Minimizing the Costs of Using Models to Assess the Financial Health of Banks

Abstract

Identifying banks that are likely to experience financial distress in the future can be problematic for bank regulators and investors. Traditionally, bank examiners use a variety of methods, including traditional statistical modelling techniques, to categorize banks as financially healthy or financially distressed. Often, these statistical models are chosen based on overall model error rate. Unfortunately, these statistical models often misclassify banks. Our study compares the ability of multivariate discriminant analysis (MDA), logistic regression (logit) and three types of artificial neural networks (ANNs) to classify banks as financially healthy or financially distressed. We calculate overall error rates, Type I error rates and Type II error rates for all five models. Our results show that both MDA and logit have lower estimated overall error rates and Type II error rates that the three ANNs. However, the ANNs have lower Type I error rates than MDA and logit. We demonstrate that relying solely on overall misclassification error rates to choose a model to analyze the financial viability of banks will result in suboptimal model performance. We find that model performance is directly related to assumptions regarding the relative costs of Type I and Type II errors. Our results indicate that if it is assumed that Type I errors are more costly than Type II errors, then a categorical learning neural network minimizes the overall cost associated with assessing the financial condition of banks.