Troubled Banks: Why Don’t They All Fail?

Robert Oshinsky

Federal Deposit Insurance Corporation 

550 17th Street, NW

Washington, DC 20429

Phone: (202) 898-3813

roshinsky@fdic.gov

Virginia Olin∗

Federal Deposit Insurance Corporation 

550 17th Street, NW

Washington, DC 20429

Phone: (202) 898-8711

volin@fdic.gov

March 2005

Working Paper 2005-03

∗ Robert Oshinsky and Virginia Olin are Senior Financial Economists, Division of Insurance and Research, FDIC. 

The authors thank John O’Keefe for his overall guidance; Andrew Davenport for his counsel; and Jesse Weiher, 

Brian Lamm, James Marino, and the anonymous readers of the FDIC for their careful review of the draft and their 

valuable comments and suggestions. The authors also thank Robert DeYoung of the Federal Reserve Bank of 

Chicago for his suggestions. Of course, all mistakes are the responsibility of the authors. The views expressed here 

are those of the authors and not necessarily those of the Federal Deposit Insurance Corporation.
1

Abstract

In this paper we examine troubled banks—those that receive a poor safety-and-soundness 

rating when examined—in order to predict future bank state. Besides failure, we see three 

alternative outcomes for these banks: recovery, acquisition, or continuation as a problem. The 

determinants of bank failure have been much researched, as has failure prediction. Most of this 

research uses a binary approach, dividing banks into two groups (those that fail and those that do 

not) or predicting one of two states (failure or nonfailure). Because our sample contains only 

troubled banks, we can go beyond a two-state approach.

First we use univariate trend analysis to determine whether financial variables differ 

within this group of banks depending on the banks’ future states. This analysis suggests that 

meaningful relationships exist between these future states and prior-period financial conditions. 

We then use financial ratios as explanatory variables in a unified model of bank states, with the 

goal of improving predictions of future bank condition.

We gauge the model’s effectiveness by testing the out-of-sample forecasting accuracy. 

Our results show that our model compares favorably with the standard binary failure-prediction 

model, yet has the added feature of predicting recovery, merge r, or continuation as a problem 

bank.
2

1. Introduction

A wealth of literature exists examining the determinants of bank failures and bank 

mergers and acquisitions. Also numerous are studies developing failure-prediction models and 

early-warning systems. Because these studies use samples of all banks, most of this research 

focuses on pairs of outcomes: failure versus nonfailure, merger versus acquisition, or problem 

banks versus nonproblem banks.

In this paper, we contribute to this literature by studying only troubled banks—banks that 

receive a composite CAMELS rating of either 4 or 5 when examined.1 Focusing on troubled 

banks is especially valuable for four reasons. First, for troubled banks, failure is but one possible 

outcome. Alternative outcomes include recovery, acquisition, or continuation as a problem.

Second, between 1990 and 2002, 96 percent of all banks that failed had first been troubled banks. 

Third, the number of troubled banks rose in the period 1999 through 2002 but began to decrease 

in 2003. This uptick is especially important to the Federal Deposit Insurance Corporation 

(FDIC), whose mission is to protect depositors and promote the safety and soundness of insured 

depository institutions and the U.S. financial system by identifying, monitoring, and addressing 

risks to the deposit insurance funds. The Financial Risk Committee within the FDIC quantifies 

risks to the deposit insurance system for purposes of financial reporting and fund management.

Each quarter, the committee meets to set a contingent loss reserve estimated from total assets of 

banks that may fail within two years. The ability to better predict the number of troubled banks 

that will not fail would improve the committee’s estimates for the contingent loss reserve.

1 CAMELS is an acronym for the five components of the regulatory rating system: Capital adequacy , Asset quality, 

Management, Earnings, Liquidity, and market Sensitivity. Examiners have rated sensitivity only since 1998. Banks 

with a rating of 4 or 5 are considered problem banks. Four-rated banks generally exhibit unsafe or unsound practices 

or condition, while 5-rated banks exhibit extremely unsafe or unsound practices or condition.
3

Finally, developing a multistate model identifying financial characteristics that contribute 

to recovery as well as to failure is important for the FDIC’s long-term strategic planning.

Accurate predictions of the future states of problem banks would affect the resources applied to 

these banks.

Knowing the future states of banks in our sample, we construct financial profiles for each 

of the four groups. We then use these profiles to develop a multinomial logit estimating 

procedure that predicts the likelihood of a bank’s future state: recovery, acquisition,continuation 

as a problem, or failure.2 We show that a four-state model predicts at least as well as binary 

failure-prediction models and adds predictive ability for alternative future states.

The next section of this study describes previous empirical studies of bank failures, 

mergers, and financial distress. Section 3 discusses our methodology; section 4, our sample and 

data; section 5, our results; and section 6 concludes.

2. Empirical Studies

Studies examining the determinants of bank failures and bank mergers and acquisitions 

are numerous. Also numerous are studies developing early-warning systems predicting 

deterioration in banks’ financial condition.3 Most of the work in this literature constructs 

financ ial ratios taken from the Consolidated Reports of Condition and Income (Call Reports) 

that banks file quarterly with the FDIC. The idea is to construct financial ratios that closely 

resemble the CAMELS rating system used by bank examiners to predict pairs of outcomes:

failure versus nonfailure, mergers versus acquirers, or problem banks versus nonproblem banks. 

(See Whalen [1991]; Cole and Gunther [1998]; Kolari et al. [2002]; and Jagtiani et al. [2003].)

2 Because of the nature of the resolution process, we deliberately omit troubled thrifts, including those resolved by 

the Resolution Trust Corporation, which kept insolvent thrifts open while trying to close them.

3 For reviews of the literature, see Demirgüç-Kunt (1989); Jagtiani et al. (2003); and King et al. (2005).
4

Only a few studies have extended this research beyond pairs of outcomes. In an effort to 

improve predictive accuracy, DeYoung (2003) estimates the long-run probability of failure and 

acquisition in de novo banks by defining three states: (1) failure, (2) merger by acquisition, and

(3) conversion of a whole-bank affiliate of a bank holding company to a branch bank of that 

bank holding company. Wheelock and Wilson (2000) use a competing-risks model to consider 

explicitly the joint determination of the probability of being acquired, failing, or surviving. 

Hannan and Rhoades (1987) predict that a bank may experience one of three outcomes: (1) not 

be acquired, (2) be acquired by a firm operating within its market, or (3) be acquired by a firm 

operating outside its market. DeYoung expects that including the other two exit states (merger 

by acquisition, and conversion) will improve the accuracy of the failure estimates. Wheelock 

and Wilson find that inefficiency increases the risk of failure while reducing the probability of a 

bank’s being acquired. And Hannan and Rhoades find that adding the third state (distinguishing 

between merger types) yields a number of firm and market characteristics that significantly 

influence the likelihood of acquisitions—characteristics that earlier studies did not yield.

We add to this literature in two ways. First, because our sample is troubled banks instead 

of all banks, we can introduce three alternatives to failure: recovery, merge r, or continuation as a 

problem. Second, by adding an additional state to the three-state models above, we hope to 

improve predictive accuracy for failed banks and add predictions for three outcomes that are 

alternatives to failure.
5

3. Methodology

Referencing previous studies, we select certain financial variables prove n to be useful in 

determining future bank state. We use univariate trend analysis to determine whether priorperiod financial characteristics differ by future banks’states.

Prior literature on bank performance suggests a number of reasons for failure: low 

earnings, low liquidity, risky asset portfolios, and poor management, to name a few. Thus we 

select financial variables from the same broad categories used to explain or predict binary bank 

states: capital adequacy, asset quality, management, earnings, and liquidity. In addition, we run 

a one-way analysis of variance to examine the financial characteristics of recovered banks versus 

banks in the other three states.

We use a multino mial logistic estimating procedure to model future bank state.

Outcomes are nominal, and therefore the multinomial logit model’s assumptions are the closest 

fit to the specification of the model being estimated. This model simultaneously estimates binary 

logits for pairwise comparisons among the outcome categories to a reference outcome. These 

binary logits are (1) recover relative to failure, (2) merge relative to failure, and (3) remain a 

problem relative to failure.

A general form of the model tested is shown in equation 1, where Probability of State

(k)i,t is the probability that bank i will be in state k at time t.

(1) Probability of State (k)i,t = F(Financial conditioni,,t-1 , Economic conditionst)

We gauge the model’s effectiveness in several ways. First, we compare the out-ofsample forecasting accuracy for each of the four states with the actual number of banks ending
6

up in each state. Two competing binary models are compared with the multistate model for 

failure predictions, one that uses the same variables that our multistate model uses and a second 

that uses the same explanatory variables that Jarrow et al. (2003) used, referred to as the lossdistribution model. These two comparisons allow us to test whether including additional 

alternative states improve s the accuracy of failure estimates over binary models. Second, we 

investigate the economic and statistical effect of our explanatory variables. Third, we check to 

determine that the banks with the highest predicted probability of failure from our model are the 

ones that actually fail.

4. Sample and Data

Our sample consists of 1,996 banks on the FDIC problem-bank list from 1990 through 

2002. Each bank has at least one first event and second event that are paired as an observation. 

The first event occurs when a bank is examined and receives a CAMELS rating of 4 or 5. The 

second event occurs when the bank (1) recovers—improves to a CAMELS rating of 1, 2, or 3 at 

the next examination, (2) merges—either merges with a bank outside of its multibank holding 

company or consolidates within its multibank holding company,4 (3) remains a problem bank—

continues to have a CAMELS rating of 4 or 5 at the next examination, or (4) fails.5 We use only 

observations in which first events are paired with second events that occurred 6 to 24 months 

after the first events. (Any second events sooner than 6 months or later than 24 months are 

ignored, and the observa tion is dropped from the sample.) The reason for this restriction is

two fold: first, we want to allow enough time to pass for changes in financial condition to occur;

4 We recognize that characteristics differ between mergers and consolidations. However, during this period, the 

number of consolidations is small (39). Because of the small number, we combined both events into one state.

