FDIC Banking Review Troubled Banks: Why Don't They All Fail? by Robert Oshinsky and Virginia Olin*
A wealth of literature examines the
determinants of bank failures and of bank mergers or consolidations. Also numerous are studies that develop
failure-prediction models and early-warning systems. But both groups of studies use samples of all
banks, and therefore most of this research focuses on pairs of outcomes:
failure versus nonfailure, merger versus consolidation,1 or problem
bank versus nonproblem bank. But in
reality, future status is more than a binary choice.
Here we study only troubled banks—banks
that receive a composite CAMELS rating of either 4 or 5 when examined. 2 A focus on troubled banks is valuable to the
FDIC and bank researchers for four reasons.
First, when a bank is troubled, failure is but one possible outcome;
alternative outcomes are recovery, merger, or continuation as a problem. Second, between 1990 and 2002, 96 percent of
all banks that failed had first been troubled banks. Including nonproblem banks would add bias
towards non-failure as a possible outcome since a vast majority of nonproblem
banks do not fail. Third, if the FDIC
can better predict the number of troubled banks that will not fail, it will be
better able to estimate the size of its contingent loss reserve. 3 Finally, development of 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.
As noted, troubled banks have four
possible outcomes: recovery, merger, continuation as a problem, and
failure. Knowing the future states of
banks in our sample,4 we construct a financial profile using
pre-examination data for the banks grouped by their future state and then use
these profiles to develop a multinomial logit estimating procedure that
predicts a bank’s likely future state.
We show that a four-state model predicts failure at least as well as
binary failure-prediction models and, in addition, provides predictive ability
for three alternative future states.
The next section describes previous
empirical studies of bank failures, mergers, and financial distress. The three subsequent sections discuss the
methodology we use, the sample and data, and the results. The concluding section gives a brief summary.
Most of the numerous studies that
examine the determinants of bank failures and bank mergers and consolidations,
like most of the numerous studies that develop early-warning systems predicting
deterioration in banks’ financial condition, construct financial ratios based
on information in the Consolidated Reports of Condition and Income (Call
Reports) that banks file quarterly with the FDIC. 5 The idea is to construct financial ratios
that closely resemble the CAMELS rating system used by bank examiners and to
use the ratios to predict pairs of outcomes: failure versus nonfailure, merger
versus consolidation, or problem bank versus nonproblem bank. 6 While the information used by examiners is
preferable, it is not available without an examination, unlike Call Report
data, which are readily available quarterly.
Only a few studies have extended this
research beyond pairs of outcomes. In an
effort to improve predictive accuracy, DeYoung estimates the long-run
probability of failure and acquisition in de novo banks by defining three
states: failure, merger by acquisition, and conversion of a whole-bank
affiliate of a bank holding company to a branch bank of that same bank holding
Wheelock and Wilson use a competing-risks model to consider the joint
determination of the probability of being acquired, failing, or surviving. Hannan and Rhoades predict that a bank may
experience one of three outcomes: not be acquired, be acquired by a firm
operating within the bank’s market, or be acquired by a firm operating outside
the bank’s market. 7 DeYoung
expects that including the other two exit states (merger by acquisition, and
conversion) will make the failure estimates more accurate. 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 types of mergers) yields a
number of firm and market characteristics that earlier studies did not yield
and that significantly influence the likelihood of acquisition.
Rarely in life are the possible
outcomes binary. While studies that
examine two-state outcomes have benefit, much is lost by not studying other
possible outcomes. Further, by limiting
our universe to only problem banks, we are able to remove the inherent bias
towards the prediction of non-failure status.
Referencing previous studies, we
select certain financial variables proven in binary models to be useful in
determining future bank state. In our
multistate model we use univariate trend analysis to determine whether
prior-period financial characteristics differ by future bank state.
Specifically, in the existing
literature on bank performance the reasons suggested for failure include low
capital, risky asset portfolios, poor management, low earnings, and low
liquidity. Thus, the financial variables
we select are from those same broad categories: 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
(merged or acquired, remained a problem, and failed).
We use a multinomial 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 three 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-of-sample forecasting accuracy for each of the four states with the actual
number of banks ending up in each state.
We compare two competing binary models with the multistate model for
failure predictions: one of the two competing models uses the same variables
that our multistate model uses, and the other uses the same explanatory
variables that Jarrow et al. use, referred to as the loss-distribution model. 8 These two comparisons allow us to test
whether including additional alternative states improves the accuracy of
failure estimates over the accuracy of 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.
