trading patterns to improve early-warning and off-site 

monitoring systems. Improved early-warning systems necessarily enhance bank supervision, 

thereby reducing the likelihood and cost of failures. More generally, integrating market-based 

information into the tools of bank supervision represents a partial response to Flannery’s (1998) 

call for enhancing our understanding of the use of market information in prudential bank 

supervision. 

Predicting failure is especially interesting because failure normally follows the dissemination 

of large amounts of negative information, often over long periods of time. Failure is also the only 

financial event for which the post-event stock price is known before the event. The period before 

failure is almost always associated with negative returns, the cessation of trading on organized 

exchanges, and the fall of prices to approximately zero. These regularities suggest that the period 

preceding failure should provide an environment conducive to the formation of trends in market-

 

1

 In this paper, “bank” refers to an FDIC-insured commercial bank or thrift institution.

 2
based data. 

This paper develops two themes on the relation between equity market data and bank 

failure.2

 The first theme examines long-term trends in market variables before failure to see if 

financial markets can provide timely information about firm failure. Early research by Pettway 

(1976, 1980) and Pettway and Sinkey (1980), and later research by Davies (1993), identified 

return patterns several years before bank failure. Our approach extends that previous work by 

exploring the longer-term pre-failure trends of a wide variety of equity market variables for 

actively traded institutions. This perspective provides a global view of the pre-failure price and 

return patterns—a view that finds several trends before bank failure. 

The second theme builds on the first by testing the extent to which equity market variables 

improve the predictive performance of traditional “ratio”-based models of bank failure.3

 Given the 

breadth of our focus, the analysis uses market variable benchmarks that are not generally 

representative of variables commonly used in previous research. In so doing, we test the extent to 

which market variables can be viewed as distinct, or as providing a different perspective, from other 

types of financial variables identified by previous theoretical and empirical work. 

The results show that univariate analysis documents distinct patterns of declining prices, 

negative returns, declining dividends, and rising return volatility several years before bank

failure. However, no clear trend emerges for several other market measures, including trading 

volume and share turnover. Multivariate analysis shows that market information such as equity 

prices and returns improves the failure-predictive content of the traditional models, which are 

 

2

 The advantages of using equity market information over using data obtained from the debt markets have been 

articulated by Saunders (2001). 3

 Among the many papers using only financial accounting data are Sinkey (1975), Bovenzi, Marino, and McFadden 

(1983), Elmer and Borowski (1988), Cole and Gunther (1995, 1998), and Kolari et al. (2000). Shumway (2001) 

uses a limited number of market variables in a traditional ratio-based model, but otherwise focuses on the 

forecasting advantages of hazard models. Commercial models, such as the “KMV” model described by Crouhy, 

 3
based only on Call Report financial ratios. In-sample and out-of-sample predictions show that 

the use of stock market data does improve the forecast of bank failure. 

The next section discusses related literature. The subsequent section presents the sample 

and data and is followed by a section studying long-term univariate trends of returns, risk, and 

other market-related variables for the period before failure for sample institutions. Then comes a 

section in which logistic regressions are performed to test the predictive content of market-related 

variables versus the content of financial data to explain bank failure. The last section concludes. 

Related Literature 

It is well known that equity prices of banks destined for failure appear to fall for long 

periods before failure, thereby generating abnormal negative returns over those periods. 

However, few studies have analyzed these pre-failure return patterns of failed institutions. As 

mentioned, Pettway (1980) and Pettway and Sinkey (1980) find patterns of negative excess 

returns in banks as much as three years before failure, as well as similar patterns almost a year 

before regulators begin the examination that initially identifies the problems preceding failure. 

One limitation of this work was a limited sample of only six large-bank failures. Davies (1993) 

found that market information can help predict bank and bank holding company insolvency (not 

failure) when used with accounting and supervisory data. However, the analysis suffers from the 

use of only a single market variable—the market-to-book capital ratio. 

While not focusing on bank failures, Curry, Fissel, and Elmer (2003) found in recent 

work significant declines in stock prices, abnormal returns, and volatility of returns preceding 

regulator-assigned CAMELS ratings to the problem-bank level (3, 4, or 5) two years before the 

 

Galai, and Mark (2000), are also known to use market variables, but their specifications are proprietary. 

 4
rating changes. Berger and Davies (1998) use event-study methodology and find that the equity 

market anticipates upgrades in regulatory ratings but follows downgrades. Berger, Davies, and 

Flannery (2000) find that regulators acquire information sooner than bond rating agencies and 

the equity markets, but the regulatory assessments are less accurate in predicting the future 

performance of bank holding companies than either stock or bond market indicators. However, 

Simons and Cross (1991) found no evidence that the market was cognizant of regulator 

downgrades before the event for a sample of 22 large bank holding companies. 

Several recent studies have incorporated market data into traditional default or 

bankruptcy models to determine if the information adds value in identifying troubled institutions. 

 Krainer and Lopez (2001, 2003a, 2003b) find that equity market variables, such as stock returns 

and equity-based expected default frequencies, can be useful to bank regulators for assessing the 

financial condition of bank holding companies. Gunther, Levonian, and Moore (2001) found 

that a measure of financial viability based on equity market data (expected default frequency) 

helps predict the financial condition of bank holding companies as reflected in their regulatory 

ratings. Finally, Curry, Fissel, and Hanweck (2003) find that market-indicator variables such as 

excess returns and volatility of returns add value to models in predicting changes in bank holding 

company supervisory risk ratings. 

This paper augments the existing literature on several fronts. First, as mentioned, we 

focus on the long-term pre-failure trends for a wide variety of market variables that have 

generally not been used in previous work. This long-term analysis will shed light on the 

signaling and timing aspects of market data. Second, we use equity market variables along with 

quarterly financial data to predict bank failures, whereas most recent work focuses on predicting 

bank or holding company supervisory ratings. Third, we test for a wide variety of market 

 5
variables with deep theoretical or empirical roots, including market returns, return volatility, 

return patterns, dividend policy, market valuations, and trading volume.

In summary, the several years preceding bank failure are well suited for testing the 

formation of market-related return patterns. This period is associated with an unusually large 

amount of private information regarding a fundamental change in a bank’s health at the same

time that published financial data are abundant. Moreover, previous research has confirmed that 

pre-failure return patterns in the banking industry consistent with a variety of market-related 

variables are potentially related to financial distress. Testing the failure-predictive content of 

these variables vis-à-vis that of publicly available financial data explores the ability of the 

variables to help predict bank failure. 