5 In the paper, failure is defined either as a closing that results from an action by a regulator or as a merger assisted

by the FDIC.
7

second, examination frequency set by the Federal Deposit Insurance Corporation Improvement 

Act of 1991 (FDICIA) was in effect during most of our period. FDICIA requires annual safetyand-soundness examinations except for banks with assets under $250 million and a composite 

CAMELS rating of 1 or 2. (Further exceptions may be found in the act.) As noted above, a 

pairing of events is considered one observation in our sample. Our sample consists of 3,747 

observations.

We divide these observations into annual cohorts corresponding to the year of the first 

event to allow us to implicitly control for the effects of economic conditions and banking 

legislation. Because second events usually occur in a year different from the first event, we 

recognize that using annual cohorts does not completely control for these effects.

A bank appears as an observation in a cohort only once, and each observation belongs to 

only one cohort.6 All observations end with the occurrence of the second event. If the bank in a 

given cohort reaches the second event as recovered (or merged, or a continued problem), the 

outcome for the observation for that bank in the cohort is considered a recovery (or a merger, or 

continuation as a problem).

The same bank can be an observation in multiple cohorts depending on when it first 

receives a CAMELS rating of 4 or 5 and on its outcome at the second event (continuation as a 

problem vis-à-vis recovery or merger). The repeat appearance of a bank in a subsequent cohort 

occurs with the second event when the bank, upon reexamination, continues as a problem bank. 

For such banks, the first observation ends with an outcome of continuation as a problem bank. 

Concurrently a second observation for that bank begins and is paired with its corresponding 

subsequent event, which takes place 6 to 24 months later. At this subsequent event, an outcome

6 Technically a bank may appear as a second observation in the same cohort since the window for the second event 

is as short as 6 months.
8

is determined for that second observation. In contrast, a bank that recovers or merge s at the 

second event has no concurrent second observation since it is no longer a problem bank.

However, recovered banks may reemerge in our sample in a later cohort (for example, the bank 

recovers but later reverts to problem-bank status ) following the recovery, whereupon the bank 

would be considered a new observation. Banks cannot appear in cohorts after they fail.

Our sample has the following characteristics: The number of problem banks declines 

drastically during the 1990s as the banking crisis that began in the mid-1980s and lasted through 

the early 1990s subsided. As figure 1 shows, the 1991 cohort has the highest number—897; the 

1997 cohort has the lowest number—62. Both the 1990 and 1998 cohorts have the highest 

percentage of problem banks that fail—5 percent. No problem banks fail before the second 

event in the 1997 or 2002 cohorts. (See figure 2.) Most remain a problem at the second event, 

ranging from a high of 69 percent in the 1990 cohort to a low of 40 percent in the 1997 cohort, 

with an average of 49 percent. The proportion that merge by the second event is small, ranging 

from 3 percent in the 1990 cohort to 20 percent in the 1998 cohort. The proportion that recover 

by the second event ranges from a low of 23 percent in the 1990 cohort to a high of 53 percent in 

the 1997 cohort. 7 Figure 3 shows that most banks that remain a problem at the second event 

ultimately recover.8

Using data from the Call Reports, we calculate beginning and ending periods for each 

bank. The beginning period is calculated from the Call Report filed just before the first event

7 Jones and Critchfield (2004) note three reasons that might explain the 1997 and 1998 peak years for merger 

activity and recoveries: (1) banks were highly profitable, liquid, and operating in favorable economic and interestrate environments; (2) the Riegle -Neal Interstate Banking and Branching Efficiency Act of 1994 removed the 

remaining barriers to interstate banking and branching; and (3) a record-breaking bull market in stocks pushed

market valuations of banks and thrifts to unprecedented levels, encouraging many banking firms to use their stock as 

currency to purchase other firms. Moreover, we found large increases in internal capital injections (as a percent of 

average assets) for banks that recovered peaked in 1996. External capital injections increased sharply from 1994 to 

1995 but did not peak until 1999. (See figure 4.)

8 The reason for the decline in the percentage of still-a -problem banks that ultimately recover beginning in 2001 is

that enough follow-up events have not yet occurred. Most banks remain a problem beyond a second event.
9

and the one filed 12 months previously.9 Balance-sheet items are averaged for the two reporting 

periods and taken as a ratio of average assets for the same two periods; income items are 

summed over the 12- month period and taken as a percentage of average assets for the two 

periods. Similar calculations are made for the ending period, using the Call Report filed 

immediately before the second event and the one filed 12 months previously.

We group banks by future state to compare their condition and performance. We then 

compare data reported at the ending period with those reported for the beginning. We compute 

the percentage of banks in each state with an increase between the two periods for each of the 

financial ratios. Assuming that banks that recover are able to improve net income and net 

noninterest income more than those that fail, we expect to see that the percentages of banks with 

increases in such ratios will be greater for banks having a future state of recovery than for banks 

with a future state of failure. For expense items, we expect the opposite. Assuming that banks 

that recover shed nonperforming and past-due assets, we expect that the percentage s of banks 

with increases in such assets will be less for banks that recover than for those that fail. We 

expect that the percentages of bankswith increases in volatile liabilities and illiquid assets will

be smaller for banks having a future state of recovery than for banks with a future state of failure, 

and that the percentages of banks with increases in capital will be larger for banks that recover 

than for those that fail. We ha ve no expectations for the ranking of banks that merge or are still a 

problem except that the percentages that increase will lie between the percentages for banks that 

recover and banks that fail.

9 Gunther and Moore (2000) find atypical movements in Call Report data for the quarters in which banks are 

downgraded by examiners. These Call Reports are more subject to revisions. For that reason, we also did our 

univariate analysis on the Call Reports filed before the ones specified in this paper. The resulting trends in data 

were similar to the trends reported in this paper.
10

We compare the percentages of banks in each state with increases in net interest income, 

provision for loan losses, and net no ninterest income as measures of earnings; average allowance 

for loan and lease losses, average loans and leases past due 30–89 days, average loans and leases 

past due 90 days or more, average nonaccrual loans and leases, and average other real estate 

owned as measures of risky asset portfolios; average risk-based capital and average tangible 

equity capital as measures of capital adequacy; average volatile liabilities and loans and 

securities with maturities greater than or equal to five years as measures for liquidity; and for the 

management measure, we use the efficiency ratio (noninterest expense as a percentage of net 

interest income plus noninterest income). A lower efficiency ratio is better.

To model future bank states, we select almost the same financial variables that were used 

in the univariate trend analysis.10 We added capital injections from a bank holding company and 

capital injections from outside as measures of the economy.11 From the univariate trend analysis 

we are able to form expectations concerning the sign that coefficients on these variables will take 

when estimated using logit analysis. A negative coefficient implies that an increase in the 

variable will result in the future state’s becoming less likely relative to failure. A positive 

coefficient implies the opposite.

Table 1 shows the expected sign of explanatory variables used in the multistate model. 

As table 1 shows, the financial ratios associated with not failing are capital, capital injections,

allowance fo r loan losses, interest income, noninterest income, and longer-term assets (assets and 

securities with maturities equal to or great than five years). Although we expect a negative sign

10 For the logits we used total income and detailed expense items instead of net interest and net noninterest income 

as used in the univariate analysis. In addition, we also estimated the model using Call Report data from the quarter 

before the quarter that precedes the examination, as in the univariate. The results differed little from those reported 

in this paper.

11 As noted in footnote 6, the economy (a record-breaking bull market) was one reason noted for increased

acquisitions.
11

for the efficiency ratio’s coefficient (because lower is better), we also associate this ratio with not 

failing. The financial ratios associated with failure are those measuring poor asset quality (pastdue loans, nonaccruing loans, and real estate owned), expense items (interest expense, loss 

provision, loan charge-offs, salaries, expenses on premises, and other noninterest expense), and 

volatile liquidity as measured by volatile liabilities.12

5. Results

Our results from both the univariate trend analysis and the multistate logit estimating 

procedure are generally in agreement with expectations. The percentage of banks with increases 

(between the beginning and ending periods) in performance ratios such as net income and net 

noninterest income that recover is greater than the percentage in any alternative state. For loan 

loss provisions, the opposite occurs. In addition, the percentage of banks that have increases in 

any of the risky asset measures and recover is less than the percentage in any alternative state.

In the logit analysis, we find that increases in financial ratios associated with nonfailure 

have positive coefficients, and those associated with failure have negative coefficients.

5.1 Univariate Trend Analysis

For all three earnings measures, the relative position of banks that recover is the best; and 

the position of banks that fail is the worst. As shown in figure 5, 62 percent of banks that 

recover (825 banks) ha ve an increase in ne t interest income between the beginning and ending 

periods, compared with 28 percent of banks tha t fail (33 banks). The percentages for banksthat

12 Volatile liabilities are federal funds purchased and sold under agreements to repurchase, demand notes issued to 

the U.S. Treasury and other borrowed money, time deposits over $100,000 held in domestic offices, foreign office 

deposits, trading liabilities less trading liabilities’ revaluation losses on interest rate, foreign exchange rate, and other 

commodity and equity contracts.
12

merge or are still a problem are also greater than the percentage for those that fail, but less than 

that for those that recover. A similar trend is seen for net noninterest income (figure 6). Fiftytwo percent of banks (689 banks) that recover experience an increase in net noninterest income, 

compared with 32 percent of banks that fail (37 banks).

Figure 7 shows that a smaller percentage of banks that recover ha ve an increase in loss 

provision between the two periods: 28 percent of banks that recover (372 banks) experience an 

increase in provision for loan losses at the second event, compared with 46 percent of banks that 

fail (53 banks). The percentages for banks that merge or are still a problem are less than the 

percentage for those that fail but greater than the percentage for banks that recover.