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 does one of the following: recovers (improves to a CAMELS
rating of 1, 2, or 3 at the next examination), merges (either merges with a
bank outside of its multibank holding company or consolidates within its
multibank holding company),9 remains a problem bank (continues to
have a CAMELS rating of 4 or 5 at the next examination), or fails. 10 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 observation is
dropped from the sample. The reason for
this restriction is twofold: first, we want to allow enough time to pass for
changes in financial condition to occur; second, during most of our period, all
banks except those with assets under $250 million and a composite CAMELS rating
of 1 or 2 were required by law to receive safety-and-soundness examinations
annually. 11 As noted above, a
pairing of events is considered one observation in our sample. Our sample consists of 3,747 observations.
To control implicitly for the effects
of economic conditions and banking legislation, we divide these 3,747
observations into annual cohorts corresponding to the year of the first
event. But because second events usually
occur in a different year from the first events, 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. 12 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.
If, at the second event, the bank upon reexamination continues as a
problem bank, the first observation ends with an outcome of continuation as a problem
bank, and 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 is determined for that second observation. In contrast, when a bank’s first observation
ends with a second event of recovery or merger, the bank 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), whereupon the bank would be considered a new
observation. Banks cannot appear in
cohorts after they fail or merge.
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) and the 1997 cohort the lowest
(62). Figure 2 shows that the 1990 and
1998 cohorts have the highest percentage of problem banks that fail—5
percent—and that in the 1997 and 2002 cohorts, no problem banks fail before the
second event. Throughout the period most
banks remain a problem at the second event: the range is 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 (figure
2). Moreover, for banks that recovered
we found that large increases in internal capital injections (as a percentage
of average assets) peaked in 1996. For
those banks, external capital injections increased sharply from 1994 to 1995
but did not peak until 1999 (figure 3). 13 Figure 4 shows that most banks that remain a
problem at the second event ultimately recover. 14
Using data from the Call Reports, we
calculate beginning and ending event financial ratios for each bank. The beginning event ratios are calculated
from the Call Report filed just before the first event and the one filed 12
months previously. 15 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 event, 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
at the beginning. We compute the
percentage of banks in each state that showed 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 percentages
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 banks with 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. For the various financial ratios, we have no
expectations for the ranking of banks that merge or are still a problem except
that their increases or decreases will fall between the levels for banks that
recover and banks that fail.
For measures of earnings, we compare the
percentage of banks in each state that showed increases in net interest income,
in net noninterest income, and in provision for loan losses. For measures of risky asset portfolios, we
compare the percentage of banks in each state that showed increases in 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. For measures of capital adequacy we compare
the percentage that showed average risk-based capital and average tangible
equity capital; for measures of liquidity, the percentage that showed average
volatile liabilities and loans and securities with maturities greater than or
equal to five years. 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 selected
almost the same financial variables that we used in the univariate trend
analysis. 16 For measures of
the economy, we added capital injections from a bank holding company and
capital injections from outside. 17
From the univariate trend analysis we are able to form expectations
about the sign that coefficients on these variables will take when they are
estimated by 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. The financial ratios associated with not
failing are capital, capital injections, allowance for 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 for the efficiency ratio’s
coefficient (because lower is better), we associate this ratio, too, with not
failing. The financial ratios associated
with failure are those measuring poor asset quality (past-due 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
Our results from both the univariate trend
analysis and the multistate logit estimating procedure generally agree with
expectations. Of banks that recover, the
percentage with increases (between the beginning and ending periods) in
performance ratios such as net income and net noninterest income is greater
than the percentage of banks 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 is smaller for
banks that recover than for banks in any alternative state. These results can be seen graphically in the
In the logit analysis, we find that
increases in financial ratios associated with nonfailure have positive
coefficients, and increases in financial ratios associated with failure have
Analysis of Variance
Results from the analysis of variance,
reported in table 2 and table 3, complement the results of the univariate trend
analysis. Both tables use the
beginning-period data. Table 2 shows the
mean and standard errors for financial variables in each of the four
states. 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
The results reported in table 2 show
that at the first event, the mean value for each financial variable is
statistically different from zero. The
results also show that the mean values in financial ratios associated with not
failing are generally larger for banks that recover, merge, or remain a problem
than 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 with
one of the alternative future states.
Or, turning the statement around, we can say that 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
The results reported in table 3 show
that except for two variables (capital injections from the bank holding company
and from outside), the difference in means between banks that recover and those
that fail is statistically significant.
Also significant for most variables are the differences in means between
banks that fail and both banks that merge and banks that remain a problem.
The results from table 3 indicate as
well that the differences in means between banks that remain a problem and both
banks that merge and banks that recover are statistically significant. For the recovery-versus-merger pairing, fewer
variables are statistically different from one another. And for the pairing remain-a-problem versus
merge, even fewer variables are statistically different from one another.