Sample and Data 

The sample comprises approximately 40 percent banks and 60 percent thrifts that were 

publicly traded and failed during the 1989–1995 period. The 1989 cutoff was chosen to avoid 

the many problems that existed in the banking and thrift industries before that date. In the case 

of thrifts, 1989 marked a watershed year with the passage of the Federal Financial Institutions 

Reform and Recovery Act (FIRREA), which not only provided a portion of the funds needed to 

resolve the thrift crisis but also contained provisions improving safety and soundness, such as 

higher capital requirements.4

 While the banking crisis of the 1980s was not as deep as the thrift 

crisis of the same era, the late 1980s nevertheless marked a sea change in bank regulation, owing 

to an increase in the regularity of bank examinations and other requirements.5

 

4

 See Gupta and Misra (1999) for an overview of changes made to the banking system throughout the 1980s and 

early 1990s. 5

 For example, figure 12.4 in Curry et al. (1997) shows a spike in the median examination period for failed banks 

during the mid-1980s, followed by much lower levels beginning in approximately 1989. 

 6
Since our goal is to compare the failure-predictive content of market-related variables 

relative to the content of a bank’s Call Report financial data, the sample excludes multibank 

holding companies. This restriction ensures that the Call Report financial data commonly used 

in failure-prediction models directly correspond to the institution that issues the stock traded in 

the market.6 The restriction also reduces contamination from nonbank activities present in many 

holding companies as well as from “cross-subsidy” policies, whereby banks in multibank 

holding companies provide credit support for other members. Regrettably, the exclusion of 

multibank holding companies prevents inferences regarding our results from extending beyond 

the confines of our sample. Nevertheless, the “cost” of limited application seems more than 

offset by the “benefit” of fostering the cleanest possible environment for testing the failurepredictive content of market-related variables and Call Report financial data. 

A bank or thrift is classified as a failure if it was either liquidated or merged with 

assistance by the FDIC or the RTC between January 1, 1989, and December 31, 1995.7

 All 

institutions in the sample have Call Report financial data and price information from the Center 

for Research in Securities Prices (CRSP) available for 16 quarters preceding failure. As shown 

on the left side of table 1, the sample size for the univariate analysis varies slightly over the 16 

quarters preceding failure. The sample declines in the early years because of the lack of CRSP 

data, which may reflect the fact that several of these institutions were not publicly traded at the 

time. Also, the sample drops noticeably in quarters 1 through 3 because of the de-listing rules of 

 

6

 This correspondence is important because the public equity of banks held by holding companies is typically issued 

at the holding-company level, whereas Call Report financial data are reported at the bank level. Banks are also 

distinguished from their holding companies in bankruptcy, for individual banks are taken over by the Federal

Deposit Insurance Corporation whereas their holding companies fall under the purview of standard bankruptcy law. 7

 Bank failures after 1995 were not included in the sample because most did not meet the sample selection criteria 

described above. Of the two failed institutions that did qualify, stock price information from the Center for 

Research in Securities Prices was not available for a long-enough period for the analysis to be conducted. 

 7
the various exchanges, such as minimum capital requirements or minimum trading activity.8

 The 

sample of failed banks used in the multivariate analysis is slightly smaller than that used in the 

univariate analysis because of the lack of data for specific variables used in the regressions.9

Univariate Trends Preceding Bank Failure

This section analyzes the univariate trends of market-related variables of banks and 

thrifts destined for failure. The goal is to identify market-based variables that financial theory 

might view as possessing failure-predictive content, then to assess the extent to which these 

variables have longer-term monotonic relationships to the likelihood of default—relationships 

either steadily increasing or decreasing as default approaches. The analysis serves to determine 

whether Pettway’s (1980) long-term return trends persist in a larger, more recent sample, while 

expanding the analysis to include new market variables. 

Table 1 examines the univariate characteristics of stock prices, returns, dividends, and 

other market-related variables of a sample of failed banks and thrifts 16 quarters (four years) 

before failure. The data show stock prices declining steadily throughout the four-year period 

before failure. The average stock price begins at 10.64 in quarter 16, then declines to less than 1 

in quarter 1. Steadily declining prices easily explain cumulative quarterly returns that are 

persistently negative throughout the 16-quarter sample. We calculated the cumulative quarterly 

compounded returns as holding-period returns by multiplying unity plus the return for each stock 

i on day t (1+rit) across all trading days reported on the CRSP, less unity. The four years of 

negative cumulative quarterly compounded returns reported in table 1 reaffirm Pettway’s (1980) 

 

8

 In the sample, the most common reasons for de-listing were insufficient number of market makers and insufficient

capital. 9

 For example, for the 4-, 8-, and 12-quarter regressions before failure, the total sample of failed banks and thrifts is 

86, 85, and 63 respectively. As noted, for each set of failed institutions there is a control sample of equal 

 8
finding of negative average returns many years before bank failure. In our case, the larger 

sample facilitates the computation of t-statistics testing the hypotheses that the mean of the 

quarterly cumulative compounded returns equals zero. The t-tests show that the negative returns 

in 13 of the first 16 quarters preceding failure are statistically significant, suggesting a 

statistically recognizable pattern of negative returns for several years preceding failure. 

Table 1 extends the analysis of return trends by calculating market excess or abnormal 

returns based on several market benchmarks. To this end, the quarterly market excess return for 

each stock i=1, . . . , j (MERi) is calculated according to convention: (1) 

 

 

∏ ∏ = =

= + − + −

n

t

mt

n

t

MER i rit r

1 1

(( (1 )) ( (1 )) 1)

where rit = the actual return for stock i on day t=1, . . . , n and rmt = the return on a market index 

on day t. The three columns of excess returns shown in table 1 report the arithmetic average 

excess return for all stocks in each quarterly sample ∑

=

=

j

t

MER j MERt

1

(1/ ) , where j equals the 

number of banks in our sample. The t-statistics below each quarterly MER test the null 

hypothesis that MER = 0. 

The MER statistics in table 1 display a consistent pattern of negative returns that are 

almost always significant at the 1 percent or 5 percent level in all 16 quarters preceding failure, 

regardless of the market index used. The CRSP equal and value-weighted return indexes 

resulted in very similar excess return estimates. A third effort calculated excess returns by 

comparing bank and thrift returns with separate value-weighted bank and thrift industry return 

 

magnitude. 

 9
indexes.10 This effort also resulted in excess returns that are consistently negative and 

significant at the 1 percent or 5 percent level. Since the behaviors of the three excess return 

estimates are very similar, we use the CRSP value-weighted index for the remainder of this study 

in the interests of transparency. 

An intriguing aspect of market data is that various aspects of financial theory effectively 

extend the list of market-related variables beyond prices and returns. For example, table 1 

proceeds by examining two risk variables commonly associated with Fama and French (1993): 

market capitalization and the market-to-book value of equity ratio (ME/BE). Steadily declining 

prices cause market capitalization (price times number of shares) to follow a similar downward 

trend throughout the 16 quarters reported. The declining value of market equity also explains the 

monotonic slide of the ME/BE ratio throughout the period preceding failure. The trend in the 

ME/BE variable reveals the change in a consistent fashion as default approaches. Also, the 

dummy dividend variable shows a consistent declining path throughout the four-year period, as 

fewer firms pay dividends in response to declining financial fortunes or regulatory mandate. 