For all five asset-quality measures, our results agree with our expectations. The relative 

position of banks that recover is the best; the position of those that fail is the worst. A smaller 

percentage of banks that recover experience increases in loan- loss reserves, average loans and 

leases past due 30–89 days, average loans and leases past due 90 days or more, average 

nonaccrual loans and leases, and average other real estate owned between the beginning and 

ending periods than banks that merge, remain a problem, or fail. As shown in figure 8, 50

percent of banks that recover (667 banks) experience an increase in loan-loss reserves, compared 

with 72 percent of banks that fail (84 banks). The percentages for banks that merge or remain a 

problem are less than the percentage for those that fail but greater than the percentage for banks 

that recover. Figure 9 shows that 36 percent of banks that recover (476 banks) experience an 

increase in average loans and leases past due 30–89 days, compared with 56 percent of banks 

that fail (65 banks). Forty-one percent of banks that recover (543 banks) experience an increase 

in average loans and leases past due 90 days or more, compared with 56 percent of banks that 

fail (65 banks), as shown in figure 10. Figure 11 shows that 38 percent of banks that recover
13

(509 bank s) experience an increase in nonaccrual loans and leases, compared with 61 percent of 

banks that fail (71 banks). Forty- four percent of banks that recover (585 banks) experience an 

increase in other real estate owned, compared with 76 percent of banks that fail (88 banks), as 

shown in figure 12.

In line with our expectations, a larger percentage of banks that recover experience 

increases in average risk-based capital and average tangible equity capital between thebeginning 

and ending periods than banks in any alternative state. The smallest percentage of banks 

experiencing increases are those that fail. Figure 13 shows that 72 percent of banks that recover 

(952 banks) experience an increase in average risk-based capital, compared with 14 percent of 

banks that fail (16 banks). And figure 14 shows that 70 percent of banks that recover (926 

banks) experience an increase in average tangible equity capital, compared with 13 percent of 

banks that fail (15 banks).

In its 1988 study of bank failures, the Office of the Comptroller of the Currency (OCC) 

lists overreliance on volatile liabilities as one of the root causes of failure. We find, however, 

that banks that fail have the smallest percentage of banks with an increase in volatile liabilities 

between the beginning and ending periods. This result could be because management has 

reduced brokered deposits as required by more recent legislation. (The Federal Deposit 

Insurance Corporation Improvement Act of 1991 restricts the activities of critically 

undercapitalized institutions. One such restriction is that these institutions cannot pay an 

exceptionally large amount of interest on new or renewed liabilities.)

In its 1988 study the OCC also notes that failed banks frequently had inadequate liquid 

assets as a second source of liquidity. Thus, we expect the percentage of banks that experience 

an increase in loans and securities with maturities of greater than or equal to five years between
14

the beginning and ending periods to be larger for banks that fail than for banks that recover. And 

our data support this expectation. One explanation for this trend may be that banks that fail were 

more likely to take on riskier loans (construction loans, commercial real estate loans) than banks 

that recover.

Twenty-six percent of banks that recover (351 banks) experience an increase in volatile 

liabilities, compared with only 19 percent of banks that fail (22 banks), as shown in figure 15. 

Figure 16 shows that 40 percent of banks that fail (46 banks) experience an increase in loans and 

securities of longer maturities, compared with 37 percent of banks that recover (492 banks).

A larger percentage of banks that recovered experience improvements in the efficiency 

ratio between the beginning and ending periods than the percentages of banks that merge, remain 

a problem, or fail. Fifty- nine percent of banks that recover (832 banks) experience an increase in 

the efficiency ratio, compared with 22 percent of banks that fail (25 banks), as shown in figure 

17.

5.2 Analysis ofVariance

Table s 2 and 3 show results from the analysis of variance that complement the above 

results. Table 2 shows the mean and standard errors for financial variables in each state. Table 3 

shows the differences in means and statistical significances for six pairings: (1) recover versus 

merge, (2) recover versus remain a problem, (3) recover versus fail, (4) remain a problem versus 

merge, (5) remain a problem versus fail, and (6) merge versus fail. The beginning-period data 

are used in these tables.

The results reported in table 2 show that the mean values for each financial variable are 

statistically different from zero at the first event. Further, it shows that the mean values in
15

financial ratios associated with not failing are generally most often larger for banks that recover, 

merge, or remain a problem than they are for banks that fail. The opposite is true for the mean 

values in financial ratios associated with failing.

There are three exceptions, however: the mean values for total interest income, total 

noninterest income, and loans and securities with maturities greater than or equal to five years 

are largest for banks that fail. These results seem counterintuitive until we consider that banks 

with a future state of fail probably take on riskier assets that will have higher yields than banks in 

the alternative states. Banks with riskier assets have a higher probability of failure. Fee income 

from these riskier assets may have resulted in higher noninterest income. And in banks with a 

future state of fail, the ratio between loans and securities in the longer-term assets may be geared 

toward loans that are usually considered riskier than more- liquid securities.

The results reported in table 3 show that except for two variables (capital injections from 

the bank holding company, and capital injections from outside), the difference in means between 

banks that recover and those that fail is statistically significant. The differences in means 

between banks that merge and those that fail and between banks that remain a problem and those 

that fail are also significant for most variables.

The results from table 3 also indicate that the differences in means between banks that 

remain a problem and banks that merge and between banks that recover and banks that remain a 

problem are statistically significant. For the pairing recovery versus merger, fewer variables are 

statistically different from one another. And for the pairing remain a problem versus merger, 

even fewer variables are statistically different from one another.
16

5.3 Logit Analysis

For our multivariate analysis, we rely on a multinomial unordered logit probability model 

that takes into account all four future bank states. Equation 2 shows the model tested:

(2) Probability of State (k)i,t = F(Financial conditioni,,t-1 )

We did not include variables for economic condition in our model for a number of 

reasons. First, Nuxoll et al. (2003) did not find that state and local economic data contributed to 

the performance of standard off-site models. Second, much of the literature theorizes that the 

economy is subsumed in the balance sheet. Thus, any effect of the economy has already shown 

up in the financial data.13 And, we included capital injections as a proxy for changes in the 

economy (see footnote 10).

A stepwise logit estimation procedure is used to identify those terms that have a 

significant relationship in predicting the likelihood that a bank will end up in one of the states: 

recover, merge, remain a problem, or fail. The stepwise estimation procedure allows us to 

include several measure of the same attribute in the logit model, yet isolates the most important 

factors in terms of predicting state.

13 As a robustness check, we did estimate the model using three variables for the economy: a ratio of the number of 

problem banks to total number of banks by state, a ratio of the assets of problem banks to total assets by state, and 

the percentage change in state housing permits. The first test was an estimation using only the economic variables 

as explanatory variables. We did two estimations: one used the ratio of the number of problem banks to total by 

state and the percentage change in state housing permits; the second used the ratio of the assets of problem banks to 

total by state and the percentage change in housing permits. These variables were significant for most of these 

estimations. However, when they were included in estimations with the rest of the explanatory variables, their 

significance disappeared.
17

Table 4 shows summary statistics for the variables used in the logistic regressions. The 

beginning-period data, as explained in the section on sample and data, are used in this table.14

We estimate the logits for each of our cohorts, 1990 through 2002. However, becauseof 

the small number of failures, beginning with the 1994 logit we combine cohorts. The 1994 

model is a combination of the 1993 and 1994 cohorts, and the 1995 model combines 1993 

through 1995. We continue combining cohorts up to five years (the 1993 through 1997 cohorts 

for the 1997 logit). For the 1998 models through 2002 we use a panel of the most recent 

previous five years.

Tables 5 through 7 show the results. The reference state is failure, so the coefficients are 

interpreted relative to failure. As mentioned above, a negative coefficient me ans that an increase 

in a variable will have the result that the future state relative to failure becomes less likely.

Some of the more interesting findings are noted here. First, more recent cohorts 

(beginning with the 1995–99 cohort) have fewer statistically significant variables than those in 

the early 1990s. This result is most likely because of the small number of failures, despite the 

paneling of data. The number of failures totaled 7, 7, 8, and 8, respectively, from the 1995–99 

cohort through the 1998–02 cohort, compared with 45, 36, and 17, respectively, for the cohorts 

1990 through 1992. However, those variables that are statistically significant in the more recent 

cohorts have the expected sign (as shown in table 1) except for capital injections. For example, 

in the 1997–01 cohort, expenses on premises is significant and has a negative sign in table 5 

(recovery) and table 7 (still a problem). An increase in this variable will have the result that a 

future state of recovery or continuation as a problem becomes less likely relative to failure. On 

the other hand, for the cohorts 1994–98 through 1998–02, capital injectio ns from outside are

14 As with the univariate analysis, we also ran the logits using a beginning period one quarter before the quarters 

specified previously. Little difference in the results was noticeable.
18

significant but with the unexpected sign for table 6 (merger) and, in two of those four cohorts, 

for table 5 (recovery). Perhaps the negative sign indicates that the institution is anticipating it 

will be acquired and eithe r does not or cannot raise capital. As mentioned above, the RiegleNeal Interstate Banking and Branching Efficiency Act was enacted in 1994.

Second, both the pairing of recovery versus failure (table 5) and the pairing of merger 

versus failure (table 6) have more statistically significant variables than the pairing of 

continuation as a problem versus failure (table 7). We expect that banks that remain a problem 

more closely resemble banks that fail than they resemble banks that recover or merge. In fact, 

the univariate trend analysis showed that for most of the financial variables, the percentage of 

banks that remained a problem was closer to the percentage that failed than were the percentages 

for the remaining two future states.

Third, asset-quality variables in each future state (tables 5, 6, and 7) are more often 

statistically significant than other variables for each cohort. Moreover, nonaccrual loans and 

leases is more often statistically significant tha n past-due loans (either 30–89 days or 90 days or 

more), a result we would expect inasmuch as past-due loans are more likely to improve and be 

worked out tha n nonaccrual loans and leases. Further, these variables are negative (as expected 

from table 1), indicating that an increase in the variable will have the result that the future state 

relative to failure becomes less likely.

