Tài liệu Déjà Vu All Over Again: The Causes of U.S. Commercial Bank Failures This Time Around* docx

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the bottom ranks of banks when measured by Tier 1 capital
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For our purposes, Torna’s study is different from ours in at least four important respects:
First, his study focuses on the distinction between “traditional” and “modern” banking activities,
but doesn’t explore the finer detail among “traditional” banking activities, such as types of loans,
which is a central feature of our study. Second, his study looks back for only a year to find the
determinants of healthy banks’ becoming troubled and troubled banks’ failing, whereas we look
back as far as five years prior to the failures. Third, by including only troubled banks among the
candidates for failure (which is consistent with the one-year look-back period), his study is
limited in its ability to consider longer and broader influences, whereas all commercial banks are
included in our analysis. Fourth, a ranking based only upon capital ignores five of the six
CAMELS components and likely seriously misclassifies “problem banks.” For all of these
reasons, we do not consider Torna’s study to be a close substitute for ours.
) and what causes a troubled bank to
fail (i.e., to become insolvent and have a receivership declared by the FDIC), based on quarterly
identifications of troubled banks and failures from Q4-2007 through Q3-2009. Torna employs
proportional hazard and conditional logit analyses and uses quarterly FDIC Call Report data for a
year prior to the quarterly identification. Torna finds that the influences on a healthy bank’s
becoming troubled are somewhat different from those that cause a troubled bank to fail.
The second point that we wish to make in this section concerns the studies of the bank
and thrift failures of the 1980s and early 1990s – e.g., Cole and Fenn (2008) for commercial

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Torna (2010) cannot directly identify the banks that are on the FDIC’s “troubled banks” list
each quarter because the FDIC releases the total number of troubled banks, but keeps their
identities confidential. As an estimate of those identities, Torna considers “troubled banks”
specifically to be the number of banks at the bottom of the Tier 1 capital ranking that is equal to
the number of banks that are on the FDIC’s “troubled banks” list for each quarter. Torna’s
method provides only a crude approximation to these identities because this method ignores all
but one of the CAMELS components that likely go into the FDIC’s determination of “troubled
bank” status.

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banks and Cole, McKenzie, and White (1995) for thrift institutions – that show how commercial
real estate investments and construction lending have often proved to be significant influences on
depository institutions’ failures. In our current study, we find that construction loans continue to
be a harbinger of failure and that commercial real estate lending and multifamily mortgages, at
least for earlier years, are significantly associated with bank failures.

3. Model, Data, and Univariate Comparisons
3.1. Empirical Model.
In our empirical model of bank failure, the dependent variable FAIL is binary (fail or
survive), so that it would be inappropriate to use ordinary-least-squares regression (see Maddala
1983, pp. 15-16). Consequently, we turn to the multivariate logistic regression model, where we
assume that Failure*
i, 2009
is an unobservable index of the probability that bank i fails during
2009 and is a function of bank-specific characteristics x
t
, so that:
Failure*
i, 2009
= β’ X
i,2009-t
+ μ
i
, (1)
where X
i,2009-t
are a set of financial characteristics of bank i as of December 31
st
in the calendar
year that was t years before 2009, where t ranges from 1 to 5; β is a vector of parameter estimates
for the explanatory variables, μ
i
is a random disturbance term, i = 1, 2, . . . , N, where N is the
number of banks. Let FAIL
i, 2009
be an observable variable that is equal to one if Failure*
i, 2009
>
0 and zero if Failure*
i, 2009
≤ 0. In this particular application, FAIL
,i, 2009
is equal to one if a bank
fails during 2009 and zero otherwise. Since Failure*
i, 2009
is equal to β’ X
i,2009-t
+ μ
i
, the
probability that FAIL
i, 2009
> 0 is equal to the probability that β’ X
i,2009-ti
> 0, or, equivalently, the
probability that (μ
i
> - β’ X
i,2009-t
). Therefore, one can write the probability that FAIL
i, 2009
is
equal to one as the probability that (μ
it
> - β’ X
i,2009-t
) , or, equivalently, that Prob(FAIL
i, 2009
= 1)