The last risk variable shown in table 1 is return volatility. This variable is most closely 

associated with Merton’s (1974) option model, which anticipates a rise in return volatility as an 

institution approaches insolvency.11 As demonstrated, return volatility rises steadily from 2.47 

in quarter 16 to 13.87 percent in quarter 1. Clearly, volatility represents an effective univariate 

gauge of default risk similar to the other risk variable, the ME/BE variable. 

Table 2 performs a sensitivity analysis of the aggregate value-weighted excess returns 

 

10 Bank and thrift industry indexes were created from approximately 2,200 institutions that could be identified on 

the CRSP and tied back to their specific charter. Separate value-weighted indexes were created for banks and thrifts 

using the CRSP utility for creating value-weighted indexes (DSXPORT). At the beginning of each year, the sample 

of banks or thrifts was set, then the index calculated for one year. The final index combined the yearly indexes into

a continuous long-term series. 11 French, Schwert, and Stambaugh (1987) lend empirical support to this view by documenting a positive relation 

 10
reported in table 1. The first cut breaks returns by two measures of institution size: total assets 

and trading volume. To the extent that larger-sized, more heavily traded firms are better known 

and more closely monitored by market analysts, investors, and bank regulators than smaller, less 

actively traded organizations, it is anticipated that the market would be more sensitive to changes 

in their idiosyncratic risk and financial condition. This should be reflected in market returns and 

trading volume as firms approach insolvency. 

In both cases, the market seems to be aware of changes in financial condition across all 

size classes several years before insolvency, but the trends are slightly stronger for the smallest 

institutions. For example, returns for institutions with assets below $1 billion are negative and 

significant in almost all 16 quarters before failure, while almost all of the returns are statistically 

significant for the $1 billion to $5 billion class and slightly less significant for the over-$5 billion 

class. A similar pattern of return persistence in smaller firms can be seen in the three columns of

excess returns split by trading volume. Apparently, local knowledge about changes in financial 

condition spreads quickly through the grapevine, which may account for some of these effects. 

The last four columns of table 2 distinguish the excess returns of low versus high ME/BE 

ratios and bank versus thrift charters. In both cases, excess returns appear very similar to the 

aggregate excess returns shown in table 1. That is, returns are always negative, they tend to be 

stronger for the smaller-sized firms, they are about the same for both banks and thrifts, the 

highest negative values tend to be relatively close to failure, and, in most cases, the returns are 

statistically significant at a high level of significance. The sample line at the bottom of table 2 

shows that the average number of institutions in the 16 quarters of univariate analysis was 42 

banks and 48 thrifts.

 

between the volatility of market returns and market excess returns (market return minus T-bill spread). 

 11
Table 3 examines the pre-failure patterns of other market variables as they relate to 

trading volume. Wang (1994) ties trading volume to the flow of information about a firm’s 

financial health, suggesting that trading volume should rise as information about financial 

distress is released.12 Interestingly, of the three variables, none exhibits a consistently rising 

trend anticipated by pre-failure behavioral theories, although some of the variables become more 

active immediately before failure. For example, two direct measures of trading activity, daily 

trading volume and the standard deviation of daily trading volume, remain largely unchanged 

throughout most the four years preceding failure but establish a trend with four quarters to 

failure. A third measure of trading activity, turnover, is calculated as the percentage of the 

number of shares traded in a quarter divided by the number of shares outstanding at the end of 

the quarter. This variable also remains largely unchanged during the two years preceding failure, 

with a modest downward trend over a three- to four-year period preceding failure. Thus, it 

appears that univariate measures of trading activity fail to follow the upward trend preceding 

failure anticipated by recent theories, even though this period is marked by rapidly changing 

private information and steadily falling stock prices. 

Following Chen, Hong, and Stein (2000), we calculated another market return variable, 

called “skewness.” It is measured as the negative of a standard measure of skewness, calculated 

as the third moment of daily returns divided by the standard deviation of daily returns raised to 

the third power. That is, the n daily log price differences described earlier for quarterly samples. 

 Skewness is measured by the following equation: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ − − ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = − − ∑ ∑ = =

3/ 2

1

2

1

3/ 2 3 ( 1) ( 1)( 2)

n

t

it

n

t

it SKEWi n n r n n r (2) 

 

12 Gallant, Rossi, and Tauchen (1992) find supportive empirical evidence that large daily price movements are 

 12
 

The skewness variable reported in table 3 fails to exhibit a consistent failure-related 

trend. For example, SKEWi moves erratically but shows some tendency to be higher in the 

fourth and first years before failure, but lower in the second and third years before failure. This 

pattern does not appear closely related to either the featureless trading volume series in table 3 or 

the steadily declining price and return series shown in tables 1 and 2. 

In summary, the univariate analysis of the market-related variables in tables 1-3 finds a 

few patterns that illustrate long-term trends preceding failure. Equity price, excess returns, 

market-to-book equity, and return volatility variables in table 1 exhibit the most distinct trends as 

the banks in the sample approach failure. 

The Predictive Content of Market versus Financial Variables 

In the multivariate tests, we proceed by specifying a traditional bank failure-prediction 

model from Call Report financial data. We then alter this model by incorporating equity market 

variables along with the financial data. The traditional model is a bivariate discrete choice 

model using logistic regression to explain the fail/no-fail binary independent variable, FAIL, at 

4, 8, and 12 quarters preceding failure. The one- to three-year period preceding failure is 

somewhat longer than the one- to two-year periods found in several earlier studies because we 

are especially interested in identifying market patterns before changes in financial ratios, which 

are already widely used for predictive purposes. In addition, analyzing this extended time before 

failure will test the forward-looking nature of market information to determine whether it can 

assist failure prediction. This approach also helps address Shumway’s (2001) concern that data 

 

followed by high trading volume. 

 13
for extended periods preceding failure need to be included in traditional failure-prediction 

models. 

As previously noted, the sample of failed banks for the logistic regression differs slightly 

from that used in the univariate analysis because of data constraints. The sample for the logistic 

regression contains 86 publicly traded institutions that failed during the 1989 to 1995 period and 

for which complete Call Report and CRSP data were available 4 quarters preceding failure. For 

the 8 and 12 quarters before failure regressions, the sample contains 85 and 63 failed banks, 

respectively. For each period, the failed institutions are matched at 4, 8, and 12 quarters before 

failure to a similar number of nonfailed institutions containing similar total assets and complete 

Call Report and CRSP data for the same periods as the failed banks. The control sample of 

banks consisted of either unit banks or one-bank holding companies, and all were publicly traded 

institutions. They were matched on the basis of asset size, and the most actively traded nonfailed 

banks were ultimately selected for the sample. The control sample was required to have 

CAMELS ratings of 1 or 2 for two years before the date of failure of the matched bank and for 

one year afterward. This requirement ensures the continuity of a healthy control sample. The 

market variables for each quarter are matched with Call Report financial data from the preceding 

quarter in an effort to synchronize the public release of the Call Report data with the movement 

of the market variables.13

The variable definitions and statistics are presented in Table 4. The first 

independent variable, INSBIF, controls for charter type by assigning a value of unity to 

institutions that are insured by the Bank Insurance Fund (BIF), and a value of zero to 

 

13 Typically, Call Report financial data are not available to the public for 65 days after the end of the quarter. Thus, 

as mentioned, market data for a quarter are matched with Call Report data from the previous quarter to account for 

this reporting lag.