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
For a number of reasons, we did not
include variables for economic condition in our model. First, Nuxoll et al. found that state and local
economic data did not contribute to the performance of standard off-site
models. 19 Second, much of the
literature theorizes that the economy is subsumed in the balance sheet, so any
effect of the economy will already have shown up in the financial data. 20 Finally, we included capital injections as a
proxy for changes in the economy (see footnote 13).
We used a stepwise logit estimation
procedure to identify the terms that have a significant relationship in
predicting the likelihood that a bank will end up in one of the four
states. The stepwise estimation
procedure allows us to include several measures of the same attribute in the
logit model, yet isolates the most important factors in terms of predicting
future bank state.
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. 21
We estimate the logits for each of our cohorts, 1990 through 2002. However, because of the small number of
failures after 1993, 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 through 2002 models, we use a
panel of the most recent previous five years.
Table 5, Table 6 and Table 7 show the
results. The reference state is failure,
so the coefficients are interpreted relative to failure. As mentioned above, a negative coefficient
means that an increase in a variable will have the result that the future state
relative to failure becomes less likely.
Eight of the findings are fairly
interesting. First, more recent cohorts
(beginning with the 1995–1999 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. 22
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
1997–2001 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 either recovery or continuation as a problem
becomes less likely relative to failure.
On the other hand, for the cohorts
1994–1998 through 1998–2002, capital injections from outside are
significant—but have the unexpected sign both for table 6 (merger) and, in two
of those four cohorts, for table 5 (recovery).
Perhaps the negative sign indicates that the institution expects to be
acquired and either does not or cannot raise capital. As mentioned in footnote 13, the Riegle-Neal
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 other two future states.
for all cohorts and in each future state, asset-quality variables are
statistically significant more often than other variables (table 5, table 6, and table 7). Moreover, the variable nonaccrual
loans and leases is more often statistically significant than 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 than nonaccrual
loans and leases. Further, these
asset-quality variables are often negative (as expected from table 1), a sign
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 table 6, nor is it significant in any year in table 7 (continuation as a problem). 23
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 1994–1998
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 this ratio uses
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 1993–1997 and 1994–1998 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, among 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
Finally, the most startling result is
in the 1994–1998 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 lies in the small number of failures and the particular nature of
the banks in the 1994–1998 cohort. The
number of failures fell from 10 in the 1993–1997 cohort to 7 in the 1994–1998
cohort, and one of the failures in 1998 was a bank that failed because of
fraudulent activity and therefore had a very low amount of other real estate
owned—perhaps low enough to skew the model.
For example, the mean of other real estate owned (as a percentage of
assets) for failed banks in the 1993–1997 cohort equaled 3.0 percent; for the
1994–1998 cohort, the mean dropped to 0.62 percent.
Prediction of State: Out-of-Sample Results
The true measure of the logit model’s
contribution is its accuracy in making out-of-sample predictions. To test the accuracy, we forecast future bank
states using prior-period 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 explanation of observations in the section above on sample and
data). To determine all predictions of
state, we summed predicted probabilities of state for the cohort, deriving the
expected number of banks in each future state.
Figures 5 through 7 show the results.
All three figures show that the number
of banks predicted for each state is very close to the number of banks that
actually ended up in the state. For
1996, for example, figure 5 shows that the logit predicts 27 banks to recover,
and 26 banks actually recovered; figure 6 shows that 5 banks are predicted to
be acquired, compared with 3 that actually were; and figure 7 shows that 26
banks are predicted to remain a problem, compared with 29 that actually did
remain a problem.
To test 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 failure versus nonfailure. We then compare the predicted probabilities
of failure from this binary model with predicted probabilities of failure from
the multistate model.
The first comparison is shown in
figure 8, which 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 9, which compares 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 9). The predictions resulted from the use of the
betas estimated in the 1993 LDM.
However, 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
Report 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 prediction that six banks would fail in 1994 resulted from the use
of estimated betas from our 1992 model (figure 9). 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 9 shows, the
two models are comparable. The advantage
of ours is that we can predict not only problem-bank failure but also problem-bank
recovery, merger, and continuation as a problem.
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
failure. We then divide the predicted
failures into deciles to determine whether the highest decile contains the
largest number of banks that actually failed.
Of the 65 failed banks in our cohorts from 1991 through 2002, 38 were in
the tenth decile. 24 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 percentage 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
Despite the weaker results for the
remaining three states, the accuracy in forecasting future bank states is
noteworthy. Arguably, this model could
be used to predict troubled banks’ future state with decent reliability.