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= 1 - Φ (-β’ X
i,2009-t
), where Φ is the cumulative distribution function of ε, here assumed to be
logistic. The probability that FAIL
i, 2009
is equal to zero is then simply Φ (-β’ X
i,2009-t
). The
likelihood function L for this model is:
L = Π [Φ (-β’ X
i,2009-t
)] Π [1 - Φ (-β’ X
i,2009-t
)] ,
FAIL
i
= 0 FAIL
i
= 1
where:
Φ (-β’ X
i,2009-t
) = exp(-β’ X
i,2009-t
) / [1 - exp(-β’ X
i,2009-t
)] = 1 / [1 + exp(-β’ X
i,2009-t
)]
and
1 - Φ (-β’ X
i,2009-t
) = exp(-β’ X
i,2009-t
) / [1 +(-β’ X
i,2009-t
)] .
There were 117 commercial banks that failed during 2009; but, clearly, there are many
more banks that will fail during 2010 – 2012 from the same or similar underlying causes. To
ignore this latter group is to impose a form of right-hand censoring; but, of course, the identities
of the banks in this latter group could not be known as of year-end 2009. Rather than ignore
them, we estimate their identities as follows: We count as a “technical failure” any bank
reporting that the sum of equity plus loan loss reserves was less than half the value of its
nonperforming assets, or, more formally:
(Equity + Reserves – 0.5 x NPA) < 0 ,
where NPA equals the sum of loans past due 30-89 days and still accruing interest, loans past
due 90+ days and still accruing interest, nonaccrual loans, and foreclosed real estate. Our
“technical failure” is equivalent to book-value insolvency when a bank is forced to write off half
the value of its bad loans. There were 148 such banks as of year-end 2009.
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Thus, we place
265 (117 + 148) in the FAIL category.
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It is worth noting that of the 57 of the 74 commercial banks that failed during the first half of
2010 (77%) are members of our “technically failed” group.


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3.2. Data and Explanatory Variables
The data that we use come from the FDIC Call Reports. Because the Call Reports for
thrifts are different from those used for commercial banks, and because thrifts operate under a
different charter and are usually focused in directions that are different from those of commercial
banks, we use only the commercial bank data.
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Our explanatory variables are primarily the financial characteristics of the banks, drawn
from their balance sheets and their profit-and-loss statements as of the fourth quarters of 2008
and earlier years, that we believe are likely to influence the likelihood of a bank’s failing. In
almost all instances, the variables are expressed as a ratio with respect to the bank’s total assets.
The variable acronyms and full names are provided in Table 1. Our expectations for these
variables’ influences are as follows:

TE (Total Equity): Since equity is a buffer between the value of the bank’s assets and the value
of its liabilities, we expect TE to have a negative influence on the likelihood of failure.
LLR (Loan Loss Reserves): Since loan loss reserves represent a reduction in assets against
anticipated losses on specific assets (e.g., a loan), they provide a source of strength against
subsequent losses. Consequently, we expect LLR to have a negative influence on bank failures.
ROA (Return on Assets): This is, effectively, net income, which we expect to have a negative
influence on the likelihood of a bank’s failing.


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However, in our logit regressions for 2008 and 2007, there are only 263 banks in the FAIL
category because two (of the 265 FAIL) banks were denovo start-ups in 2009 and, thus, filed no
financial data for 2008 or 2007.

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We also exclude savings banks, even though they use the same Call Report forms as
commercial banks, because they too are usually focused in directions that are different from
those of commercial banks. Their inclusion does not qualitatively affect our results.

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NPA (Non-performing Assets): Since non-performing assets are likely to be recognized as losses
in a subsequent period, we expect NPA to have a positive influence on the likelihood of a bank’s
failing.
SEC (Securities Held for Investment plus Securities Held for Sale): Securities (e.g., bonds) have
traditionally been considered to be safe, low-risk investments for banks – especially since banks
are prohibited from investing in “speculative” (i.e., “junk”) bonds. The subprime RMBS debacle
has shown that not all bonds that are rated as “investment grade” by the major credit rating
agencies will necessarily remain in that category for very long. Nevertheless, as a general matter
we expect this category (which includes the RMBS) to have a negative effect on a bank’s failing,
especially for smaller banks that generally refrained from purchasing the subprime-based
RMBS that proved so toxic.
BD (Brokered Deposits): These are deposits that are raised through national brokers rather than
from local customers. Although there is nothing inherently wrong with a bank’s deciding to
raise its funds in this way, brokered deposits have traditionally been seen as a way for a bank to
gather funds and grow quickly; and rapid growth has often been synonymous with risky growth.
Consequently, we expect this variable to have a positive effect on failure.
LNSIZE (Log of Bank Total Assets): Smaller banks, especially younger ones, are generally more
prone to failure than are larger banks. On the other hand, larger banks were more likely to have
invested in the toxic RMBS. Consequently, though this variable could well be important, it is
difficult to predict a priori the direction of the influence.
CASHDUE (Cash & Items Due from Other Banks): Since this represents a liquid stock of assets,
we expect it to have a negative effect on failure.