 14
institutions insured by the Savings Association Insurance Fund. This variable is 

specified because FIRREA provided funds in 1989 to deal with a backlog of troubled 

thrifts. The INSBIF mean value equals 0.37, signifying that BIF-insured banks represent 

37 percent of our sample of failed banks, whereas thrifts represent 63 percent. This 

variable is used to separate these institutions and to account for the relatively high failure 

rates that thrifts and later banks experienced in the post-FIRREA period. The coefficient 

for the “charter” dummy has no expected value in the failure prediction models for this 

paper because neither banks nor thrifts will tend to have higher failure probabilities, 

given similar financial ratios and asset sizes. Moreover, the role of the charter dummy is 

ambiguous in models with market data because market efficiency suggests that market 

assessments of the likelihood of failure should not be systematically biased by charter or 

other industry indexes.

A traditional failure-prediction model is specified with financial ratios from the Call 

Reports (table 4).14 The first variable, the book equity-to-assets ratio, EQ_AS, is a standard 

capital ratio expected to vary inversely with failure. An asset-quality variable, NC_RES, 

measures credit risk as the difference between noncurrent (delinquent) assets and loss reserves, 

divided by total assets, and is expected to be positively related to the likelihood of failure. Loan 

provisions to total assets, PROV_AS, could be positively or negatively correlated with the 

likelihood of failure, depending on the period examined. For example, for the year before bank 

failure, a negative relationship could result because banks in serious financial straits may be 

unable to set aside earnings (most such banks are experiencing losses). Conversely, two or three 

 

14 Several size variables were also tried, but none appeared to have a material effect on the reported results. The 

limited effect of size can be seen in subsequent specifications that include the market value of equity. To avoid 

 15
years before failure, banks may be provisioning for possible loan losses in an effort to replenish 

reserves and restore capital in order to remain solvent and satisfy regulatory scrutiny. In this 

instance, loan provisioning may be positively related to the likelihood of failure. A common 

return-on-assets variable, ROA, is expected to have a negative coefficient. A securities-to-assets 

ratio, SC_AS, is a liquidity variable with an expected negative sign. A second measure of 

liquidity, volatile liabilities divided by assets, VOL_AS, has an expected positive sign because 

of an association between funding problems and higher levels of volatile liabilities. 

The first group of independent market variables consists of widely used, or “core,” 

market variables. Obvious starting points are the excess market price, EXPRC, and excess 

return, EXRET, variables as defined in table 4. The DIV variable measures dividend policy 

before failure, with a dummy variable that equals unity for periods when dividends are paid, and 

zero otherwise.15 As mentioned, typically dividends are postponed during times of distress, 

often with the encouragement of regulators.16 We expect all three core variables to be negatively 

related to failure. 

The second set of market variables measures risk with the use of the Fama and French 

standard market model, which contains the natural logarithm of market capitalization (LN_ME) 

and market equity divided by book equity (ME_BE). These two variables are expected to have 

negative signs. The option theoretic variable, the standard deviation of returns (SDRET) as cited 

by Merton (1974), is also included as an additional risk measure. Since risk rises as failure 

 

conflicting size effects, we elected to omit a size variable from our traditional Call Report model. 15 We define DIV as a market-related variable because the dividend amounts that the banks distributed were 

obtained with the other market data from CRSP. However, data on bank dividends paid on capital stock are 

available from the Call Report. 16 Bank regulators generally insist on the postponement of dividend payments when institutions enter a “problembank” stage, which is typically at a CAMELS rating of 4 or 5, although it can occur earlier. 

 16
approaches, the coefficient for the SDRET variable are expected to be positively related to 

failure. 

The remaining market variables are selected to test for other patterns or trends before 

failure. Two trading activity variables from table 3, trading volume and turnover, are very 

similar measures of the same effect, so we use only the turnover measure (TURN) in our 

published tests, and report here that the choice has not affected the results. A related measure, 

the standard deviation of volume (SDVOL), is used alongside TURN because the volatility of 

trading volume is one step removed from direct measures of trading activity. The signs of the 

two trading activity variables, TURN and SDVOL, are predicted to be positive. 

In summary, the logistic regressions are organized as follows: (3) 

 

 

∑ ∑

∑ ∑

=

− −

=

−

=

− −

=

−

+ +

Φ = + + + +

15

14

, ,

13

11

,

10

8

, 1

7

2

1 ,

( ) ( )

( ) ( Re ) ( )

j

i t k j i t k it

j

j

i t k

j

i t k j

j

i i t k j

RiskVariables OtherMarketVariables

Charter Call portVariables CoremarketVariables

β β ε

α β β β

where Φi represents the failed and nonfailed groups; t= quarter of failure; k= 4, 8, and 12 quarters 

before failure; β1 controls for charter type; β2,... β7 are a vector of variables for the traditional 

failure prediction model, based on Call Report financial variables; β8.....β10 are a vector of 

variables that measure the impact of core market variables; β11...β13 are the coefficients that 

control for risk effects; and β14...β15 take account of other market variables. We report here that 

correlation between the independent variables did not appear to be a problem.

17

 

17 The correlation coefficients fell at acceptable levels except for SC_AS and VL_AS which fell between –0.81 and 

–0.83, and PROV_AS and ROA which ranged from 0.77 to 0.78 depending on the sample. However, since 

eliminating these variables had no meaningful effect on the reported results, both variables are included in the 

reported regressions. 

 17
The regression results are shown in table 5. Specifications 1 through 6 initialize two 

failure-prediction models, one focused on Call Report financial data and the other on marketrelated variables, with controls in both for charter type. In specification 1, the performance of 

the Call Report financial ratios is generally consistent with expectations, as five Call Report 

coefficients are significant at the 10 percent or higher level, and all but the PROV_AS variable 

have their anticipated signs. The unanticipated negative and significant sign for the PROV_AS 

variable could be explained by the inability of a bank, which fails within a year, to provision for 

loan losses because in all likelihood, the bank is reporting earnings losses during this period. In 

specification 2, the coefficients for all three core market variables are statistically significant at 

the 1 percent level. However, the explanatory power of specification 2 is less than that of the 

Call Report model as measured by pseudo- R 2

 ( R 2) and the Akaike Information Criterion (AIC), 

so the core market model has less explanatory power than the traditional financial ratios.18

Specifications 3 through 5 incorporate the risk and other market variables into the core 

market variable regression of specification 2. The comparison begins in specification 3, where 

the risk variables are added to the core market model of specification 2. In this case, only the 

risk variable, SDRET, becomes significant. The explanatory power of the regression shows a 

marginal improvement from specification 2. However, comparison of specifications 2 and 3, 

through a likelihood ratio test (LRT) using a chi-square statistic, finds that the risk variables 

significantly improve the performance of the core market variable model at the 5 percent level. 

