FRBSF Economic Letter
2003-01; January 24, 2003
Using Equity Market Information to Monitor Banking Institutions
Bank supervisors and stock market investors engage
in extensive monitoring of bank holding companies (BHCs), but for different
reasons. While investors
are looking to ensure that BHC managers maximize shareholder value, bank
supervisors monitor BHCs to enforce regulations, gauge their safety and
soundness, and guard against broader systemic risk. Despite these differences
in motivation, changes in stock prices could be relevant for supervisory
concerns.
In this Economic Letter, we present empirical evidence
on the potential usefulness of equity market data in the supervisory
surveillance of BHCs.
We find that changes in stock prices tend to precede changes in supervisory
BHC ratings by at least nine months. We also assess the contribution
of equity market information in the context of an off-site monitoring
model for BHCs. Our results indicate that equity market information can
be
marginally
useful to supervisors, especially since the cost of acquiring and manipulating
the data is quite low.
Supervisory surveillance and equity markets
The Federal
Reserve is the supervisor of BHCs in the United States. (Note that the
BHC is typically the stock-issuing entity within a banking organization.)
Full-scope, on-site inspections of BHCs are a key element of the supervisory
process. These inspections are generally conducted once a year. At the
conclusion of an inspection, the supervisors assign a composite BOPEC
rating, which
summarizes their opinion of the BHC's overall health and financial condition.
The BOPEC acronym stands for the five key areas of supervisory concern:
the condition of the BHC's Bank subsidiaries, Other nonbank subsidiaries,
Parent company, Earnings, and Capital adequacy. BOPEC ratings are assigned
according to an absolute scale from the highest rating of one (indicating
strong performance) to the lowest rating of five (indicating very poor
performance). Note that BOPEC ratings are highly confidential and are
not made public.
Between on-site inspections, when private supervisory information cannot
be gathered as readily, supervisors monitor BHCs using a well-specified
off-site monitoring system; see Supervisory Letters 95-43 and 02-01 issued
by the Federal Reserve Board of Governors. Three primary sources of information
are used in the surveillance process. One source, known as the BHC Performance
Report, is a detailed summary of the quarterly Y-9C regulatory reporting
forms filed by BHCs. From this report, certain variables are selected
as key performance criteria, and if a BHC fails to meet these criteria
in a
given quarter, it is noted as an exception that requires further monitoring.
A
second source of information for off-site BHC monitoring is the supervisory
CAMELS ratings assigned to banks within a bank holding company. As with
BOPEC ratings, CAMELS ratings are assigned after bank examinations and
are confidential. Since the condition of a BHC is closely related to
the condition
of its subsidiary banks, the off-site BHC surveillance process includes
monitoring recently assigned CAMELS ratings.
A third information source
is BHC financial market information, when available. Supervisors monitor
BHC stock prices (and other financial
market variables). If a BHC exhibits irregular stock price movements,
it can be
noted as an exception that requires further monitoring during the regular
surveillance process.
Using equity market data in the BHC supervisory
surveillance process is in keeping with broader efforts to promote market
discipline in banking;
see Kwan (2002) for a summary. A potential obstacle to using equity market
data is the opaqueness of BHC assets; that is, loans, credit lines, and
other BHC financial assets may be especially hard for investors to value.
If so, signals from BHC stock prices may not be reliable enough for supervisory
purposes. Fortunately, most of the recent academic research provides
some reassurance concerning the reliability of BHC equity market information.
These findings suggest that BHCs are not harder for equity investors
to
value than nonfinancial firms.
Can the equity market anticipate BOPEC
changes?
If equity market assessments are to be useful to BHC supervisors,
they must, at a minimum, agree with supervisory assessments a reasonably
large
fraction of the time. The equity market assessment that we use is based
on BHC stock returns leading up to a BOPEC assignment. Large stock returns
(either positive or negative) could give supervisors an early warning
of changes in the economic environment that are relevant to a BHC's condition.
We examined this possibility by conducting an event study of BHC stock
returns
leading up to BOPEC assignments. We constructed a model that decomposes
BHC stock returns into a systematic component based on general market
conditions and an idiosyncratic component that captures individual BHC
factors. We
examined whether the realized cumulative idiosyncratic returns up to
twelve months before the BOPEC assignment behaved abnormally. This approach
allows
us to examine whether the BHC's idiosyncratic returns are consistent
with the BHC's assigned BOPEC rating.
In our event studies (see Krainer
and Lopez, 2001), we found that the equity market sends a clear signal
well in advance of an approaching
ratings change. For upgrades, the returns are positive and statistically
significant
as early as twelve months before the inspection. For downgrades, the
returns are negative and statistically significant starting at about
nine months
before the change. For no change in BOPEC rating, the returns are insignificantly
different from zero, implying that the market is not signaling a change.
These results suggest that equity market assessments of BHC conditions,
as reflected in idiosyncratic stock returns, are consistent with future
supervisory assessments.
Contributions to an off-site monitoring model
The second step in gauging
the usefulness of BHC equity market information is to assess its contribution
when used in conjunction with standard
supervisory variables. For this exercise, we turn to the proposed BOPEC
off-site monitoring
(BOM) model discussed in Krainer and Lopez (2002). The benchmark, or
core, BOM model examines the relationship between assigned BOPEC ratings
and selected
supervisory variables. We extended the model by incorporating BHC equity
market variables, such as the systematic and idiosyncratic stock returns
discussed above. When the model is estimated over our full sample of
BOPEC ratings assigned from 1990 to 1999, the equity market variables
are statistically
significant and contribute to the model's empirical fit of the data.
