Do Local Economic Data Improve Off-Site Bank-Monitoring Models? by Daniel A. Nuxoll, John O’Keefe, and Katherine Samolyk*
Researchers at U.S. bank regulatory agencies
developed several types of statistical models to monitor potential problems at
individual banks off-site (that is, without having to visit bank
premises). These off-site monitoring
models tend to be “unconditional” forecasting models that use available data on
a bank’s current and past condition to predict its future condition; they do
not require the user to “condition” the forecast on assumptions about the
future values of any of the variables in the model.
Generally the models attempt to predict one
of two phenomena: either that a bank will fail or that its condition has
deteriorated enough that it will receive a downgrade in its supervisory rating
(composite safety-and-soundness rating) during the next on-site
examination. Although most models use
fairly standard measures of banking conditions, variables describing conditions
in the broader economy in which banks operate have not been important features
of the models.1 And whereas
historical episodes of regional recessions and banking-sector difficulties have
been studied, the contribution of economic data in forecasting future bank
distress has received relatively little attention in empirical banking
Improving off-site monitoring capabilities would enable regulatory
agencies to allocate supervisory resources more efficiently and intervene more
promptly and would reduce the costs associated with bank failures.
For these reasons we investigate the extent
to which state-level economic data could be used to improve the performance of
standard types of statistical models that forecast a bank’s condition
off-site. Specifically, we focus on the
linkages between economic conditions and problems of bank performance between
the mid-1980s and the early 1990s—a period characterized by significant
regional disparities in both banking-sector and broader economic
conditions. The national economic
expansion that followed the recession of the early 1980s was an uneven one:
agricultural and oil-producing states alike experienced local economic problems
and serious banking-sector difficulties.
In addition, the national recession of the early 1990s was largely
concentrated along the coasts and was linked to bank failures in New England
and California. Since the early 1990s
the U.S. banking industry has consolidated into larger, more geographically
diverse institutions, so one might argue that the industry is now less
vulnerable to local economic conditions of the type experienced in the 1980s
and early 1990s. Nonetheless, for
thousands of small U.S. banks, linkages between local economic conditions and
bank performance are likely to remain significant.
empirical strategy is to take variables measuring economic conditions in the
state where a bank is located and add them to statistical models that attempt
to identify institutions likely to experience financial difficulties.
We study the contribution of state-level
economic variables in three types of forecasting models—specifically, those
that forecast bank failures, those that forecast changes in the quality of bank
assets, and those that forecast risky bank growth (as indicated by supervisory
rating downgrades). The sole criterion
for success is whether these variables improve the accuracy of forecasts.
of preview, the addition of state-level economic variables generally does not
improve upon the forecasts generated by models using only data on a bank’s
condition. Indeed, the models
forecasting bank failures and changes in the quality of bank assets perform
about the same or worse when state-level economic variables are included.
The models predicting risky bank growth,
however, show a more consistent, albeit modest, improvement.
These findings do not imply that economic
conditions are unimportant for a bank’s performance.
Rather, as we discuss in the conclusion, it
is possible that factors not considered in our models contribute to this
finding of no, or little, predictive improvement.
next section discusses the conceptual link between state-level economic data
and bank performance. The subsequent
three sections present the results of incorporating state-level economic data
into models forecasting the three aspects of bank performance that we focus on
(failures, changes in asset quality, and risky growth).
The final section presents our conclusions
and discusses the implications of our findings for future research on bank
Conceptual Link between Local Economic
Conditions and Bank Performance
the purpose of our study is to investigate whether local economic variables can
improve the ability of statistical models to forecast which banks will
experience difficulties, we judge the success of each model in terms of the
accuracy of its forecasts relative to the forecasts of an otherwise equivalent
model that does not include the economic variables.
Before we turn to the models we develop,
however, it will be helpful to discuss the conceptual link between local
economic conditions and bank performance.
Some theories posit that the main comparative advantage of banks
relative to other financial firms lies in banks’ information about and
expertise in lending locally. This
advantage is viewed as particularly important for smaller, more-localized
banking institutions. In making its
lending decisions, bank management must address the risk that local economic
conditions will affect the profitability of local borrowers and the subsequent
performance of loans granted to those borrowers.