Specification 4 combines the core market model with the other market-related variables 

to find that the addition of these variables slightly improves the core model from specification 2. 

 Three of the core market variables have statistically significant coefficients. In contrast, only 

 

18 The lower the value for the AIC variable, the better the fit of the model. 

 18
one of the other market variables is statistically significant (TURN). An LRT statistic significant 

at the 1 percent level confirms the importance of adding the other market variables to the core 

model in specification 2, while the R 2

 shows a slightly improved explanatory power of 

specification 4. 

Specification 5 combines the performance of all the market variables into a single model. 

 The results show that four of the eight market variables are significant at the 1 percent to 10 

percent level. In this case, the LRT test finds that the regression represents again a slight 

improvement over the core market model in specification 2, with a fit that is statistically 

significant at the 1 percent level. This is supported by a higher a R 2

 and a lower AIC value. 

Specification 6 incorporates the benchmark Call Report model in specification 1 and 

combines the best-performing market variables from specification 5. In this case, an LRT finds 

that the regression represents a significant improvement over the initial Call Report model as 

shown in specification 1. The combined model in specification 6 exhibits a lower AIC value and 

higher R 2

 relative to specification 1 and is statistically significant at the 5 percent level, 

demonstrating that selected market variables do add value to the basic Call Report model—

despite the surprising observation that only two of the Call Report variables are statistically 

significant, and none of the market variables. However, the market variables do have the correct 

signs and contribute significantly to the model, as revealed by the LRT test. In summary, the 

market variables do provide some explanatory power to accounting data in the identification of 

bank failures. 

Table 6 contains in- and out-of sample tests for the regressions in table 4, where the 

critical probability is set at 50 percent because of the use of a matched sample of failed and 

nonfailed institutions. The bootstrap procedure as suggested by Efron and Tibshirani (1998) and 

 19
Freedman and Peters (1984) is used to estimate the in-sample and out-of-sample classification 

accuracy for each of the six logistic regressions performed in the table. With the bootstrap 

technique, 120 separate iterations were performed, with each iteration composed of a unique 

sample of failed and nonfailed banks and thrifts. The results in table 6 represent averages of 

those iterations. 

The in-sample tests show that market data incrementally improve the identification of

failed and nonfailed institutions as we pass from Call Report data in equation 1 to the combined 

model in specification 6. The correctly identified failed institutions increase from an already 

high 97 percent to 99 percent—or (in terms of the number of institutions) from 53 to about 55, or 

a modest 3 percent increase. The correct identification of banks that survived also increases 

slightly. 

The out-of-sample tests show larger but insignificant increases in the correct 

classifications of failed and nonfailed institutions when equity market data are added to the Call 

Report model. In percentage terms, the predictive accuracy of the Call Report model in 

specification 1 (94 percent) is higher than the accuracy of the models that incorporated market 

data (specifications 2 through 5) but is below the combined model in specification 6 (95 percent); 

in terms of the average number of banks correctly identified, this increases from 29.13 to 29.46 

banks for specifications 1 and 6, respectively. While the change is not large, it should be noted 

that the average resolution cost to the FDIC of the failed banks in this study was $220.3 million. 

Thus, even if the model identified only one additional failed bank by incorporating market data, it 

would be worth it. In addition, as with the in-sample test, the correct prediction of surviving 

banks also increased slightly. 

Table 7 repeats the primary regressions of table 5 (specifications 1, 2, 5, 6) and finds 

 20
similar results, but for 8 and 12 quarters preceding failure. Specifications 1 and 5 in table 7 show 

the Call Report variables generally robust with respect to explaining failure two and three years 

preceding the event. In these regressions most of the Call Report variables perform as expected, 

and many are significant at high levels. Specifications 2 and 3 in the 8-quarter model and 6 and 7 

in the 12-quarter model find that the market variables by themselves contribute less to the 

identification of failed institutions than does the benchmark accounting model. However, the 

LRT test finds that the addition of the market variables to the Call Report variables is statistically 

significant at the 1 percent level in both specification 4 and 8. As was the case in table 5, the 

addition of market variables significantly improves the performance of the traditional bank failure 

model. 

Conclusions 

This paper explores the notion that market-related variables may be used to augment 

financial ratios for the purpose of predicting bank failures over the 1989–1995 period. 

Univariate analysis documents distinct patterns of declining prices, negative returns, rising return 

volatility, declining dividends and falling market-to-book equity ratios for several years before 

failure. However, no clear trend emerges for several other market measures, such as trading 

volume and return skewness. 

Multivariate tests examine and support the ability of market-related variables to improve 

the failure-predictive content of more traditional models, which are based on Call Report 

financial ratios. The inclusion of equity market variables such as stock price, return, and 

volatility of returns significantly improves the ability of the model to identify institutions that 

failed over the 1989–1995 period for up to three years before the failed event. The in-sample 

 21
and out-of-sample classifications show that the simultaneous use of equity market data and Call 

Report variables does generally yield larger numbers of failed and nonfailed banks being 

correctly predicted. The empirical results of this paper support the use of forward-looking equity 

market variables to complement the Call Report financial variables to produce a more accurate 

failure-prediction model. In this regard, relatively simple measures of market activity, such as 

price, excess return, and volatility of returns, appear to capture the primary failure-predictive 

content of observable market activity. 

 22
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 28
CRSP CRSP Industry

Cum. Eq. Wt. Va. Wt. Va. Wt. Dividend St. Dev.

Qtrs. Stock Qtrly. Excess Excess Excess Paid Market Daily

To Price Return Return Return Return in Qtr. Capital Return

Failure Sample ($) (%) (%) (%) (%) Dummy ($000s) ME/BE (%)