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GOODWILL (Intangible Assets): For banks, this largely represents the undepreciated excess
over book value that a bank paid when acquiring another bank. Though it can represent
legitimate franchise value, it can often represent simply the overpayment in an acquisition. We
expect it to have a positive influence on a bank’s failing.
RER14 (Real Estate Residential Single-Family (1-4) Mortgages): Prior to the current crisis,
single-family
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REMUL (Real Estate Multifamily Mortgages): Lending on commercial multifamily properties
has had a history of being troublesome for banks and other lenders (including Fannie Mae and
Freddie Mac); consequently, we expect it to have a positive influence on failing.
residential mortgages were generally considered to be safe, worthwhile loans for
banks; the failure of millions of subprime mortgages has thrown some doubt on this proposition.
Because most residential mortgages are not subprime, our general expectation is that RER14
would have a negative influence on a bank’s failing.
RECON (Real Estate Construction & Development Loans): This is a category of lending that
has been extraordinarily risky for banks in the past; we expect it to have a positive influence on
failure.
RECOM (Real Estate Nonfarm Nonresidential Mortgages): This is a category of commercial real
estate loans, such as office buildings, and retail malls that proved especially toxic during the
previous banking crisis. We expect it to be positively related to failure.
CI (Commercial & Industrial Loans): This is a category of lending in which commercial banks
are expected to have a comparative advantage. We expect it to have a negative influence on
failure.

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Almost all U.S. housing statistics lump one-to-four residential units into the single-family
category.

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CONS (Consumer Loans): This encompasses automobile loans, other consumer durables loans,
and credit card loans, as well as personal unsecured loans. Again, this is an area where banks
should have a comparative advantage. We expect a negative influence on failure.
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3.3. Univariate Comparisons
Tables 2A – 2E provides the means and standard errors for all banks and separately for
the subsamples of surviving banks and failed banks, along with t-tests for statistically significant
differences in the means of the surviving and failing groups. Tables 2A – 2E provide descriptive
statistics for 2008, 2007, 2006, 2005, and 2004, respectively, so that we can see how the
differences in the two subsamples evolved over the five years prior to the 2009 failures.
In Table 2A are the univariate comparisons based upon year-end 2008 Call Report data.
Not surprisingly, during this period just prior to the 2009 failures, we see that the difference in
the means of virtually every variable is highly significant and with the expected sign. Among
the traditional CAMELS proxies, failing banks have significantly lower capital ratios (0.076 vs.
0.124), higher ratios of NPAs (0.126 vs. 0.026), lower earnings (-0.026 vs. 0.005), and fewer
liquid assets (0.045 vs. 0.062 for Cash & Due, 0.106 vs. 0.204 for Securities, and 0.172 vs. 0.043
for Brokered Deposits). Of course, this is not surprising, as regulators based their decisions to
close a bank largely upon the CAMELS rating of the bank, and that rating is closely proxied by
these variables (see Cornyn, Cole, and Gunther 1995).
More interesting are the loan portfolio variables, especially those that are related to real
estate. Failing banks have significantly higher allocations to commercial real estate of all

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Other financial variables that we tried, but that generally failed to yield significant results,
included Trading Assets; Premises; Restructured Loans; Insider Loans; Home Equity Loans; and
Mortgage-Backed Securities (classified into a number of categories).