Fourth, surprisingly, tangible equity is highly statistically significant for only the 1990, 

1991, and 1992 cohorts in table 5 (recovery) and table 6 (merger). It is not statistically 

significant in the remaining years in tables 5 or 6, nor is it significant in any year in table 7 

(continuation as a problem).15

15 Thinking that tangible equity would be correlated with capital injections, we ran the models using only tangible 

equity. However, taking out capital injections made no difference in significance.
19

Fifth, another surprise is in the 1992 cohort, where external capital injections are 

significant and positive for all three future states (and yet are not significant again until the 

1994–98 cohort for merger [table 6], where the sign is negative).

Sixth, the efficiency ratio is significant only for the 1991 cohort for continuation as a 

problem versus failure (table 7). Since it is a ratio using income and expense variables, we 

omitted the earnings variables from the model as a robustness check. The results showed that 

without the earnings variables, the frequency of significance improved in the efficiency ratio. 

For example, in the 1993–97 and 1994–98 cohorts, the efficiency ratio is significant in all three 

future states and has the expected sign. In the 1991 and 1992 cohorts, it is significant for 

recovery versus failure and has the expected sign.

Seventh, in the earnings ratios, loan- loss provision is the most consistently significant 

ratio in all three future states, but more so for recovery (table 5) than for the other two outcomes 

(table 6 and table 7). This result makes sense since the sooner loan losses are recognized, the 

more likely it is that a bank will survive. We also tried running the logits without the efficiency 

ratio to see whether we could gain more significance in the earnings ratios. However, the 

significance in the earnings ratios still did not become more frequent.

Finally, the most startling result is in the 1994–98 cohort for all three future states. The 

coefficients for loans past due 90 days, nonaccrual loans and leases, and other real estate owned 

are much larger than in any other cohorts, and the sign on other real estate owned is positive 

(indicating that an increase in this variable is more likely to result in nonfailure). The likely 

explanation is the small number of failures and the banks that are in the 1994–98 cohort. First, 

the number of failures fell from 10 in the 1993–97 cohort to 7 in the 1994–98 cohort. Second, 

one of the failures included in 1998 was a bank that failed because of fraudulent activity. As a
20

result, this bank had a very low amount of other real estate owned, perhaps enough to skew the 

model. For example, the mean of other real estate owned (as a percentage of assets) for failed 

banks in the 1993–97 cohort equaled 3.0 percent; for the 1994–98 cohort the mean dropped to

0.62 percent.

5.4 Prediction of State: Out-of-SampleResults

Whether the logit model is accurate in making out-of-sample predictions is the true 

measure of its contribution. To test its accuracy, we forecast future bank states using priorperiod estimations from our unordered logit model on the following year’s cohort. For example, 

we use the coefficients from the 1990 cohort to predict the future state of the 1991 cohort, 

coefficients from the 1991 cohort to predict the future state of the 1992 cohort, and so on. Since 

our model is estimated from paired observations of first events and outcomes at second events, 

no observations that are in the 1990 cohort can be in the 1991 cohort (see explanatio n of 

observations in the section on sample and data). All state predictions are determined by 

summing predicted state probabilities for the cohort, yielding the expected number of banks in 

each future state. Figures 18 through 20 show the results.

All three figures show that the number of predicted banks in each state is very close to 

the number of actual banks ending up in the state. For example, in figure 18, the logit predicts 

27 banks to recover in 1996, and 26 banks actually recovered. Five banks are predicted to be 

acquired in 1996 (figure 19), compared with 3 that actually merged. And 26 banks are predicted 

to remain a problem in 1996 (figure 20), compared with 29 that actually did remain problems.

To compare binary forecasts against our multistate model, we do two comparisons. First, 

we run a binary model using the same financial variables as in the multistate model to predict
21

failure versus nonfailure. We then compare the predicted probabilities of failure from this binary 

model with predicted probabilities of failure from the multistate model. Figure 21 compares the 

forecasts of the two models with the actual number of failed banks. As can be seen, both models 

predict failure fairly accurately.

The second comparison is shown in figure 22. We compare the predicted probabilities of 

failure from our multistate model with predictions from the loss-distribution model (LDM). This 

model uses variables found in conventional bank- failure literature to predict bank failure within 

the second quarter after the Call Report is filed. In our test, we use the betas from the LDM 

estimated one year earlier to predict failures of problem banks for the following year. For 

example, the LDM predicted that eight banks would fail in 1994 (figure 22). The predictions 

resulted from the use of the betas estimated in the 1993 LDM.

As noted above in the section on sample and data, a second event for an observation may 

occur as many as 24 months after the first event. A bank on the problem list in December 1993 

may be in our 1992 cohort that used Call Data after 1993 in the estimation. Thus, to get a true 

out-of-sample prediction using our model, we have to use the estimated betas from a cohort two 

years before the date for which we are predicting failures for problem banks. The six banks that 

are predicted to fail in 1994 resulted from using estimated betas from our 1992 model (figure 

22). But since the LDM requires only a one-year prediction horizon, one would expect it to be a 

better predictor of failure than our model. This expectation is not quite borne out. As figure 22 

shows, the two models are comparable. The advantage of ours is that we can predict not only 

problem-bank failure but also recoveries, mergers, and continuations as problems.

To determine whether banks with the highest probability of failure are the ones that 

actually fail, we rank banks that our model predicts to fail in each period by their probability of
22

failure. We then divide them into deciles to determine that the highest decile contains the largest 

number of banks that actually failed. Thirty-eight of the 65 failed banks in our cohorts from 

1991 through 2002 were in the tenth decile.16 An additional 8 banks were in the ninth decile.

The results for the remaining three states, however, were not quite as accurate. Of the

1,058 recovered banks in our cohorts from 1991 through 2002, 166 (16 percent) were in the tenth 

decile ; adding the 159 banks in the ninth decile raises the percent age to 31. For banks that are 

predicted to merge, 14 percent are in the tenth decile (26 banks out of the 191 banks that merged 

in our cohorts from 1991 through 2002); and for banks that are predicted to remain a problem, 15 

percent are in the tenth decile.

5.5 EconomicSignificance

To test the economic significance of the explanatory variables, we use a fairly standard

approach—evaluating in-sample predicted state probabilities on the basis of the mean values of 

explanatory variables and evaluating how these probabilities change with marginal changes in 

key explanatory variables. Because asset-quality variables, specifically loans past due 90 days or 

more and nonaccrual loans and leases, were the variables most consistently significant across 

panels, we compared their economic significance in two periods: 1990 and the panel 1995–99.

We use the predicted in-sample state probabilities for 1990 and the 1995–99 panel based 

on the mean values for explanatory variables in 1990 and in the 1995–99 panel. The means for 

the sample of banks used in model estimation for both periods are shown in table 8.

The predicted state probabilities evaluated at the mean for banks in the 1990 cohort are

16.41 percent for recover, 1.82 percent for merge, 80.82 percent for remain a problem, and 0.95 

percent for fail. Both loans past due 90 days or more and nonaccrual loans and leases were

16 For the binary model, 39 of the 65 failed banks were in the tenth decile.
23

statistically significant in 1990 and in the 1995–99 panel. Table 9 shows the effects on estimated 

state probabilities, ceteris paribus, should each of these ratios experience a 1 percentage point 

increase in either of the periods examined. For example, in 1990 the mean for loans past due 90 

days or more equaled 0.7964 percent of assets. If that is increased 1 percentage point to 1.7964 

percent, the probability of recovery decreases from 16.41 percent to 12.52 percent ; the 

probability of merger decreases from 1.82 percent to 1.59 percent; the probability of continuation 

as a problem increases from 80.82 percent to 84.74 percent; and the probability of failure 

increases from 0.95 percent to 1.15 percent.

6. Conclusions

We offer an approach that differs from that of previous failure-prediction models.

Because we focus on troubled banks only, we can estimate a model to predict failures and the 

three alternative outcomes: recovery, merger and continuation as a problem.

Our results show that our model not only compares favorably with the standard binary 

failure-prediction model but also gives banks that will eventually fail the highest probability of 

failure. Further, the explanatory variables are economically significant.

Knowing these four predicted states is arguably more helpful to the deposit insurer than 

knowing only two predicted states. First, the FDIC’s long-term strategic planning requires 

knowing the likely direction of all problem banks; second, bank regulation often focuses on 

whether policy can affect the likelihood that troubled banks can successfully resolve their own 

problems.
24

References

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Federal Reserve Bank of Cleveland Economic Review (Quarter 4): 2–18.

DeYoung, Robert. 2003. De Novo Bank Exit. Journal of Money, Credit, and Banking 35, no.

5:711–28.

Cole, Rebel A., and Jeffery W. Gunther. 1998. Predicting Bank Failures: A Comparison of Onand Off-Site Monitoring Systems. Journal of Financial Services Research 13, no. 2:103–

17.

Hannan, Timothy H., and Stephen A. Rhoades. 1987. Acquisition Targets and Motives: The 

Case of the Banking Industry. Review of Economics and Statistics 69, no. 1:67–74.

Gunther, Jeffery W., and Robert R. Moore. 2000. Financial Statements and Reality: Do 

Troubled Banks Tell All?. Federal Reserve Bank of Dallas Economic and Financial 

Review (Quarter 3): 30–35.

Jagtiani, Julapa, James Kolari, Catharine Lemieux, and Hwan Shin. 2003. Early Warning 

Models for Bank Supervision: Simpler Could Be Better. Federal Reserve Bank of 

Chicago Economic Perspectives (Quarter 3): 49–60.

Jarrow, Robert A., Rosalind L. Bennett, Michael C. Fu, Daniel A. Nuxoll, and Huiju Zhang.

2003. A General Martingale Approach to Measuring and Valuing the Risk to the FDIC 

Deposit Insurance Funds. Unpublished paper, presented at the FDIC Conference on 

Fina nce and Banking: New Perspectives.
25

Jones, Kenneth D., and Tim Critchfield. 2004. The Declining Number of U.S. Banking 

Organizations: Will the Trend Continue? Future of Banking Study. Online. FDIC. 