16 75 10.64 0.00 -7.80 -8.02 -5.03 0.40 28,323 0.86 2.47

(0.00) (4.04) *** (4.06) *** (2.57) **

15 81 10.42 -2.38 -8.30 -8.05 -7.40 0.36 30,136 0.81 2.66

(1.00) (3.68) *** (3.61) *** (3.33) ***

14 85 9.77 -9.26 -11.21 -11.20 -10.31 0.38 25,995 0.79 2.89

(4.41) *** (6.45) *** (6.10) *** (5.61) ***

13 89 9.44 -2.95 -8.34 -6.93 -7.62 0.34 25,485 0.78 2.82

(1.23) (4.16) *** (3.15) *** (3.58) ***

12 90 8.28 -4.48 -9.91 -7.43 -9.40 0.36 24,153 0.70 3.20

(1.73) * (4.76) *** (3.26) *** (4.50) ***

11 92 8.06 -3.60 -14.133 -11.25 -10.23 0.36 21,874 0.64 3.32

(1.76) * (7.42) *** (5.73) *** (5.41) ***

10 94 6.61 -9.92 -13.225 -12.63 -12.77 0.31 19,831 0.61 3.84

(3.65) *** (5.22) *** (4.87) *** (4.99) ***

9 97 6.08 -11.39 -16.173 -13.14 -11.60 0.31 15,303 0.57 4.00

(3.99) *** (6.83) *** (5.22) *** (4.98) ***

8 97 5.04 -15.28 -20.298 -18.30 -16.16 0.28 13,560 0.54 4.30

(4.85) *** (7.42) *** (6.41) *** (5.66) ***

7 97 4.09 -10.00 -15.244 -14.17 -14.29 0.22 12,585 0.53 4.45

(2.35) ** (4.01) *** (3.52) *** (3.69) ***

6 99 3.61 -19.64 -21.16 -21.40 -18.49 0.18 10,456 0.44 5.01

(8.07) *** (9.06) *** (8.89) *** (7.76) ***

5 99 2.68 -23.48 -23.614 -23.34 -21.76 0.15 8,221 0.42 7.47

(4.95) *** (5.78) *** (5.31) *** (5.17) ***

4 99 1.81 -31.25 -35.251 -30.69 -29.31 0.09 6,919 0.33 8.25

(6.53) *** (8.27) *** (6.66) *** (6.62) ***

3 95 1.40 -28.57 -36.745 -30.56 -31.40 0.03 5,318 0.25 9.52

(5.21) *** (7.23) *** (5.59) *** (5.94) ***

2 86 0.97 -33.33 -36.243 -35.41 -37.30 0.02 3,818 0.24 12.06

(5.63) *** (6.48) *** (5.97) *** (6.49) ***

1 75 0.77 -39.13 -47.782 -40.55 -42.49 0.00 2,584 0.19 13.87

(4.24) *** (5.58) *** (4.44) *** (4.78) ***

Table 1

Stock Return Characteristics 16 Quarters Prior to Bank Failure

The stock price, excess return, and other variables reported on the 16 quarter-to-failure lines are calculated as simple

averages from the individual sample values. The stock price and market capital (stock price times shares 

Call Report value of the equity, where both are measured as of end-of-quarter values. T-statistics are shown in 

parenthesis below the quarterly average return percentages. A single, double or triple "*" indicates significance

at the 10%, 5% and 1% percent levels respectively

outstanding) variables are averaged over all trading days in each quarter. All excess returns are calculated as the 

difference between the cumulative quarterly return of each stock and the cumulative quarterly return of the index. 

The "Dividend Paid in Qtr. Dummy" variable equals unity if an institution pays a dividend in the current quarter, 

and 0 otherwise. ME/BE is the market-to-book equity ratio, calculated as the market value of equity divided by the

 29
Daily Daily Daily

Qtrs. Trading Trading Trading

To Assets Assets Assets Volume Volume Volume ME/BE ME/BE

Failure <= $1b $1 - 5b > $5b <= 10,000 10-30,000 > 30,000 <= 1.0 > 1.0 Banks Thrifts

16 -7.53 *** -9.49 ** -3.67 -8.61 *** -6.18 -3.67 -8.54 *** -3.82 -6.38 *** -8.96 ***

(3.11) (2.08) (1.50) (3.60) (1.43) (0.66) (3.21) (1.31) (2.83) (2.78)

15 -8.04 *** -8.38 * -7.57 ** -8.93 *** -3.60 -7.57 -8.37 *** -7.36 * -7.57 ** -8.89 ***

(2.66) (1.93) (1.98) (3.51) (0.78) (1.04) (3.37) (1.71) (2.34) (2.87)

14 -9.23 *** -14.94 *** -5.62 -9.51 *** -14.94 *** -8.07 -11.99 *** -9.54 ** -8.98 *** -13.06 ***

(4.21) (3.80) (1.08) (4.05) (4.93) (1.54) (5.97) (2.47) (3.86) (4.65)

13 -7.84 *** -5.47 -3.12 -7.84 *** -5.91 -3.12 -7.29 ** -6.72 *** -7.70 *** -5.69

(3.05) (1.12) (0.48) (2.86) (1.25) (0.51) (2.44) (3.02) (3.21) (1.60)

12 -5.89 ** -12.27 ** -14.76 ** -5.89 ** -14.77 *** -12.77 ** -7.48 ** -6.40 ** -7.56 ** -7.34 **

(2.55) (2.09) (2.14) (2.13) (3.80) (2.15) (2.55) (2.07) (2.23) (2.38)

11 -12.03 *** -9.35 ** -10.59 *** -12.27 *** -13.50 *** -2.49 -13.82 *** -5.19 * -14.59 *** -8.82 ***

(4.69) (2.16) (3.41) (5.13) (4.90) (0.40) (5.90) (1.66) (5.54) (3.24)

10 -10.82 *** -14.70 ** -18.85 *** -12.63 *** -5.16 -23.64 *** -14.20 *** -7.73 * -14.17 *** -12.27 ***

(4.59) (2.03) (2.81) (3.79) (1.06) (3.84) (4.53) (1.81) (4.44) (3.16)

9 -13.59 *** -10.79 -23.52 *** -12.79 *** -15.13 *** -11.69 -13.38 *** -12.86 *** -13.19 *** -13.14 ***

(5.41) (1.57) (2.74) (4.64) (4.62) (1.22) (4.57) (2.84) (3.30) (4.06)

8 -15.59 *** -20.51 *** -20.85 *** -17.38 *** -46.19 -26.43 *** -20.25 *** -6.48 -24.47 *** -13.01 ***

(4.74) (2.93) (2.79) (4.88) (0.06) (5.09) (6.34) (1.06) (6.31) (3.31)

7 -15.63 *** -14.17 -11.57 -16.26 *** -10.07 5.23 -15.63 *** -8.68 * -14.37 ** -14.17 ***

(6.25) (1.06) (0.91) (4.09) (0.66) (0.48) (3.40) (1.75) (2.31) (2.67)

6 -18.27 *** -26.60 *** -25.53 *** -14.94 *** -34.22 *** -31.89 *** -25.11 *** -8.58 * -28.92 *** -15.83 ***

(5.84) (5.20) (5.72) (5.02) (5.65) (9.87) (9.59) (1.86) (9.23) (4.67)

5 -19.64 *** -33.44 *** -26.52 *** -18.34 *** -33.52 *** -26.33 *** -23.43 *** -8.45 -23.58 *** -20.70 ***

(3.03) (5.92) (3.50) (3.05) (5.70) (3.87) (4.98) (0.88) (5.05) (2.92)

4 -30.69 *** -31.27 *** -28.92 ** -27.76 *** -45.26 *** -38.89 ** -34.51 *** -22.41 *** -33.91 *** -28.41 ***

(5.56) (3.12) (2.34) (5.40) (5.52) (2.05) (6.75) (3.17) (6.01) (4.03)

