RELATED ARTICLES Barr, Richard S., Kory A. Killgo, Thomas F. Siems and Sheri Zimmel (2002), "Evaluating the Productive Efficiency and Performance of U.S. Commercial Banks," Managerial Finance Vol. 28, No. 8, 3-25. Barr, Richard S., and Thomas F. Siems (1997), "Bank Failure Prediction Using DEA to Measure Management Quality," in Interfaces in Computer Science and Operations Research: Advances in Metaheuristics, Optimization, and Stochastic Modeling Technologies, eds. R.S. Barr, R.V. Helgason, and J.L Kennington, Kluwer Academic Publishers: Boston, MA, 341-365. Barr, Richard S., Lawrence M. Seiford and Thomas F. Siems (1993), "An Envelopment-Analysis Approach to Measuring the Managerial Efficiency of Banks," Annals of Operations Research Vol. 45, 1-19.

Money and Banking
Survival and the Hump in the CAMELS

Thomas F. Siems looks at the measurement of management quality and its relationship to a bank's success or failure.

Suppose two banks of roughly the same size conduct business in a market with essentially the same customer base and economic conditions. Suppose further that one survives and one fails. What accounts for the diverging result? This question is not easily answered. As it turns out, the tangible success or failure of a bank is largely dependent on intangible factors.

CAMELS Ranking System
Examiners use six factors in evaluating the safety and soundness of U.S. commercial banks. Based on the score in each of these factors, an overall rating is assigned to each bank. The ranking system is known by the acronym CAMELS, where each letter stands for a different factor.[1] The third factor in the acronym, M (the "hump" in the CAMELS rating), refers to the bank’s management quality. While the other factors can be quantified fairly easily from current financial statements, management quality is a somewhat elusive and subjective measure, yet one that is crucial to institutional success.

Measuring Management Quality
Since management quality is inextricably tied to a bank’s success or failure, it is important to develop and improve methods for grading management efficacy. Recent research by Barr, Killgo, Siems and Zimmel (2002) generates a proxy for management quality that measures the relative productive efficiency of U.S. Commercial banks from 1984 to 1998. These efficiency scores suggest that significant differences in performance and soundness exist between the most efficient and least efficient institutions and point to the hump in the CAMELS rating as an important indicator of a bank’s ability to survive.

Return on Average Assets and Efficiency
As shown in Chart 1, return on average assets (ROAA) is positively related to the authors’ efficiency measure. In each year, the differences in ROAA between the most and least efficient groups are highly significant. ROAA is a good barometer of the effect the 1980s banking crisis had on banks of varying efficiencies. From a maximum deficit of 109 basis points below the most efficient quartile in 1987, average ROAA at the least efficient banks climbed to within 15 basis points in 1994 and averaged a difference of 18 basis points from 1994 through 1998.

Chart 1

Bad Loans and Efficiency
In Chart 2, the percentage of nonperforming gross loans reflects a strong difference between relatively efficient and inefficient banks across varying industry conditions. The gap between most and least efficient banks widens with the declining conditions for banks until 1987, when the gap between the two begins to narrow. At this point the nonperforming loan ratio begins to decrease at a faster rate at less efficient institutions than at more efficient ones. By 1994, the difference is no longer statistically significant, a condition that persists through 1998.

Chart 2

Risk and the Loan-to-Asset Ratio
Chart 3 shows that the least efficient banks hold a higher concentration of loans. If a higher loan-to-asset ratio is related to a bank’s willingness to take on additional risk, then these findings are consistent with the view that less efficient institutions are less risk averse than their more efficient counterparts. This may also indicate greater attempts by less efficient banks to improve their operating efficiencies and profitability by increasing revenues through riskier ventures. Considered over time, these results suggest a banking market that is on the whole growing more amenable to risk or perhaps better able to manage and control it through other channels (for example, financial derivatives and other risk management practices).

Chart 3

CAMELS and Efficiency
But how does efficiency correlate with bank examiner ratings? Banks with composite CAMELS ratings of 1 and 2 are generally considered strong banks, whereas those rated 3, 4 or 5 are considered weak. Banks with strong CAMELS ratings have significantly and consistently higher efficiency scores than banks with weak ratings (Chart 4). This finding suggests that the authors’ model might prove useful as an off-site bank surveillance tool to monitor banks between on-site examinations.

Chart 4

Key to a Bank's Long-Run Survival
A camel's humps help it survive for weeks without food and water. Similarly, the hump in examiners’ CAMELS ratings—or the quality of a bank’s management—is key to a bank’s long-run survival. If a bank survives and another fails while conducting business with the same customer base and economic conditions, the disparate outcome is likely due to the differences in management quality. Accurate and timely measurement that approximates management quality shows strong and consistent relationships exist between efficiency and independent measures of performance and reveals relationships between efficiency and soundness as determined by bank examiner ratings.

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