Available at: http://www.fdic.gov/bank/analytical/future/index.html. March 11, 2005.

King, Thomas B., Andrew P. Meyer, Daniel A. Nuxoll, and Timothy J. Yeager. Are the Causes 

of Bank Distress Changing? Can Researchers Keep Up? Center for Financial Research 

Working Paper Series, no. 2005-03. FDIC.

Kolari, James, Dennis Glennon, Hwan Shin, and Michele Caputo. 2002. Predicting Large U.S. 

Commercial Bank Failures. Journal of Economic and Business 54:361–87.

Nuxoll, Daniel A., John O’Keefe, and Katherine Samolyk. 2003. Do Local Economic Data 

Improve Off-Site Bank-Monitoring Models? FDIC Banking Review 15, no. 2:39–53.

Whalen, Gary. 1991. A Proportional Hazard Model of Bank Failure: An Examination of Its 

Usefulness as an Early Warning Tool. Federal Reserve Bank of Cleveland Economic 

Review 27, no. 1:21–31.

Wheelock, David C., and Paul W. Wilson. 2000. Why do Banks Disappear? The Determinants 

of U.S. Bank Failures and Acquisitions. Review of Economics and Statistics 82, no.

1:127–38.
26

Fail Remain a Problem Merge Recover

100%

80%

60%

40%

20%

0%

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Figure 1

Number of Troubled Banks by Cohort

Figure 2

Cohort Composition of Troubled 

Banks at Second Event

1000

897 866

800

713

600

413

400

194 200

110 115

64 62 65 76 98 74

0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
27

Number

Percent

Figure 3

Number of Troubled Banks That 

Remain a Problem and Ultimately Recover

700 100%

595 600

535 80%

500

60%

400 359

300

40%

200 190

20%

100 87

32 36 35 47 57

52

25 27

0 0%

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Number of Troubled Banks (left scale) Percent That Ultimately Recovered (right scale)

Figure 4

External and Internal Capital Injections 

in Troubled Banks That Recovered

(12 months before recovery)

External Injections (left scale) Internal Injections (left scale) Percent of Banks Recovering (right scale)

6% 60%

5% 50%

4% 40%

3% 30%

2% 20%

1% 10%

0% 0%

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Percent of Average Assets

Percent of Banks that Recovered
28

Figure 5

Increases in Net Interest Income

Figure 6

Increases in Net Noninterest Income

80%

62%

60%

47% 47%

40%

28%

20%

0%

FAILURE STILL A PROBLEM MERGER RECOVERED

80%

60%

52%

42% 43%

40%

32%

20%

0%

FAILURE STILL A PROBLEM MERGER RECOVERED

Percentage Percentage
29

Figure 7

Increases in Loss Provision

Figure 8

Increases in Loan-Loss Reserves

100%

80%

72%

67% 64%

60%

50%

40%

20%

0%

FAILURE STILL A PROBLEM MERGER RECOVERED

80%

60%

46%

41%

40% 36%

28%

20%

0%

FAILURE STILL A PROBLEM MERGER RECOVERED

Percentage Percentage
30

Figure 9

Increases in 30–89 Days Past-Due Loans

Figure 10

Increases in Past-Due Loans 90 Days or More

100%

80%

60% 56%

50%

46%

40% 36%

20%

0%

FAILURE STILL A PROBLEM MERGER RECOVERED

100%

80%

60% 56%

47% 46%

41%

40%

20%

0% 

FAILURE STILL A PROBLEM MERGER RECOVERED

Percentage Percentage
31

Figure 11

Increases in Nonaccrual Loan

Figure 12

Increases in Other Real Estate Owned

100%

80%

61% 59% 60%

52%

40% 38%

20%

0% 

FAILURE STILL A PROBLEM MERGER RECOVERED

100%

80% 76%

63%

60%

52%

44%

40%

20%

0%

FAILURE STILL A PROBLEM MERGER RECOVERED

Percentage Percentage
32

Figure 13

Increases in Risk-Based Capital

Figure 14

Increases in Tangible Capital

100%

80%

72%

60%

47% 46%

40%

20% 14%

0%

FAILURE STILL A PROBLEM MERGER RECOVERED

100%

80%

70%

60%

47%

41%

40%

20% 13%

0% 

FAILURE STILL A PROBLEM MERGER RECOVERED

Percentage

Percent
33

Figure 15

Increases in Volatile Liabilities

Figure 16

Increases in Loans and Securities with Maturities 

Greater than or Equal to Five Years

100%

80%

60%

40% 40% 38% 37%

32%

20%

0%

FAILURE STILL A PROBLEM MERGER RECOVERED

100%

80%

60%

40%

29% 26% 23%

20% 19%

0%

FAILURE STILL A PROBLEM MERGER RECOVERED

Percentage
34

Figure 17

Improvement in the Efficiency Ratio

100%

80%

60% 59%

40% 40%

40%

22%

20%

0%

FAILURE STILL A PROBLEM MERGER RECOVERED

Percent
35

300

Figure 18

Recovered

(One-Year-Ahead Forecasts versus Actual)

250

200

150

100

50

0

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Actual 265 281 166 81 45 26 32 22 31 28 40 41

Unordered 218 241 152 76 46 27 28 32 33 30 40 51
36

40

Figure 19

Acquired

(One-Year-Ahead Forecasts versus Actual)

35

30

25

20

15

10

5

0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Actual 35 35 33 18 9 3 4 13 6 6 8 12

Unordered 30 30 25 14 7 5 5 4 6 6 10 9
37

700

Figure 20

Continued as a Problem

(One-Year-Ahead Forecasts versus Actual)

600

500

400

300

200

100

0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Actual 516 343 174 83 49 29 25 26 36 33 46 53

Unordered 572 391 193 87 46 26 28 27 31 30 44 44
38

45

40

35

30

25

20

15

10

5

Figure 21

Failed

(Predictions of Unordered Multistate and Bivariate Models 

One-Year-Ahead Forecasts versus Actual)

0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Actual 33 16 6 1 0 1 0 3 2 1 2 0

Binary 41.6 12.9 9.5 5.2 4.1 0.9 0.4 1.7 2.6 2.1 1.9 1.3

Unordered 41.1 13.1 8.6 5.6 4.0 0.1 0.0 1.4 5.2 1.5 1.9 1.4

Figure 22

Failed

(Predictions of Unordered Multistate Model and LDM Models 

One-Year-Ahead Forecasts versus Actual

Total Problem-Bank List at Year-End)

40

30

20

10

0

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Actual 38 12 6 4 1 1 6 2 3 5 1 3

Unordered 21 5 5 7 1 3 9 2 3 6 5 6

LDM Bi i t 36 24 8 3 1 1 2 2 4 3 5 4
39

Table 1

Explanatory Variables and Expected Signs for 

Predicting Nonfailure versus Failure States

Variable Sign

Capital

Tangible Equity Capital +

Capital Injections:

From BHC +

Outside +

Asset Quality

Past-Due Loans (30 - 89 days) -

Past-Due Loans (90+ days) -

Nonaccrual Loans and Leases -

Other Real Estate Owned -

Allowance for Loan Loss +

Management

Efficiency Ratio -

Earnings

Total Interest Income +

Total Noninterest income +

Total Interest Expense -

Loan-Loss Provision -

Loan Charge-offs -

Expenses on Premises -

Salaries -

Other noninterest expense -

Liquidity

Volatile Liabilities -

Loans + Securities > Five Years +
40

Table 2

Mean and Standard Errors for Financial Variables, by State

(Standard Errors in Parentheses)

Variable Recover Merge

Remain a

Problem Fail

Number of Banks 1,326 228 2,077 116

Capital

Tangible Equity Capital 6.68 *** 6.57 *** 6.39 *** 3.90 ***

(0.08) (0.20) (0.07) (0.29)

Capital Injections:

From BHC 0.19 *** 0.41 *** 0.20 *** 0.14 *

(0.02) (0.06) (0.02) (0.08)

Outside 0.36 *** 0.29 *** 0.29 *** 0.42 ***

(0.04) (0.09) (0.03) (0.13)

Asset Quality

Past-Due Loans (30 - 89 days) 1.88 *** 2.12 *** 2.43 *** 3.08 ***

(0.04) (0.10) (0.03) (0.14)

Past-Due Loans (90+ days) 0.59 *** 0.65 *** 0.92 *** 1.17 ***

(0.04) (0.09) (0.03) (0.13)

Nonaccrual Loans and Leases 2.17 *** 2.56 *** 2.64 *** 3.82 ***

(0.06) (0.14) (0.05) (0.20)

Other Real Estate Owned 2.00 *** 1.88 *** 2.60 *** 3.19

(0.07) (0.17) (0.06) (0.23) ***

Allowance for Loan Loss 1.69 *** 1.97 *** 1.72 *** 2.11 ***

(0.03) (0.07) (0.02) (0.10)

Management

Efficiency Ratio 88.32 *** 94.15 *** 94.43 *** 115.77 ***

(0.67) (1.62) (0.54) (2.28)

Significance at 1%, 5% and 10% levels are indicated by ***, **, and * asterisks, respectively.
41

Table 2 (Continued)

Mean and Standard Errors for Financial Variables, by State

(Standard Errors in Parentheses)

Variable Recover Merge

Remain a

Problem Fail

Number of Banks 1,326 228 2,077 116

Earnings

Total Interest Income 8.80 *** 8.99 *** 9.59 *** 10.58 ***

(0.09) (0.21) (0.07) (0.29)

Total Noninterest income 1.41 *** 1.44 *** 1.45 *** 1.98 ***

(0.06) (0.15) (0.05) (0.21)

Total Interest Expense 4.47 *** 4.51 *** 5.27 *** 6.51 ***

(0.06) (0.14) (0.05) (0.20)