3 -34.58 *** -31.33 ** -20.11 * -30.48 *** -39.53 *** -17.60 -35.74 *** -24.26 -28.15 *** -40.85 ***

(6.65) (2.12) (1.68) (4.58) (3.21) (1.11) (6.18) (1.40) (4.06) (4.89)

2 -31.87 *** -39.63 *** -26.56 ** -29.79 *** -50.66 *** -38.89 *** -34.54 *** -46.11 *** -39.63 *** -33.66 ***

(3.44) (7.60) (1.98) (3.41) (8.26) (3.01) (5.25) (4.74) (4.16) (4.62)

1 -36.56 *** -53.66 *** -42.33 -39.20 *** -52.09 ** -55.72 ** -44.66 *** -33.01 ** -43.85 *** -39.20 ***

(2.75) (4.47) (1.40) (3.89) (2.22) (2.12) (4.23) (2.04) (2.99) (3.50)

Avg. Sample 56.00 25.06 9.38 57.81 17.88 14.75 72.25 18.19 42.06 48.38

Table 2

Breakout of Value Weighted Excess Returns 16 Quarters Prior to Bank Failure (%)

The stock price, excess return, and other reported variables are calculated as simple averages from the individual sample values. All returns are

expressed as percentages, and calculated as the difference between the cumulative quarterly return of each stock and cumulative quarterly

shown in parentiesis below the quarterly average return percentages. A single, double or triple "*" indiciates significance at the 10%, 5%

and 1 % levels respectively.

return of the value weighted CRSP index. Asset size is determined by total assets, as reported on the Call Reports. Trading volume is the

average of all daily trading volumes reported throughout the quarter on the CRSP tapes. ME/BE is the market-to-book equity ratio,

calculated as the market value of equity divided by the Call Report book value of equity, where both are measured as end-of-quarter values.

Institutions are classified as banks (thrifts) if they are insured by the bank insurance fund (savings asociation insurance fund). T-statistics are

 30
Table 3

Other Market Variable Trends 16 Quarters Prior to Bank Failure 

The stock price, excess return, and other variables reported on the 16 quarter-to-failure 

lines are calculated as simple averages from the individual sample values. The daily trading 

volume and standard deviation of trading volume are the simple mean and standard 

deviation statistics calculated across the daily trading activity of each quarter. Turnover 

is the number of times each share is traded in a quarter. Skewness is calculated from 

equation 2 in the text. 

St. Dev.

Qtrs. Daily Daily

To Trading Trading Return

Failure Sample Volume Volume Turnover Skewness

16 75 6,386 10,899 0.13 0.30

15 81 5,636 11,105 0.13 0.30

14 85 6,657 9,307 0.15 0.29

13 89 6,241 8,020 0.12 0.39

12 90 4,630 7,338 0.12 0.24

11 92 5,589 7,790 0.13 0.16

10 94 5,239 7,994 0.13 0.19

9 97 4,595 7,544 0.11 0.18

8 97 5,787 8,261 0.12 0.22

7 97 5,076 7,493 0.10 0.31

6 99 4,227 8,317 0.10 0.28

5 99 3,967 7,339 0.09 0.23

4 99 4,537 8,028 0.09 0.34

3 95 5,212 10,264 0.09 0.28

2 86 6,844 12,222 0.12 0.46

1 75 6,959 12,495 0.10 0.50

 31
Standard Standard

Mean Deviation Mean Deviation

Dependent Variable

FAIL Dummy variable equal to 1 if the institutionn failed, and 0 otherwise.

Charter

INSBIF Dummy variable equal to 1 if the institution is associated with the 0.372 0.486 0.919 0.275

the Bank Insurance Fund, and 0 if it is associated with the 

Savings Association Insurance Fund.

Call Report Variables

EQAP Book value of equity divided by total assets (%). 2.540 2.676 11.601 13.864

NC_RES Non-Current (delinquent) assets, less loan-loss reserves, divided 4.497 4.039 0.271 0.859

by total assets (%).

ROA Year-to-date annualized earnings, divided by total assets (%). -2.701 3.682 0.984 0.737

SC_AS Securities divided by total assets (%). 17.911 14.207 27.110 16.071

PROV_AS Year-to-date annualized Loan-loss Provisions, divided by total 1.954 2.609 0.333 0.446

 assets (%).

VL_AS Volatile liabilities divided by total assets (%). 25.524 12.018 16.990 10.731

Core Market Variables

EXPRC Market excess price, calculated as the natural logarithm of the -7.410 1.044 -5.142 1.042

EXRET Market excess return, calculated as the difference between -0.229 0.479 -0.003 0.188

the cumulative quarterly return of each stock and the 

cumulative quarterly return of the CRSP value weighted index.

DIV Dummy variable equal to 1 if a dividend is paid during 0.093 0.292 0.860 0.349

the quarter, and 0 otherwise. 

Risk Variables

LN_ME Natural logarithm of market capitalization. 8.633 1.272 11.136 1.300

ME_BE Market capitalization divided by book equity. 0.762 3.102 1.395 1.119

SDRET Standard deviation of daily returns during the quarter. 0.105 0.071 0.027 0.016

Other Market Variables

SDVOL Standard deviation of trading volume (rescaled by 1,000,000). 0.019 0.027 0.012 0.044

TURN Number of shares traded in a quarter divided by the number 14.806 15.026 8.170 15.726

of shares outstanding at the end of the quarter (%). 

1

Data are for 4 quarters prior to failure. 

Control Sample

average quarterly stock price and the Wilshire 500 stock index.

Failed Sample

Table 4

Variable Definitions, Means, and Standard Deviations1

 32
Independent Anticipated Specification

Variable Sign 1 2 3 4 5 6

Intercept + 0.52 -9.69 -1.05 -11.51 -10.85 -9.43

(0.43) (3.03) *** (0.17) (2.91) *** (1.22) (1.14)

Charter

INSBIF -5.62 -2.86 -3.44 -3.76 -3.94 -4.88

(2.83) *** (3.68) *** (3.55) *** (3.73) *** (3.62) *** (1.82) *

Call Report Variables

EQ_AS(1) - N/A N/A

NC_RES + 1.63 1.69

(2.98) *** (1.75) **

ROA - -2.98 -1.54

(2.71) *** (0.96)

SC_AS - -0.14 -0.14

(2.64) *** (1.64)

PROV_AS + -2.06 -0.45

(1.70) * (0.24)

VL_AS + 0.21 0.29

(2.49) ** (1.87) *

Core Market Variables

EXPRC - -1.98 -0.78 -2.33 -1.57 -1.11

(3.99) *** (1.53) (3.69) *** (1.98) * (1.24)

EXRET - -2.69 -3.96 -2.92 -3.80 -2.57

(3.17) *** (2.61) *** (3.00) *** (2.47) ** (0.96)

DIV - -2.26 -2.78 -2.73 -3.02 -2.77

(2.97) *** (2.93) *** (2.72) *** (2.69) *** (1.44)

