CAMELS (capital adequacy, assets, management capacity, earnings, liquidity, and
sensitivity) ratings can be used to assess a bank’s condition. The authors explore
whether there has been a material change over time in the standards that bank examiners
use when assigning CAMELS ratings to commercial banks.
What looks to be a rather slow economic recovery from the Great Recession in the United
States has bankers and clients questioning whether unduly stringent supervision of
commercial banks has limited credit growth and hampered a return to economic well-being. A
robust examination and testing of models that use time-varying parameters and macroeconomic
variables suggests the contrary—namely, that supervisory standards used to assign
capital adequacy, assets, management capacity, earnings, liquidity, and sensitivity (CAMELS)
ratings in the aftermath of the crisis have been consistent with historical precedent.
Heightened supervisory rigor has noticeably curbed lending activity in subsequent
How Is This Research Useful to Practitioners?
Commercial banks play a critical role in the financial system through their control of
credit provision. Lenders and borrowers alike have asserted that what they deem to be
excessive regulatory scrutiny of their sector has contributed to its lackluster performance,
with ripple effects throughout the US economy, particularly in the wake of the recent
financial crisis. In response to these concerns, regulators have evaluated their own
performance and standards to ensure that their efforts do not unnecessarily constrain the
availability of credit to worthy borrowers. Moreover, this situation is occurring against
the backdrop of new regulation under the Dodd–Frank Act and Basel III that is designed
to enhance the macroprudential supervision of financial institutions and provide
countercyclical capital buffers.
In this heightened regulatory environment, the authors consider the variation in
supervisory stringency or rigor that may be evident in composite CAMELS ratings over a
22-year period that encompasses credit downturns in the early 1990s and early 2000 and the
2007–09 recession. They expand on the limited earlier literature on the subject by
considering a larger sample, using improved measures of macroeconomic conditions, and
comparing a number of econometric specifications to avoid deficiencies of any particular
The authors conclude that supervisory standards used to assign these ratings are mostly
consistent and stable over the examination period. Additionally, the degree of stringency
estimated from the CAMELS ratings depends to some extent on the models used. Using
macroeconomic variables instead of time-period fixed effects to control for macroeconomic
conditions seems to imply decreased stringency on several occasions over the time period
considered. Finally, meaningful changes in lending standards and changes in lending itself
are linked to the level of supervisory rigor that the models identify.
Academics and regulators will find the authors’ conclusions interesting and
informative. Portfolio managers and analysts covering the banking sector may want to
consider the salient points of this research in their ongoing appraisal of financial
How Did the Authors Conduct This Research?
The existing body of literature on the effects of banking supervision and economic activity
is fairly robust, but the research on bank supervisors’ changes to standards and
policy over economic cycles and the effects of these changes on the economy are less so. The
authors observe deficiencies in previous researchers’ use of time-period fixed
effects, which may pick up extraneous regulatory responses to broader market conditions at
the expense of regional or state control variables and fail to allow for differential
changes in standards across different CAMELS rating criteria.
From here, the authors devise an improved process to incorporate macroeconomic variables
and time-varying standards in the CAMELS ratings. The data include supervisory data on
CAMELS ratings along with bank-specific financial statements, capital adequacy, asset and
management quality, earnings, liquidity, market risk sensitivity, and macroeconomic and
financial sector variables. Econometric parameters also consider the bank’s
structure—for example, whether it is a standalone entity or part of a bank holding
company with different regulatory remit and more robust oversight. The period under
examination is from 1991 to 2013.
The heterogeneity of banking institutions is likely evident in the CAMELS ratings,
notwithstanding extensive use of control variables in the models. The infrequency of and
irregularly timed supervisory exams preclude a simple model analysis. Therefore, the authors
use different panel data models—all of which exhibit various deficiencies.
Results across the three regression models indicate that the same variables are
statistically significant, which suggests that the outcomes are robust to these
models’ specification errors. How they indicate degree of supervisory stringency
points to a limitation of the methodology that measures it at a given point in time relative
to the average over the sample period rather than an absolute level over time. Exclusion of
macroeconomic variables materially affects the outcome in terms of degree of supervisory
stringency. Their inclusion, by contrast, mitigates the cyclicality of CAMELS ratings. The
authors’ conclusions withstand robustness checks for such things as selection bias and
exclusion of less than full-scope examinations. Market dislocations can result in changes in
supervisory stringency that may influence economic activity, but overall, supervisory
standards for CAMELS ratings have adhered to historical norms.
The authors undertake a rigorous evaluation of how changes in standards that banks
supervisors use to assign CAMELS ratings affect supervisory stringency over commercial banks
and, by extension, economic activity. They use several different types of models to simulate
CAMELS ratings and compare their output with the actual ratings. Discrepancies observed
relate to lending cycle fluctuations. For the most part, CAMELS rating standards have been
consistent over time. The results hold up to changes in specifications and robustness tests.
Possible additional research could consider items other than the use of CAMELS ratings to
influence bank risk management—for example, loan classification supervisory standards.
Alternative proxies for management quality and access to qualitative measures could widen
the scope of further research.