Bank lending tends to move procyclically as
borrowers seek to fund profitable business opportunities in economic expansions
and to retrench during economic downturns.
Once loans are issued, a bank’s profitability and credit quality will
depend to some extent on the economic fortunes of its borrowers.
Indeed, when economic conditions change
dramatically, we expect to find a correlation between these conditions and the
likelihood that a bank will fail.3
Thus, when local economic conditions vary substantially, we expect to
find some relationship between these variations and the performance of local
banks. And because profitability and
asset quality are key factors affecting bank supervisory ratings, we also
expect to see a link between local economic conditions and the on-site examination
ratings received by institutions—all other things being equal.
all other things may not be equal. The
relationship between local economic conditions and a bank’s performance also is
affected by the management of the bank.
Differences in credit cultures, lending strategies, underwriting
standards, and asset-and-liability management will lead to differences in the
exposure of institutions to local economic developments.
We expect that “better-managed” banks will be
able to weather local economic downturns better than poorly managed banks.
Because management is so important to a
bank’s success, it receives particular attention during on-site
safety-and-soundness examinations. The
summary, or composite, safety-and-soundness rating (CAMELS rating) reflects not
only the bank’s current profitability, asset quality, and capital adequacy but
also the soundness of the bank’s current management.4
The linkages among the local economic
conditions a bank faces, its management policies, its profitability and asset
quality, its on-site composite safety-and-soundness examination rating, and its
survival are depicted in figure 1.
Despite the multiplicity of
factors at play, banks operating in poorly performing economies are nonetheless
more likely to perform worse than banks in healthier environments.
This suggests that local economic data have
the potential to improve the performance of the statistical models used for
identifying banks that are likely to experience problems.
Whether these data do improve the models’
performance is ultimately an empirical question.
But the fairly dramatic regional differences
in U.S. economic conditions and bank performance during the 1980s and early
1990s present a good opportunity to study this question (especially given the
regulatory structure of the industry at the time, and in particular the
interstate banking restrictions that to a large degree delineated banking
activities along state lines).
number of considerations influenced our decision to investigate the usefulness
of state-level economic data in off-site monitoring models.
First, we wanted to use economic variables
that were consistently reported for all regions during the study period.
Second, we wanted to use variables that
would have been available in a timely fashion for inclusion in off-site
monitoring models. Third, we wanted to
use economic data measured for the type of geographic region that reasonably
could be expected to reflect the conditions faced by many banks.
Various data series are available for
counties (or parishes), states, or Census-level divisions, but given our
selection criteria, state-level data seemed the best choice.5
A fair number of data series are available
for all states within a reasonable time frame.6
interstate banking restrictions and state banking laws delineated
banking markets along state lines.
Therefore, for the U.S. banking industry of the 1980s and early 1990s,
state-level economic data seemed to be reasonable measures of the local
economic conditions affecting banks.
Predicting Bank Failures
The first part of our study examines the contribution that
state-level economic variables make when added to standard models predicting
bank failures.7 Patterns in
the state-level data during the 1980s and early 1990s suggest that regional
economic conditions were related to the incidence of bank failure.
More specifically, states experiencing
economic booms followed by busts tended to have high failure rates.
Figure 2 shows this by comparing state
personal-income growth rates and bank-failure rates for Texas and for
Massachusetts. Although there were also
regions where weak economic performance was not followed by high bank-failure
rates, these tended to be regions where the economic weakness had not been
preceded by an economic boom.
Here we look at whether measures of state-level economic
conditions would have helped supervisors identify the institutions that
ultimately failed during the late 1980s and early 1990s.
Taking what have become fairly standard
logistic regression models, we use bank financial data at the beginning of a
period to predict the likelihood that an institution will fail sometime during
a subsequent two-year interval. In these
models, the precise relationships used to assign bank-failure probabilities are
based on the historical relationships observed for failures during the prior
two-year interval. That is, first we
estimate statistical relationships about the conditions preceding failures
during the previous two years, and then we use these relationships to forecast
specific failures during the subsequent two years.
these models generate a failure probability for each bank, one must choose a
critical (or cutoff) probability in order to classify banks as survivors or
failures. For example, a critical
probability of 50 percent indicates that all banks having estimated failure
probabilities greater than 50 percent are classified as “predicted failures.”