To test the economic significance of
the explanatory variables, we use a fairly standard approach: first we evaluate
in-sample predicted state probabilities on the basis of the mean values of
explanatory variables, and then we evaluate 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 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.
We offer an approach that differs from
that of previous failure-prediction models by focusing on troubled banks
only. As a result, we can estimate a
model that predicts failure as well as the three alternative outcomes to
failure: recovery, merger, and continuation as a problem. Further, this model predicts failure as
successfully as the standard binary failure-prediction model.
For the deposit insurer, this
four-state model offers more information about the possible future states of
problem banks than a two-state model can.
This additional information can better assist the FDIC in long-term
strategic planning regarding problem banks.
This planning involves budgeting personnel, time, and funding. Moreover, our model shows that certain
explanatory variables affect future bank state.
This knowledge may help regulators choose policies that affect the
likelihood that troubled banks can successfully resolve their own
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* Robert Oshinsky is a Senior Financial Economist and Virginia Olin is a former Senior Financial Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation (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 article and their valuable comments and suggestions. The authors also thank Robert DeYoung 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 FDIC.
1 In previous studies, a merger is the absorption of a bank by a previously unrelated bank while consolidation is the absorption of a bank by a related bank. For purposes of this paper, we combine the two types of absorptions and refer to them as mergers.
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 during the resolution process. CAMELS is an acronym for the six components of the regulatory rating system: Capital adequacy, Asset quality, Management, Earnings, Liquidity, and (since 1998) market Sensitivity. Banks are rated from 1 (the best) to 5 (the worst), and banks with a composite rating of 4 or 5 are considered problem banks. A rating of 4 generally indicates that the bank exhibits unsafe or unsound practices or is in an unsafe or unsound condition, while a rating of 5 means that the bank's practices or condition are extremely unsafe or unsound.
3 The mission of the FDIC 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 FDIC's Financial Risk Committee quantifies risks to the deposit insurance system for purposes of financial reporting and fund management, and each quarter it meets to set a contingent loss reserve estimated from total assets of banks that may fail within two years.
4 Our sample consists of institutions assigned a CAMELS rating of 4 and 5 between 1990 and 2002. The sample is described in detail in a later section of this article.
5 For reviews of the literature, see Demirgüç-Kunt (1989), Jagtiani et al. (2003), and King et al. (2005).
6 See Whalen (1991), Cole and Gunther (1998), Kolari et al. (2002), and Jagtiani et al. (2003).
7 DeYoung (2003), Wheelock and Wilson (2000), and Hannan and Rhoades (1987).
9 We recognize that mergers and consolidations have different characteristics. However, during the period we study, the number of consolidations is small (39), so we combined both events into one state.
10 Here we define failure either as a closing that results from an action by a regulator or as a merger assisted by the FDIC.
11 The frequency of examinations was set by the Federal Deposit Insurance Corporation Improvement Act of 1991 (FDICIA). Further exceptions to the requirement for annual safety-and-soundness exams are listed in the act.
12 Technically a bank may appear as a second observation in the same cohort since the window for the second event is as short as six months.
13 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 interest-rate environments; (2) in 1994 the Riegle-Neal Interstate Banking and Branching Efficiency Act 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.
14 The reason for the decline beginning in 2001 in the percentage of still-a-problem banks that ultimately recover is that enough follow-up events have not yet occurred. Most banks remain a problem beyond a second event.
15 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 article. The resulting trends in data were similar to the trends reported here.
16 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 here.
17 As pointed out in footnote 13, the economy (a record-breaking bull market) was one reason noted for increased acquisitions.
18 Volatile liabilities are defined in the FDIC data dictionary as (1) federal funds purchased and sold under agreements to repurchase, (2) demand notes issued to the U. S. Treasury and other borrowed money, (3) time deposits over $100,000 held in domestic offices, (4) foreign-office deposits, (5) trading liabilities less trading liabilities' revaluation losses on interest rate, (6) foreign exchange rate, and (7) other commodity and equity contracts.
20 As a robustness check, we estimated the model using three variables for the United States 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 number by state and the percentage change in state housing permits; the second used the ratio of the assets of problem banks to total assets by state and the percentage change in housing permits. These variables were significant for most of these estimations. However, when these variables were included in estimations with the rest of the explanatory variables, their significance disappeared.
21 As with the univariate analysis, we also ran the logits using a beginning period one quarter before the quarters specified previously. The results revealed little difference.
22 From the 1995-1999 cohort through the 1998-2002 cohort, the number of failures totaled 7, 7, 8, and 8, respectively, compared with 45, 36, and 17 for the cohorts 1990 through 1992.
23 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.
24 For the binary model, 39 of the 65 failed banks were in the tenth decile.