Risk Variables

LN_ME - -0.30 0.26

(0.74) (0.49)

ME_BE - 0.15 0.20

(0.44) (0.50)

SDRET (1) + 48.21 37.74 N/A

(2.79) *** (1.90) *

Other Market Variables

SDVOL + -23.41 -25.15

(1.45) (1.18)

TURN + 0.06 0.05

(2.42) ** (1.62)

Pseudo R2 0.70 0.65 0.66 0.67 0.67 0.71

Akaike Information Criterion (AIC) 39.99 65.48 60.52 58.50 59.59 35.58

χ2 (relative to specification 1) N/A N/A N/A N/A N/A 10.42 **

degrees of freedom 3

χ2 (relative to specification 2) N/A N/A 10.96 ** 10.98 *** 15.90 *** 39.90 ***

degrees of freedom 3 2 5 5

NOTE: (1) EQ_AS and SDRET cause frequent complete separations between failed and nonfailed banks in specified regressions and are labelled as 'N/A'.

their corresponding coefficients. A single, double, or triple "*" indicates significance at the 10%,5% and 1% levels respectively.

Table 5 

Logit Regression Results: 4 Quarters Before Failure

This table performs logit regression analysis on a sample of 86 banks and thrifts and an equal number of institutions

in the control sample. All independent variables are defined in Table 4. T-statistics are shown in parentheses below

 33
Predict Survival, Predict Failure,

Equation Correctly Predict But Fails Correctly Predict But Survives

Specification Failure (Type 1 Error) Survival (Type 2 Error)

(%) N (%) N (%) N (%) N

 In-Sample Classification

1 96.95 53.33 3.05 1.68 97.83 53.81 2.17 1.19

2 95.12 52.32 4.88 2.68 95.80 52.69 4.20 2.31

3 95.42 52.48 4.58 2.52 96.08 52.84 3.92 2.16

4 97.05 53.38 2.96 1.63 97.12 53.42 2.88 1.58

5 97.12 53.42 2.88 1.58 96.91 53.30 3.09 1.70

6 99.61 54.78 0.39 0.22 99.41 54.68 0.59 0.33

 Out-of-Sample Classification

1 93.98 29.13 6.02 1.87 96.52 29.92 3.48 1.08

2 92.55 28.69 7.45 2.31 95.11 29.48 4.89 1.52

3 89.73 27.82 10.27 3.18 94.57 29.32 5.43 1.68

4 92.66 28.72 7.34 2.28 95.22 29.52 4.78 1.48

5 88.81 27.53 11.19 3.47 94.32 29.24 5.68 1.76

6 95.03 29.46 4.97 1.54 97.04 30.08 2.96 0.92

Table 6

 Failure Prediction Accuracy and Error Analysis: 4 Quarters Before Failure

This table shows the prediction accuracy of the 6 bootstrapped regressions that mimic the regressions performed 

in Table 5. The bootstrap procedure (Efron and Tibshirani, 1998; Freedman and Peters, 1984) is used to estimate the

applied to a sample of 62 banks and thrifts (31 failed and 31 non-failed) held in the out-of-sample group. The critical

probability used to determine failure is 50 percent.

in-sample and out-of-sample classification accuracy of the 6 logistic regressions performed in Table 5. With this

bootstrap technique, 120 separate repetitions were performed where each repetition was composed of a unique sample

of failed and non-failed banks. The top half of this table shows the predictive accuracy of these regresions applied to a 

sample of 110 (55 failed and 55 non-failed) institutions. The botton half of the table shows the accuracy of the regressions

 34
Specification Independent Anticipated 8 quarters before Failure 12 quarters before Failure Variable Sign 1 2 3 4 5 6 7 8

Intercept + 4.78 -9.76 1.84 -26.48 4.50 -5.51 14.50 28.46

(2.89) *** (3.71) *** (0.31) (1.15) (2.38) ** (1.99) ** (2.35) ** (3.30) ***

Charter

INSBIF -2.9596 -2.7291 -2.7706 -4.7696 -4.8552 -1.95521 -2.268 -5.8108

(3.07) *** (4.71) *** (4.19) *** (2.62) *** (3.69) *** (3.02) *** (2.64) *** (2.75) ***

Call Report Variables

EQAP - -0.13 -0.28 -0.08 -0.03

(1.14) (1.81) * (2.31) ** (0.55)

NC_RES + 0.32 -0.06 0.95 1.98

(1.05) (0.19) (2.24) ** (2.33) **

ROA - -2.78 -0.50 -0.31 1.04

(2.82) *** (0.43) (0.74) (1.56)

SC_AS - -0.10 -0.23 -0.14 -0.29

(2.90) *** (2.43) ** (3.62) *** (2.81) ***

PROV_AS + 0.11 0.34 1.68 3.41

(0.09) (0.23) (1.27) (1.63)

VL_AS + 0.09 0.20 0.08 0.25

(1.64) (2.09) ** (2.45) ** (2.80) ***

Core Market Variables

EXPRC - -2.09 -1.19 -5.93 -1.39 0.13

(4.57) *** (2.07) ** (2.03) ** (2.96) *** (0.24)

EXRET - -1.25 -1.13 -3.00 -3.26 1.92

(1.03) (0.88) (1.87) * (1.66) * (0.41)

DIV - -1.03 -0.84 -1.39 -1.23 -2.26

(1.92) * (1.34) (2.61) *** (2.05) ** (1.60)

Risk Variables

LN_ME - -0.75 -0.31 -1.15 -2.41

(1.78) * (0.27) (2.86) *** (3.10) ***

ME_BE - -0.93 -0.63

(1.45) (0.84)

SDRET + 26.21 35.37

(1.33) (1.43)

Other Market Variables

SDVOL (1) + N/A N/A

TURN + 0.05 0.08 0.0141

(2.62) *** (2.59) *** (0.87)

R2 0.64 0.54 0.58 0.70 0.58 0.42 0.49 0.66

Akaike Information Criterion(AIC) 72.33 110.10 99.75 45.37 75.43 112.01 101.20 52.46

χ2

 (relative to specification 1) N/A N/A N/A 32.96 *** N/A N/A N/A 28.97 ***

degrees of freedom 3 3

χ2

 (relative to specification 2) N/A N/A 18.34 *** 76.73 *** N/A N/A 18.82 71.55 ***

degrees of freedom 4 8 4 7

NOTE: (1) STDVOL perfectly separates failed and nonfailed banks in particular regressions, and are labelled as 'N/A'.

12 quarters before failure. T-statistics are shown in parentheses below their corresponding regression coefficents. A single, double

or triple "*" indicates significance at the 10%, 5%, and 1% levels, respectively.

Table 7

Logit Regression Results: 8 and 12 Quarters Before Failure

This table extends the logit regressions performed in Table 7, using a sample of 85 failed banks and thrifts and and equal number of institutions 

in a control sample for 8 quarters before failure and a sample of 63 failed banks and thrifts and an equal number of non-failed instituions for 

 35