Obviously, choosing a lower, more-stringent
critical probability will yield a greater number of predicted bank failures
than will a higher, less-stringent one.
Furthermore, the accuracy of failure-model predictions is measured in
terms of two types of forecast errors that the model can make: one, bank failures
that are not predicted (missed failures); and two, surviving banks are
erroneously identified as failures (missed survivors).
Thus, in choosing a critical failure
probability, a model user faces a trade-off in terms of the types of prediction
errors that will be obtained from the model.
By choosing a lower critical probability, a user can generally reduce
the percentage of missed failures but will increase the percentage of missed
survivors. A more accurate
failure-prediction model is one that gives the user a better trade-off in terms
of these forecast errors. In other
words, given the percentage of missed failures yielded by the user’s cutoff, a
more accurate model will yield fewer missed survivors (and a less-accurate
model will yield more).
Here we report forecast results for two
first period, we use the relationship between bank and state-level economic
conditions as of year-end 1986 and actual failures in the years 1987 and 1988
to predict failures occurring in 1989 and 1990.
For the second period, we use the relationship between bank and
state-level economic conditions as of year-end 1988 and actual failures in the
years 1989 and 1990 to predict failures occurring in 1991 and 1992.
Table 1 lists the variables in the bank failure-
prediction models. As indicated in the
top panel, the basic “banking” model uses fairly standard bank financial data
and supervisory (CAMEL) ratings to predict failure/survival during the
subsequent two years. The statistical
relationships yielded by the models for the subperiods studied here are
generally consistent with those reported by other researchers.
All else being equal, banks with less
capital, more asset-quality problems, and lower supervisory ratings for
management and liquidity are assigned higher projected failure
probabilities. We next examine the
contribution to the basic banking model made by various proxies measuring
state-level economic conditions (see the bottom panel of table 1).
Model results are displayed in figure 3.
solid line in figure 3A illustrates the prediction-error trade-off yielded by
the banking model using actual failures in 1987 and 1988 to predict failure in
1989 and 1990. Here the prediction-error
trade-off is not as good as that depicted in figure 3B.
There is a greater trade-off between
minimizing missed survivors and minimizing missed failures.
The broken line summarizes the predictive
accuracy of the model when measures of state-level personal-income growth are
added to the pure banking model: the addition of the economic data materially
reduces the accuracy of the bank-failure predictions for 1989 and 1990.
The solid line in figure 3B illustrates the prediction-error
trade-off yielded by the banking model using actual failures in 1990 and 1991
to predict failure in 1992 and 1993. The
model predicts fairly well, in the sense that one could have chosen a lower
critical probability (fewer missed survivors) without dramatically increasing
the proportion of missed failures. The
broken line summarizes the predictive accuracy of the model when measures of
state-level personal-income growth are added to the pure banking model: the
economic data do not materially improve our ability at year-end 1991 to predict
evidence about the contribution of state-level economic data in off-site
monitoring models is sparse, our findings are consistent with what has been
reported. The most relevant work in this
area was conducted by researchers at the Federal Reserve System when they were
developing their near-term-prediction Financial Institution Monitoring System
(FIMS) in the early 1990s. These
researchers’ systemwide effort yielded two models that have been modified and
improved over time. The developers of
the FIMS model found that including state-level data on unemployment rates,
personal income, and housing permits did not significantly improve upon
predictions based solely on bank-examination and bank-financial data.8
Changes in the Credit Quality of Bank Assets
the goal of off-site monitoring models is to identify emerging banking
problems, accurate forecasts of bank nonperforming-asset ratios are useful,
insomuch as declining asset quality generally is a precursor of more serious
banking problems. Thus, the second part
of our study investigates whether state-level economic variables would improve
the performance of reduced-form models that predict changes in bank
profitability and asset quality. Here we
report results for models that predict changes in nonperforming-asset ratios.9
with the incidence of bank failure, one can find examples of states where poor
economic conditions have been correlated with higher-than-average bank
asset-quality problems. Figure 4A
illustrates a situation in which the nonperforming-asset ratio of banks in a
state is inversely related to the state’s economic health.