Loan-Loss Provision 1.35 *** 2.23 *** 1.81 *** 3.21 ***

(0.07) (0.16) (0.05) (0.22)

Loan Charge-offs 1.46 *** 1.78 *** 1.80 *** 2.86 ***

(0.05) (0.12) (0.04) (0.17)

Expenses on Premises 0.68 *** 0.75 *** 0.77 *** 0.96 ***

(0.01) (0.03) (0.01) (0.04)

Salaries 2.06 *** 2.08 *** 2.18 *** 2.56 ***

(0.03) (0.08) (0.03) (0.11)

Other noninterest expense 2.23 *** 2.54 *** 2.43 *** 3.29 ***

(0.05) (0.11) (0.04) (0.16)

Liquidity

Volatile Liabilities 13.26 *** 13.62 *** 14.96 *** 15.04 ***

(0.27) (0.65) (0.22) (0.91)

Loans + Securities > Five Years 66.13 *** 68.11 *** 68.71 *** 68.04 ***

(0.31) (0.76) (0.25) (1.06)

Significance at 1%, 5% and 10% levels are indicated by ***, **, and * asterisks, respectively.
42

Table 3

Differences in Means and Standard Errors 

of Financial Variables for Selected Pairs 

(Standard Errors in Parentheses)

Variable

Recover -

Merge

Recover -

Remain a 

Problem

Recover -

Fail

Merge -

Remain a 

Problem

Remain a

Problem -

Fail Merge - Fail

Capital

Tangible Equity Capital 0.10 0.29 ** 2.78 *** 0.19 2.49 *** 2.68 ***

(0.22) (0.11) (0.30) (0.22) (0.29) (0.35)

Capital Injections:

From BHC -0.22 *** (0.01) 0.04 0.21 *** 0.05 0.26 **

(0.06) (0.03) (0.08) (0.06) (0.08) (0.10)

Outside 0.07 0.08 (0.06) 0.00 -0.14 -0.13

(0.10) (0.05) (0.13) (0.10) (0.13) (0.16)

Asset Quality

Past-Due Loans (30 - 89 days) -0.24 ** -0.55 *** -1.20 *** -0.31 ** -0.65 *** -0.96 ***

(0.11) (0.05) (0.15) (0.11) (0.15) (0.18)

Past-Due Loans (90+ days) -0.06 -0.33 *** -0.58 *** -0.27 ** -0.25 * -0.52 ***

(0.10) (0.05) (0.13) (0.10) (0.13) (0.16)

Nonaccrual Loans and Leases -0.39 ** -0.47 *** -1.65 *** -0.09 -1.18 *** -1.27 ***

(0.15) (0.07) (0.20) (0.15) (0.20) (0.24)

Other Real Estate Owned 0.12 -0.59 *** -1.19 *** -0.72 *** -0.59 ** -1.31 ***

(0.18) (0.09) (0.24) (0.18) (0.24) (0.29)

Allowance for Loan Loss -0.29 *** -0.04 -0.42 *** 0.25 ** -0.38 *** -0.13

(0.08) (0.04) (0.11) (0.08) (0.11) (0.13)

Management

Efficiency Ratio -5.83

(1.76)

*** -6.10

(0.86)

*** -27.44

(2.37)

*** -0.28

(1.71)

-21.34

(2.34)

*** -21.62

(2.79)

***

Significance at 1%, 5% and 10% levels are indicated by ***, **, and * asterisks, respectively.
43

Table 3 (Continued) 

Differences in Means and Standard Errors 

of Financial Variables for Selected Pairs

(Standard Errors in Parentheses)

Variable

Recover -

Merge

Recover -

Remain a 

Problem

Recover -

Fail

Merge -

Remain a 

Problem

Remain a

Problem -

Fail Merge - Fail

Earnings

Total Interest Income -0.19 -0.79 *** -1.78 *** -0.60 ** -0.99 *** -1.59 ***

(0.22) (0.11) (0.30) (0.22) (0.30) (0.36)

Total Noninterest income -0.03 -0.04 -0.57 ** -0.01 -0.53 ** -0.54 **

(0.17) (0.08) (0.22) (0.16) (0.22) (0.26)

Total Interest Expense -0.03 -0.79 *** -2.03 *** -0.76 *** -1.24 *** -2.00 ***

(0.15) (0.07) (0.21) (0.15) (0.20) (0.24)

Loan-Loss Provision -0.88 *** -0.46 *** -1.85 *** 0.42 ** -1.40 *** -0.97 ***

(0.17) (0.08) (0.23) (0.17) (0.23) (0.27)

Loan Charge-offs -0.32 ** -0.33 *** -1.39 *** -0.02 -1.06 *** -1.08 ***

(0.13) (0.07) (0.18) (0.13) (0.18) (0.21)

Expenses on Premises -0.07 ** -0.09 *** -0.28 *** -0.02 -0.19 *** -0.21 ***

(0.03) (0.02) (0.04) (0.03) (0.04) (0.05)

Salaries -0.03 -0.12 ** -0.50 *** -0.09 -0.38 *** -0.48 ***

(0.08) (0.04) (0.11) (0.08) (0.11) (0.13)

Other non-int. exp -0.30 ** -0.20 *** -1.06 *** 0.11 -0.86 *** -0.76 ***

(0.12) (0.06) (0.16) (0.12) (0.16) (0.19)

Liquidity

Volatile Liabilities

Loans + Securities > Five Years

-0.36

(0.70)

-1.98

(0.82)

**

-1.70

(0.35)

-2.57

(0.40)

***

***

-1.78

(0.95)

-1.91

(1.11)

*

*

-1.34

(0.69)

-0.60

(0.80)

* -0.09

(0.94)

0.66

(1.09)

-1.43

(1.12)

0.07

(1.31)

Significance at 1%, 5% and 10% levels are indicated by ***, **, and * asterisks, respectively.
44

Table 4

Selected Descriptive Statistics for Data in Logits: Mean, Standard Deviation, and 

Minimum and Maximum Values of Financial Ratios for Each State

Variable All Recover Merge

Remain a

Problem Fail

Number of Banks 3,747 1,326 228 2,077 116

Capital

Tangible Equity Capital

Mean 6.42 6.68 6.57 6.39 3.90

Standard Deviation 3.12 2.54 3.42 3.37 2.76

Minimum -4.77 -0.10 -4.77 -1.26 -1.37

Maximum 63.10 38.34 34.52 63.10 15.79

Capital Injections:

From BHC

Mean 0.20 0.19 0.41 0.20 0.14

Standard Deviation 0.87 0.82 1.41 0.84 0.65

Minimum -1.07 -1.07 0.00 -0.93 -0.02

Maximum 12.87 9.98 12.87 8.97 4.74

Outside

Mean 0.36 0.36 0.29 0.29 0.42

Standard Deviation 1.38 1.46 1.52 1.25 2.24

Minimum -2.03 -1.89 -0.07 -2.03 -0.62

Maximum 25.16 15.98 15.98 25.16 18.38

Asset Quality

Past-Due Loans (30 - 89 days)

Mean 2.24 1.88 2.12 2.43 3.08

Standard Deviation 1.58 1.38 1.84 1.60 1.95

Minimum 0.00 0.00 0.00 0.00 0.25

Maximum 18.66 12.41 18.66 13.96 9.59

Past-Due Loans (90+ days)

Mean 0.79 0.59 0.65 0.92 1.17

Standard Deviation 1.39 0.81 0.81 1.66 1.82

Minimum 0.00 0.00 0.00 0.00 0.00

Maximum 44.66 10.25 6.22 44.66 14.42

Nonaccrual Loans and Leases

Mean 2.51 2.17 2.56 2.64 3.82

Standard Deviation 2.13 1.83 2.62 2.19 2.36

Minimum 0.00 0.00 0.00 0.00 0.05

Maximum 24.71 15.60 24.71 17.67 11.18

Other Real Estate Owned

Mean 2.36 2.00 1.88 2.60 3.19

Standard Deviation 2.55 2.13 1.97 2.79 2.63

Minimum -10.05 0.00 0.00 -10.05 -0.24

Maximum 20.20 18.61 10.99 20.20 12.48

Allowance for Loan Loss

Mean 1.74 1.69 1.97 1.72 2.11

Standard Deviation 1.13 1.08 2.01 1.03 0.95

Minimum 0.11 0.11 0.36 0.14 0.28

Maximum 26.45 19.82 26.45 14.13 5.63

Management

Efficiency Ratio

Mean 92.91 88.32 94.15 94.43 115.77

Standard Deviation 25.00 22.85 25.34 25.09 29.99

Minimum -30.64 -30.64 35.09 29.38 26.72

Maximum 198.71 193.89 195.03 198.71 194.40
45

Table 4 (Continued)

Selected Descriptive Statistics for Data in Logits: Mean, Standard Deviation, and 