To
be useful for supervisory purposes, this extended BOM model also must
be able to forecast BOPEC ratings accurately out-of-sample. In order
to mimic actual practices, we reestimated the BOM model with and without
equity
market variables every quarter based on a rolling four-quarter sample
of data. The estimated models then were used to generate one-quarter-ahead
BOPEC forecasts. The model signals a change in supervisory rating if
the
BOPEC forecast is more than three-quarters of a rating grade different
from its corresponding lagged BOPEC rating. (Note that the forecasts
are continuous
variables and need not take integer values like the BOPEC ratings themselves.)
When compared to all the ratings in our sample at four quarters prior
to assignment, the extended model's forecasts correctly identify 70%
of all
the BOPEC assignments and about 18% of all BOPEC changes. These percentages
increase to 76% and 36%, respectively, at one quarter prior to assignment.
Another
dimension of accuracy for the model is the mix of correct and incorrect
signals. Given that the model signals, say, a downgrade, what
is the probability that the signal will be correct? This dimension of
accuracy is measured by the ratio of correct signals of a given BOPEC
assignment to the total number of signals of that type. For example,
the accuracy
of
downgrade signals is the ratio of correctly signaled downgrades to the
total number of signaled downgrades. The figure presents these percentages
for
the upgrade, no change, and downgrade signals. These percentages of correct
signals are relatively high at four quarters prior to assignment and
improve at one quarter prior.
"
No change" signals are the most common and are correct about 70% of
the time. Downgrade signals are correct about 45% of the time at four
quarters prior, and that percentage improves to 66% at one quarter prior.
Upgrade
signals are correct about 60% of the time at four quarters prior and
almost 80% of the time at one quarter prior. These results indicate that
forecast
signals from the extended BOM model are accurate a large percentage of
the time, even up to a year prior to the BOPEC assignments, and could
thus be
useful for off-site monitoring purposes.
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A critical question is whether
the model that includes both supervisory and equity market variables
provides useful information about BOPEC ratings
beyond what is obtained by the model using only supervisory variables.
A common way to make such an assessment is to compare statistically
the accuracy
of the two sets of forecasts, which in this case is the percentage
of BOPEC ratings accurately forecasted. By this metric, we find little
statistical difference between the accuracy of the forecasts based on supervisory
variables
alone and that of the model augmented with equity market variables.
This
result, however, does not mean that the forecasted BOPEC ratings
from the two models are the same. The forecasting literature has
shown that combining forecasts from different models can improve certain
aspects of
forecast accuracy. That appears to be the case here since the two
models
signal BOPEC changes for different, although overlapping, sets of
BHCs. Hence, another way to gauge the contribution of equity market information
is to examine the additional forecast signals for public BHCs as
generated
by the extended model relative to the core model's signals. At four
quarters prior, the extended model signals 72 additional BOPEC changes,
of which
27 (about 40%) were correct. The correct signals were almost evenly
split between BOPEC upgrades and downgrades. For one quarter prior,
101 additional
BOPEC changes were signaled, of which 44 were correct and again almost
evenly split between upgrades and downgrades.
Seen in this light,
the marginal benefit of adding these additional signals to the core
model signals is notable. At four quarters prior,
the additional
27 correct signals of BOPEC changes increase the total to 178,
a 20% increase. At one quarter prior, the additional 44 correct signals
increase
the total
of correct BOPEC change forecasts by 12% to 394. The benefits from
having the additional correct signals provided by these forecasts
could very
well be worth the supervisory costs of dealing with the additional
incorrect signals.
Conclusion
Changes in stock prices for BHCs appear to provide information
on their financial condition that is relevant to supervisory
concerns. When
examined in isolation, we find that equity market variables
lead BOPEC changes
by at least nine months in advance. Equity market variables
are statistically
significant in our BOPEC off-site monitoring model estimated
over our sample
period. The model's out-of-sample forecasts perform well when
compared to actual BOPEC outcomes, but the forecasts were not statistically
different from those of the model based solely on supervisory
information.
However, the two models did not produce identical sets
of forecasts. The marginal contribution of using equity market information
is notable since combining the core and extended model's
forecasts increased
the number of rating changes correctly signaled by about
20%. We conclude
that using
equity market variables in this way may have practical value
for supervisors
for two further reasons. Equity market data are available
sooner than supervisory data from quarterly financial statements,
which could assist
a monitoring
model in detecting sudden changes in BHC conditions sooner.
Since the cost of incorporating equity market variables into
a model,
such as
the BOM model,
is low, even small net improvements in forecast accuracy
could be of value.
| John Krainer |
Jose A. Lopez |
| Economist |
Economist |
References
Krainer, J., and J.A. Lopez. 2001. "Incorporating Equity Market Information
into Supervisory Monitoring Models." FRBSF Working Paper 01-14.
Krainer, J., and J.A. Lopez. 2002. "Off-Site Monitoring of Bank Holding
Companies." FRBSF Economic Letter 2002-15 (May 17).
Kwan, S.H. 2002. "The Promise and Limits of Market Discipline in Banking." FRBSF
Economic Letter 2002-36 (December 13).
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