However, one also can find examples of states
where bank asset-quality problems are not clearly related to state-level
economic conditions. As figure 4B shows,
the nonperforming-asset ratio of California banks was high even when the
state’s economy was healthy.10
The nature of bank asset-quality ratios makes them an attractive
candidate to study. First, as discussed
above, the economic conditions affecting a bank’s borrowers should be directly
related to the credit quality of the bank’s loan portfolio.
Second, unlike bank failure (which is a
discrete event occurring only when a bank’s condition worsens beyond some
threshold level), the quality of bank assets is measured in the same continuous
fashion as economic variables; hence, it may exhibit a more systematic
correlation with economic variables.
difference, however, between this part of the study and the first part is that
bank supervisory staff do not currently use “standard” models that forecast a
bank’s profitability or asset quality.
Thus, we begin by using bank financial data from prior periods to
construct reduced-form linear models that predict the change in a bank’s
nonperforming-asset ratios one year forward.
We then include a variety of state-level economic data to see whether
they improve upon the forecasts yielded by the bank financial data.
evaluate the forecasts of asset-quality changes by using a standard summary
measure of a linear model’s prediction error.
The root mean-squared error (RMSE) measures the square root of the
average value of a model’s squared forecast errors.
Forecast errors are squared before averaging
so that negative errors and positive errors count equally, and larger errors
are given more weight.
models we use here, the RMSE summarizes those differences in asset-quality
changes across banks that are not explained by the model.
To put the size of the RMSE in perspective,
we compare each model’s RMSE with the RMSE we obtain when we use only the
historical mean change in nonperforming-asset ratios (no banking or economic
data) to predict future changes.11
U.S. banks vary greatly in size, we want to account for the possibility that
the link between state-level economic variables and nonperforming-asset ratios
could vary with a bank’s size. First,
very large banks (those with assets of more than $20 billion in 1994 dollars)
are excluded from all analyses because they operate in markets that are much larger
than the state in which they are headquartered.
We divide the remaining institutions into five classes based on asset
size in 1994 dollars, and we estimate separate models for each size class.
This allows the measured link between
state-level data and the quality of bank assets to differ for each class of
banks. Table 2 identifies the bank size
Here we report results for models that measure the link between
lagged bank conditions and annual changes in bank nonperforming-asset ratios
during two periods: 1986 through 1989 and 1991 through 1994.12
We assess each of these models in terms of
the accuracy of its out-of-sample predictions of asset-quality changes in the
year following each model’s estimation period—that is, in 1990 and 1995.
In modeling changes in asset quality, we
include lagged values of the bank’s financial variables that are most likely to
be related to the quality of bank assets.13
These measures are identified in the top two
panels of table 3. We then include a set
of economic variables (identified in the bottom two panels of table 3) in what
we refer to as “banking and economic models.”14
5 illustrates the amount of variation in nonperforming-assets-ratio changes
that is not predicted by the models linking past conditions to asset-quality
changes during the previous four years.
All the results we report here include the same sets of banking and
economic variables. Hence differences in
results across specifications can be attributed to the inclusion of the
economic variables, differences in bank size, and differences in the sample
period under scrutiny.
indicated in figure 5A, the reduced-form models using Call Report variables
predict only modest change in bank asset quality during 1990, and the economic
variables do not materially improve upon these forecasts.
Figure 5B shows that historical relationships observed during the early 1990s
do not help predict changes in bank nonperforming-asset ratios during
1995. For this period, the inclusion of
state-level economic variables would have made our prediction errors
In summary, this part of our study indicates that future changes
in bank asset quality are hard to predict even with data on recent trends in
bank asset-quality measures. And state-level
economic data do not generally improve upon these predictions.