Minimum andMaximum Values of Financial Ratios for Each State

Variable All Recover Merge

Remain a

Problem Fail

Earnings

Total Interest Income

Mean 9.30 8.80 8.99 9.59 10.58

Standard Deviation 3.14 2.60 4.63 3.14 4.11

Minimum -0.01 -0.01 5.24 0.97 4.47

Maximum 64.32 33.69 64.32 32.78 26.25

Total Noninterest income

Mean 1.45 1.41 1.44 1.45 1.98

Standard Deviation 2.31 2.18 1.84 2.38 3.20

Minimum -0.09 -0.09 0.00 -0.05 0.18

Maximum 46.35 37.82 16.34 46.35 30.51

Total Interest Expense

Mean 4.98 4.47 4.51 5.27 6.51

Standard Deviation 2.18 1.76 2.25 2.28 2.95

Minimum -0.68 -0.68 1.25 0.25 0.95

Maximum 17.01 14.74 15.42 17.01 16.57

Loan-Loss Provision

Mean 1.72 1.35 2.23 1.81 3.21

Standard Deviation 2.43 1.72 6.07 2.02 2.69

Minimum -13.56 -2.17 -1.54 -13.56 -0.42

Maximum 87.33 23.14 87.33 24.55 13.82

Loan Charge-offs

Mean 1.71 1.46 1.78 1.80 2.86

Standard Deviation 1.89 1.41 3.52 1.86 2.06

Minimum -6.32 -0.49 -6.32 0.00 0.25

Maximum 47.20 19.12 47.20 24.12 11.50

Expenses on Premises

Mean 0.75 0.68 0.75 0.77 0.96

Standard Deviation 0.46 0.40 0.44 0.47 0.67

Minimum -0.48 -0.48 0.00 -0.04 0.15

Maximum 4.46 3.38 2.79 4.35 4.46

Salaries

Mean 2.14 2.06 2.08 2.18 2.56

Standard Deviation 1.18 0.95 0.99 1.23 2.32

Minimum 0.00 0.06 0.00 0.04 0.43

Maximum 22.99 16.14 9.17 22.36 22.99

Other noninterest expense

Mean 2.39 2.23 2.54 2.43 3.29

Standard Deviation 1.68 1.55 2.43 1.61 2.12

Minimum -3.03 -3.03 0.45 -0.41 0.71

Maximum 32.70 22.46 32.70 25.38 14.86

Liquidity

Volatile Liabilities 

Mean

Standard Deviation 

Minimum Maximum

Loans + Securities > Five years 

Mean

Standard Deviation

Minimum 

Maximum

14.28

9.85

0.00

90.19

67.74

11.51

19.40

102.02

13.26

9.51

0.00

86.08

66.13

11.39

24.31

102.02

13.62

11.08

0.00

90.19

68.11

12.15

19.40

96.38

14.96

9.90

0.00

89.43

68.71

11.43

22.14

101.05

15.04

9.26

0.00

51.54

68.04

11.21

37.34

90.33
46

Table 5

Multinomial Logit Regressions of Determinants of Bank State: Recovery versus Failure 

Estimation Period (Years)

Explanatory Variables 1990 1991 1992 1993 1993-94 1993-95 1993-96 1993-97 1994-98 1995-99 1996-00 1997-01 1998-02

Intercept

Constant -0.2388 10.6388 *** -2.0548 -2.8325 8.4024 * 7.0800 * 6.2905 6.0942 14.2305 -2.8352 3.9788 1.0819 -0.6638

Capital

Tangible Equity for PCA 0.7743 *** 1.0350 *** 0.7071 *** -0.1248 -0.1827 -0.0345 -0.0198 -0.0262 1.4338 0.2043 -0.0802 -0.0156 -0.0120

BHC Capital Injections 2.0621 -0.3816 0.6065 0.5490 0.4402 0.3137 0.3806 0.3978 -3.1364 -0.6126 -0.3481 -0.6839 * -0.6238

External Capital Injections 0.0021 0.1636 5.1764 ** 0.8219 1.1397 1.5649 1.5940 1.6146 -0.9101 -0.3444 * -0.2561 -0.2861 ** -0.2477

Asset Quality

Loans Past Due 30 to 89 Days -0.4142 *** -0.4417 *** -0.2429 -0.8305 ** -0.4773 ** -0.2567 -0.1913 -0.1730 0.5548 0.3001 0.5276 0.1184 0.0400

Loans Past Due 90 Days or More -0.4630 *** -0.1805 -0.5600 -0.3232 -0.1968 -0.3677 ** -0.4260 *** -0.4530 *** -1.0422 ** -0.5484 *** -0.3320 -0.1901 0.0021

Nonaccrual Loans and Leases -0.2163 ** -0.3689 *** -0.4007 ** -0.7762 * -0.6210 *** -0.5803 *** -0.6002 *** -0.6283 *** -2.9354 ** -0.6012 ** -0.3506 -0.2224 -0.1628

Other Real Estate Owned -0.3847 *** -0.2984 *** 0.1820 0.3759 0.3007 0.2900 0.3299 0.3245 7.6096 ** 0.6112 0.0660 -0.0690 -0.1354

Allowance for Loan Losses 0.3333 0.7585 *** 0.1987 2.5538 ** 0.9456 * 0.7284 0.7687 * 0.7911 * 4.1672 0.8971 1.0031 1.0392 0.9083

Management

Efficiency Ratio -0.0010 -0.0218 -0.0233 0.0277 -0.0398 -0.0406 -0.0328 -0.0341 -0.0417 0.0685 0.0308 0.0411 0.0498

Earnings

Total Interest Income 1.0295 -0.9335 0.6710 2.3293 -0.3460 -0.3550 -0.1461 -0.1235 -3.0691 0.4429 0.4718 0.6600 1.0897

Total Noninterest Income 0.6360 -0.6387 0.3575 2.2171 * 0.4048 0.1514 0.2814 0.2917 -2.3035 0.7560 0.4635 0.5670 0.7607

Total Interest Expense -1.2585 * -0.0224 -0.6493 -1.2851 0.7541 0.3780 0.0680 0.0577 -0.7468 -0.9387 -1.0633 -1.3890 * -1.6172

Loan-loss Provision -0.7479 *** -0.6765 *** -0.1868 -1.2186 * -0.9301 ** -0.8185 ** -0.9011 ** -0.9159 ** 0.1979 -0.5587 -0.8741 -0.6199 -0.3175

Loan Chargeoffs 0.7248 *** 0.6950 *** -0.4833 0.8751 0.6614 0.7422 0.7013 * 0.6955 ** 1.5349 0.6323 0.6174 0.4337 0.2000

Expenses on Premises -0.3109 -0.3120 -2.5638 -1.7553 -0.6163 -0.9336 -1.0243 -0.9938 -2.3193 -2.3415 -2.7488 * -3.4456 ** -3.7522 **

Salaries -1.4292 ** 0.5001 0.9138 -1.6762 -0.0150 0.1143 -0.0836 -0.1369 7.9328 -0.6335 -0.3216 -0.3277 -0.2640

Other Noninterest Expense -0.3044 0.0733 -0.4493 -3.4496 ** -0.7108 -0.3225 -0.4617 -0.4250 -0.0248 -1.1180 -0.7435 * -0.7250 -1.2015

Liquidity

Volatile Liabilities -0.0130 0.0180 0.0191 -0.0726 -0.0456 -0.0072 0.0087 0.0117 -0.3340 * -0.0723 * -0.0434 -0.0387 -0.0349

Loans Plus Securities >= Five years 0.0225 0.0033 0.0730 0.0743 0.0807 0.0792 0.0798 0.0856 0.0863 0.0708 0.0208 0.0473 0.0345

Log Likelihood -584.9 -662.5 -561.8 -328.8 -505.8 -597.7 -660.9 -708.4 -416.7 -339.3 -315.9 -348.5 -410.6

Number of Observations 866 897 713 413 607 717 781 843 495 377 341 375 428

Akaike Information Criteria 1289.8 1445.0 1243.6 777.6 1131.6 1315.4 1441.8 1536.8 953.4 798.5 751.7 816.9 941.3

Pseudo R squared 0.090 0.222 0.170 0.195 0.162 0.159 0.144 0.145 0.172 0.121 0.102 0.103 0.078

Significance at 1%, 5% and 10% levels are indicated by ***, **, and * asterisks, respectively.
47

Table 6

Multinomial Logit Regressions of Determinants of Bank State: Merger versus Failure 

Estimation Period (Years)

Explanatory Variables 1990 1991 1992 1993 1993-94 1993-95 1993-96 1993-97 1994-98 1995-99 1996-00 1997-01 1998-02

Intercept

Constant -4.4684 9.6836 ** -7.5929 -5.191 4.203 4.6942 4.5547 4.7692 11.3291 -5.1144 0.9346 -0.6199 -1.9189

Capital

Tangible Equity for PCA 0.5301 ** 0.8500 ** 0.7454 ** -0.0461 -0.1382 -0.0093 -0.0002 -0.0191 1.4089 0.2249 -0.0238 -0.0080 -0.0014

BHC Capital Injections 2.1647 -0.1048 0.3075 1.0384 0.7004 0.5830 0.5993 0.5579 -3.1430 -0.8561 -0.5000 -0.7831 * -0.5443

External Capital Injections -0.9151 -0.0810 5.0893 ** 0.8027 0.9729 1.3522 1.3519 1.3907 -1.6622 ** -1.2036 ** -0.5297 * -0.4764 * -0.4560 *

Asset Quality

Loans Past Due 30 to 89 Days -0.6823 ** -0.4077 ** -0.2239 -0.8464 ** -0.4781 ** -0.1863 -0.2035 -0.1915 0.6182 0.3477 0.5696 0.2731 0.2128

Loans Past Due 90 Days or More -0.3236 -0.0706 -0.5440 -0.1931 -0.2134 -0.5354 ** -0.5049 ** -0.5198 ** -0.9865 ** -0.3893 * -0.2086 -0.1001 0.0566

Nonaccrual Loans and Leases -0.2397 -0.4158 ** -0.3294 ** -0.7045 -0.6407 ** -0.5988 *** -0.6019 *** -0.6018 ** -2.9516 ** -0.4764 * -0.1760 -0.0955 -0.1115

Other Real Estate Owned -0.1902 * -0.4054 ** 0.2594 0.3367 0.3322 0.2715 0.3054 0.3055 7.6746 ** 0.5882 0.0078 -0.0945 -0.1525

Allowance for Loan Losses 0.3356 0.8715 ** 0.3666 2.6342 ** 1.2752 ** 0.9467 ** 0.9844 ** 0.9958 * 4.3713 0.5819 0.7722 0.8808 0.8598

Management

Efficiency Ratio 0.0094 -0.0345 -0.0171 0.0437 -0.0179 -0.0297 -0.0320 -0.0379 -0.0522 0.0580 0.0218 0.0396 0.0435