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kinds—most notably to Construction & Development loans (0.232 vs. 0.070), but also to
Nonfarm Nonresidential Mortgages (0.226 vs. 0.164) and Multifamily Mortgages (0.029 vs.
0.014). In contrast, failing banks have significantly lower allocations to Residential Single-
Family Mortgages (0.104 vs. 0.143) and Consumer Loans (0.016 vs. 0.046).
In Table 2E are the univariate comparisons based upon 2004 data, which should reflect
the portfolio allocations that led to the shockingly high rates of NPAs and associated losses
reflected in ROA and Total Equity found in Table 2A. Surprisingly, the failed banks had higher
capital ratios than did the surviving banks back in 2004, although the difference is not
statistically significant. Asset quality as measured by NPAs was virtually identical at 0.014.
Profitability (ROA) was significantly lower for the failed banks (0.007 vs. 0.011) as was
liquidity (0.036 vs. 0.049 for Cash &Due, 0.140 vs. 0.240 for Securities, and 0.065 vs. 0.019 for
Brokered Deposits). However, once again, it is the loan portfolio variables that are most
interesting. Even five years before failure, the group of failed banks had much higher
concentrations of commercial real estate loans (0.171 vs. 0.051 for Construction/Development
Loans, 0.221 vs. 0.144 for Nonfarm Nonresidential Mortgages, and 0.029 vs. 0.012 for
Multifamily Mortgages) and much lower concentrations of Residential Single-Family Mortgages
(0.109 vs. 0.146) and Consumer Loans (0.031 vs. 0.059).
Table 3 provides a summary of significant differences in means across the five years
analyzed. As can be seen, most of the variables across the five time periods are consistently
associated (positively or negatively) with failures in 2009.
One point concerning the comparisons of the results using 2008 data with those that use
earlier years’ data – whether the simple comparisons of means that are discussed here or the
multivariate logit results that are discussed in Section 4 – should be stressed: To the extent that a

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category of assets from an earlier year generates losses, those losses will reduce (via write-
downs) the magnitude of the assets (cet. par.) in that category in later years. Thus, if (say)
investments in construction loans in 2006 lead to large losses in 2008 and the eventual failure of
banks in 2009, then the regression involving 2006 data will capture the positive effect of
construction loans on bank failure; but the regression involving 2008 data may fail to find a
significant effect from construction loans, since the write-downs may be so substantial as to
make the importance of construction loans (as of 2008) appear to be relatively modest.

4. Logit Regression Results
In Table 4 are the results of a set of logistic regression models that provide the main
results of our study. In these models, the dependent variable is equal to one if a bank failed
during 2009 or was technically insolvent (as previously defined) as of year-end 2009; and is
equal to zero otherwise. The five pairs of columns present results that are based upon data (i.e.,
explanatory variables) from 2008, 2007, 2006, 2005, and 2004, respectively. The coefficients in
the table represent the marginal effect of a change in the relevant independent variable, when all
variables are evaluated at their means.
The results in the first pair of columns, which are based upon the financial data reported
just prior to failure, we find that the standard CAMELS proxies have the expected signs and are
highly significant. Lower capital as measured by equity to assets was associated with a higher
probability of failure, as was worse asset quality as measured by NPAs to assets, lower earnings
as measured by ROA, and worse liquidity as measured by Cash & Due to assets, Investment
Securities to assets, and Brokered Deposits to assets. These results closely follow the univariate
results presented in Panel A of Table 2. The loan portfolio variables indicate that failed banks

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had significantly higher concentrations of Construction & Development loans and significantly
lower concentrations of Residential Single-Family Mortgages and Consumer Loans. Overall,
this model explains more than 60 percent of the variability in the dependent variable as measured
by the pseudo-R2 statistic (also known as McFadden’s LRI).
As we move back in time in the subsequent pairs of columns in Table 4, our explanatory
power falls off to only 20 percent for the results in the last pair of columns, which are based upon
2004 data, but we find that most of the explanatory variables that are significant for the 2008
data retain significance for the 2004 data—five years prior to the observed outcome of failure or
survival. Only the capital ratio loses significance. Moreover, the prominence of the real estate
loan variables rises as we go back in time, most notably the ratio of Construction &
Development Loans to total assets.
In Table 5, we present a summary of the results in Table 4. As can be seen, there are six
variables that are consistently significant for at least four of the five years prior to measurement
of our outcome of failure or survival. Two are standard CAMELS proxies: asset quality as
measured by the ratio of Nonperforming Assets to total assets, and earnings as measured by
ROA. Brokered deposits, as an indicator of rapid growth and likely a negative indicator of asset
quality and of management quality, has a clear negative influence. The remaining three are real-
estate loan portfolio variables that neatly summarize the underpinnings of not only this banking
crisis but also the underpinnings of the previous crisis during the 1980s: High allocations to
Construction & Development Loans, Nonfarm Nonresidential Mortgages (i.e., commercial real
estate), and Multifamily Mortgages are strongly associated with failure.
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A potential issue of multicollinearity should be mentioned: If the variable Nonfarm
Nonresidential Mortgages is excluded from the regressions, most of the other variables retain the