These results suggest that, at least for the
periods we study, a reasonable predictor of a bank’s nonperforming-asset ratios
one year forward is the bank’s current nonperforming-asset ratios.
The manner in which a bank grows has important implications for
its overall safety and soundness.
Imprudent or ill-timed growth can lead to risky loan concentrations,
funding problems, or other difficulties for bank management.15
Bank regulators are aware of these
possibilities and have included appropriate safeguards in the supervisory
process. Most relevant to this article
is the FDIC’s growth-monitoring system (GMS), which seeks to identify risky
bank growth ex ante.16 We
propose that economic conditions in a bank’s market might provide a useful
context for assessing the potential risks of bank growth and might therefore
contribute to bank off-site monitoring models.
To see whether our proposal is correct, we next test whether data on
state economic conditions added meaningful information to GMS.17
Before we describe those tests, it is useful to look at the past
correlation between bank safety and soundness (that is, risky bank growth) and
state economic conditions. The U.S.
banking experience of the 1980s and early 1990s suggests that deteriorating
economic conditions were associated with declines in the condition of
banks. Figure 6A illustrates that sharp
increases in state unemployment rates in the southwestern United States during
the mid-1980s coincided with deteriorating banking conditions, as identified
through composite CAMEL ratings of banks.18
(In the figure, positive changes in the
average composite CAMEL rating for the region’s banks indicate a widespread
decline in banks’ safety and soundness because the rating is an ordinal index
that increases in value the poorer a bank’s assessed safety and
soundness.) As indicated in figure 6B,
the correlation between adverse changes in state unemployment rates and
declines in CAMEL ratings was particularly pronounced in the northeastern
Although informative, these simple comparisons do not tell us
whether data on state economic conditions add to off-site growth-monitoring
models. To answer this question, we
develop and compare two off-site growth-monitoring models designed to rank
banks in terms of the relative riskiness of their growth (that is, we designed
two risky-growth indexes). The first
model (“bank model”) serves as our basis of comparison and uses information on
a bank’s portfolio composition, changes in portfolio composition, and
supervisory assessments of bank condition to construct a risky-growth
index. The bank model excludes measures of
state economic activity, however. The
second model (“bank and economic model”) includes all the information the bank
model contains plus measures of state-level economic activity.
The measures of economic activity we test are
quarterly changes in both state unemployment rates and state personal-income
growth. Because our conclusions are the
same for both of these economic activity measures, for brevity we present only
the results of tests that use changes in state unemployment rates.
The premise behind the bank model is that all other things being
equal, the risks to a bank’s future safety and soundness increase when growth
(1) proceeds too quickly, (2) increases the concentration in risky activities,
or (3) increases the reliance on volatile sources of funding.
In addition, it is presumed that the poorer a
bank’s initial condition, the greater the future risks from growth.
As shown in table 4 the bank model uses 11
variables to capture the factors that can lead to risky bank growth.
More specifically, the bank model uses 5
measures of portfolio change: the annualized rates of growth in total assets,
gross loans and leases, the ratio of loans plus securities with maturities of
five years or more to assets, the ratio of volatile liabilities20 to
assets and the ratio of equity capital to assets.
In addition, the bank model uses 4 portfolio
ratios: the ratios of loans plus securities with maturities of five years or
more to assets, volatile liabilities to assets, equity capital to assets, and a
summary measure of portfolio concentration.
The summary measure of loan concentration is used to capture potentially
risky shifts in business activity and is based on the Herfindahl-Hirschman
Index (HHI). To calculate the
concentration measure we first compute the shares of total loans held in 15 well-defined
categories of loans and leases. Next we
square and sum the loan shares.21
Rather than using the raw values of these measures of portfolio change
and portfolio ratios, we use a bank’s percentile ranking for each measure,
based on either a peer group or a national ranking, as appropriate.22
Finally, the bank model includes 2
supervisory measures: a bank’s composite CAMEL rating as of the quarter-end,
and the number of days since the bank’s last on-site safety-and-soundness
examination as of the quarter-end.
The final step in computing the bank-model growth index is to
combine the 11 variables into a summary growth index.