Earnings

Total Interest Income 1.1992 -1.5633 ** 0.5040 2.2421 -0.1898 -0.5105 -0.3677 -0.3640 -3.3443 0.2597 0.5499 0.6946 0.9715

Total Noninterest Income 0.7799 -1.4107 ** 0.2819 2.5038 ** 0.5521 0.1408 0.1259 0.0759 -2.6277 0.6042 0.4134 0.6291 0.6470

Total Interest Expense -0.9474 0.7870 -0.4657 -0.8863 0.7604 0.6581 0.4352 0.4075 -0.3228 -0.5881 -0.7982 -1.1075 -1.3550

Loan-loss Provision 0.1164 -0.1086 0.0177 ** -1.0359 -0.6691 -0.5882 -0.6731 ** -0.7569 ** 0.2790 -0.6447 -0.7495 -0.5020 -0.2115

Loan Chargeoffs -0.0145 0.0398 -0.7365 ** 0.8755 0.3109 0.3865 0.3321 0.3975 1.0550 0.7349 0.5245 0.3932 0.1833

Expenses on Premises -0.8320 1.1644 -1.5325 -1.7446 -0.2103 -0.4991 -0.4863 -0.3517 -1.2111 -1.3686 -1.4539 -2.0358 -2.7002

Salaries -1.7712 * 1.4012 * 0.8435 -1.5131 -0.0220 0.2606 0.1441 0.1638 8.1622 -0.6658 -0.5979 -0.7600 -0.4629

Other Noninterest Expense -0.2371 * 0.6104 ** -0.0030 ** -4.1409 -1.3090 -0.4806 -0.4177 -0.3441 0.2520 -0.8389 -0.6434 -0.8411 -1.0175

Liquidity

Volatile Liabilities -0.0085 -0.0177 0.0025 -0.1507 * -0.1159 -0.0679 -0.0431 -0.0379 -0.3553 ** -0.0813 * -0.0253 -0.0431 -0.0287

Loans Plus Securities >= Five year 0.0159 0.0091 0.0927 ** 0.0593 0.0729 0.0791 0.0805 0.0855 * 0.1157 0.0861 0.0125 0.0277 0.0228

Log Likelihood -584.9 -662.5 -561.8 -328.8 -505.8 -597.7 -660.9 -708.4 -416.7 -339.3 -315.9 -348.5 -410.6

Number of Observations 866 897 713 413 607 717 781 843 495 377 341 375 428

Akaike Information Criteria 1289.8 1445.0 1243.6 777.6 1131.6 1315.4 1441.8 1536.8 953.4 798.5 751.7 816.9 941.3

Pseudo R squared 0.090 0.222 0.170 0.195 0.162 0.159 0.144 0.145 0.172 0.121 0.102 0.103 0.078

Significance at 1%, 5% and 10% levels are indicated by ***, **, and * asterisks, respectively.
48

Table 7

Multinomial Logit Regressions of Determinants of Bank State: Remain a Problem versus Failure 

Estimation Period (Years)

Explanatory Variables 1990 1991 1992 1993 1993-94 1993-95 1993-96 1993-97 1994-98 1995-99 1996-00 1997-01 1998-02

Intercept

Constant -1.7037 9.8229 *** -5.7298 -8.1433 3.4994 1.9090 2.3718 2.9545 11.8183 -4.7010 2.4569 -0.1438 -1.3300

Capital

Tangible Equity for PCA 0.6653 0.9255 0.6366 -0.0180 -0.1213 0.0157 0.0210 0.0161 1.4656 0.2476 0.0092 0.0247 0.0209

BHC Capital Injections 1.8868 -0.2906 0.5581 0.8628 0.4019 0.2479 0.2982 0.3084 -3.3388 -0.8366 -0.3956 -0.6834 -0.6538 *

External Capital Injections -0.2729 0.1045 5.0410 ** 0.4656 0.9132 1.4138 1.4588 1.4872 -0.9185 -0.3014 -0.1564 -0.1621 -0.1645

Asset Quality

Loans Past Due 30 to 89 Days -0.1508 * -0.1852 * -0.0386 -0.6301 -0.2901 -0.0618 -0.0480 -0.0389 0.6652 * 0.3108 0.5140 0.1504 0.1100

Loans Past Due 90 Days or More -0.1451 -0.0449 -0.2691 -0.1211 0.0590 -0.0202 -0.0798 -0.0852 -0.6682 -0.2518 -0.1519 -0.0492 0.0091

Nonaccrual Loans and Leases -0.0503 -0.0609 -0.2043 -0.5441 -0.4911 *** -0.4661 *** -0.5049 *** -0.5101 *** -2.9075 *** -0.5721 -0.3620 -0.2440 -0.2381

Other Real Estate Owned -0.1520 * -0.0992 0.3269 0.5932 0.5444 ** 0.5269 ** 0.5422 ** 0.5437 ** 7.8727 0.7603 0.1889 0.0840 -0.0439

Allowance for Loan Losses 0.2190 0.4678 ** -0.1398 2.0740 0.6592 0.3662 0.4775 0.4219 3.8584 0.5901 0.8449 0.7692 0.8030

Management

Efficiency Ratio -0.0094 -0.0479 ** -0.0088 0.0608 -0.0183 -0.0160 -0.0218 -0.0298 -0.0504 0.0616 0.0153 0.0351 0.0417

Earnings

Total Interest Income 0.5102 -1.4084 ** 0.8538 2.3853 -0.2317 -0.2204 -0.1554 -0.2602 -3.2794 0.3022 0.3374 0.6819 0.9719

Total Noninterest Income 0.0929 -1.6561 *** 0.4092 2.3166 * 0.4836 0.2710 0.2199 0.1216 -2.5602 0.6202 0.2145 0.5437 0.6399

Total Interest Expense -0.4149 0.7310 -0.8402 -1.6448 0.4186 0.0461 -0.0415 0.0889 -0.4817 -0.5840 -0.6594 -1.1102 -1.3588

Loan-loss Provision -0.5443 * -0.2876 0.0509 -0.8631 -0.5969 -0.4833 -0.5775 * -0.5975 * 0.4569 -0.4709 -0.8459 -0.6286 -0.3183

Loan Chargeoffs 0.5898 * 0.2622 -0.6566 * 0.8309 * 0.5432 0.6234 0.5602 0.5720 1.3316 0.6531 0.6585 0.5533 0.3056

Expenses on Premises -0.0152 1.0657 -1.8408 -1.8289 -0.4222 -0.9661 -0.7504 -0.6864 -1.6630 -1.7840 -2.0767 -3.3064 * -3.3969 **

Salaries -0.4521 1.5279 *** 0.6316 -1.4041 0.2488 0.3225 0.2618 0.3786 8.4899 -0.3599 0.0343 -0.2674 -0.2008

Other Noninterest Expense 0.1244 0.8622 ** -0.4144 -3.9178 ** -1.0867 * -0.6549 -0.5648 -0.4626 0.1029 -1.0150 -0.5520 -0.6832 * -0.9796

Liquidity

Volatile Liabilities -0.0006 0.0166 0.0404 -0.0821 -0.0456 -0.0061 0.0126 0.0192 -0.3160 * -0.0456 -0.0169 -0.0090 -0.0073

Loans Plus Securities >= Five years 0.0273 0.0089 0.0877 ** 0.0966 0.1024 * 0.1027 ** 0.0997 ** 0.1050 ** 0.1040 0.0777 0.0213 0.0394 0.0348

Log Likelihood -584.9 -662.5 -561.8 -328.8 -505.8 -597.7 -660.9 -708.4 -416.7 -339.3 -315.9 -348.5 -410.6 Number of Observations 866 897 713 413 607 717 781 843 495 377 341 375 428

Akaike Information Criteria 1289.8 1445.0 1243.6 777.6 1131.6 1315.4 1441.8 1536.8 953.4 798.5 751.7 816.9 941.3

Pseudo R squared 0.090 0.222 0.170 0.195 0.162 0.159 0.144 0.145 0.172 0.121 0.102 0.103 0.078

Significance at 1%, 5% and 10% levels are indicated by ***, **, and * asterisks, respectively.
49

Table 8

Means Ratio: 1990 and 1995–99 Panel 

(Percentage of assets)

Variable Mean Mean

Capital

Tangible Equity Capital 

Capital Injections: 

From BHC

Outside

Asset Quality

Past-Due Loans (30-89 

days)

Past-Due Loans (90+ days) 

Nonaccruing Loans

Other Real Estate Owned 

Loan Charge-offs 

Allowance for Loan Loss

Management

Efficiency Ratio

Earnings

Interest Income 

Noninterest income

Interest Expense 

Loss Provision 

Salaries

Expenses on Premises 

Other noninterest expense

Liquidity

Volatile Liabilities 

Loans and Sec. > 5 years

1990

5.3409

0.1399

0.1888

2.2262

0.7964

2.6136

2.6808

1.6613

1.7465

93.5429

9.6398

1.2205

5.8164

1.6035

1.9194

0.6952

2.0764

14.4676

65.1021

1995-99

7.297

0.2176

0.6267

2.5514

1.1164

2.5111

1.4720

1.7754

1.9878

93.5453

8.2684

2.1216

3.4738

1.7133

2.7150

0.8492

2.8551

14.2472

68.6646
50

Table 9

Effects of 1 Percentage Point Change

in Selected Variables on Predicted Probability

1990

Increase in Loans Increase in

Past-Due 90 Days Nonaccrual Loans

At Mean to 1.7964% to 3.6136%

Recover 16.41 % 12.52 % 14.29 %

Merge 1.82 1.59 1.55

Remain a problem 80.82 84.74 83.13

Fail 0.95 1.15 1.02

1995--99

Increase in Loans Increase in

Past-Due 90 Days Nonaccrual Loans

At Mean to 2.1164% to 3.5111%

Recover 43.22 % 36.45 % 42.14 %

Merge 6.61 6.53 7.30

Remain a problem 49.93 56.65 50.12

Fail 0.25 0.37 0.45