Predicting large US commercial bank failures
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The present study applies empirical methods to the problem of predicting large US commercial bank failures. Due to sampling limitations, scant research has examined the feasibility of using computer-based early warning systems (EWSs) to identify pending large bank failures. In the late 1980s and early 1990s numerous large banks failed in the US enabling us to collect adequate samples of large banks with more than $250 million in assets for empirical analyses. Both the parametric method of logit analysis and the nonparametric approach of trait recognition are employed to (1) develop classification EWS models based on original samples and (2) test the efficacy of these models based on their prediction accuracy using holdout samples. Both logit and trait recognition performed well in terms of classification results. However, with regard to the prediction results using holdout samples, trait recognition outperformed logit in most tests in terms of minimizing Type I and II errors. Other results from the trait recognition models reveal that complex two- and three-variable interactions between financial and accounting variables contain additional information about bank risk not found in the individual variables themselves. We conclude that computer-based EWSs can provide valuable information about the future viability of large banks. 2002 Published by Elsevier Science Inc.