We do this by weighting each variable in
terms of its importance in explaining downgrades in composite CAMEL ratings
during the prior period and then summing the weighted variables.23
The reason we choose this approach is that a
growth index is most useful to bank supervisors if it can be used to anticipate
changes in bank safety and soundness (which is measured by composite CAMEL
banking and economic model, we study the contribution of economic data in
growth monitoring by including state-level economic variables as additional
explanatory variables. This article
presents the results of tests based on the quarterly percentage change in state
unemployment rates. To construct the
bank and economic model growth index, we use the same approach as with the bank
model but add percentage changes in state unemployment rates for the current
quarter and four prior quarters.
stated at the outset, useful risky-growth indexes should anticipate declines in
bank safety and soundness. Hence, to
assess each index’s usefulness, we rank banks on the basis of their growth
indexes and group the ranked banks into “risk” quintiles.
Next we measure the proportion of banks
receiving CAMEL downgrades (during the subsequent three years) in each of the
quintiles.24 For example, we
construct bank-model growth indexes as of year-end 1988 and then compare the
distribution of CAMEL downgrades between 1989 and 1991 across risk
we report results for banks that were examined during five three-year
periods. For each period, we compute
risky-growth indexes (with and without the state economic data) on the basis of
the methodology described above. We then
compare the downgrade experiences of the risk quintiles generated by the bank
model with those of the risk quintiles generated by the bank and economic
model. We measure the contribution of
the state economic variables by comparing the proportion of downgrades in each
risk quintile across the two models. The
model that performs “better” will be the one with a higher proportion of
downgrades in its highest-risk quintile and a lower proportion of downgrades in
its lowest-risk quintile.
7A shows the percentage of CAMEL downgrades (during the indicated three-year
period) occurring in the highest-risk quintile as classified by each
model. Except for the 1990 to 1992
period (which coincided with a national recession), the proportion of
downgrades occurring in the highest-risk quintile identified by the bank and
economic model is somewhat larger than the proportion in the same quintile for
the bank model. Figure 7B shows the
percentage of downgrades received by banks in the lowest-risk quintile.
Here the proportion of future downgrades
occurring in the lowest-risk quintile identified by the bank and economic model
is generally lower than the proportion in the same quintile for the bank
model. These results suggest that
state-level economic data might be useful in identifying imprudent bank
growth. Although the improvement in the
performance of the growth-monitoring model in anticipating future downgrades is
somewhat modest, it is fairly consistent over time and is in line with evidence
about historical patterns of local economic conditions, portfolio growth, and
subsequent bank performance.25
study investigates the usefulness of state-level economic data in statistical
off-site monitoring models. Our results
indicate that state-level economic data do not contribute to the models that
forecast bank failures and changes in the quality of bank assets.
The results for the model predicting risky
bank growth are more encouraging, indicating that the inclusion of state-level
economic data slightly improves the predictive power of this model.
these results run counter to our initial expectations, we can offer possible
reasons for the findings; some of the reasons might be addressed by future
research. It makes sense to expect that
broad measures of economic conditions, such as state unemployment rates and
personal-income growth, have varying relevance to individual banks.
This variation would be partly due to wide
variation not only in the services and products offered by banks but also in
the composition of state economies. We
are limited in investigating this possibility because banks do not publicly
report business activity (for example, loans) by the geographic markets and
industry sectors served. Given this
limitation, it is difficult to determine which economic variables are likely to
be most relevant to a bank’s current condition and future performance.
Our hope was that broad measures of economic
conditions would have had relevance for most banks and therefore for off-site
We also anticipate that
bank management plays a very significant role in determining how economic
conditions affect a bank’s performance.
Prior research by the FDIC and others has suggested that bank-specific
attributes such as the quality of management, loan underwriting, and
risk-management practices should have an important influence on a bank’s
performance and its susceptibility to adverse economic conditions.
Although these characteristics are hard to
quantify, bank supervisors do collect data in some of these areas.
For example, all federal bank regulators
conduct periodic surveys of bank underwriting practices.
The FDIC is pursuing research on the
contribution that the data in its semiannual underwriting survey might make to
off-site monitoring models.
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