A Comparison of the Ability of Neural Networks and Logit Regression Models to Predict Levels of Financial Distress

Abstract

In this study we compared the classification accuracy rates of neural networks to those from ordinal legit models for a multi-state response variable. The results indicate that with the multi-state response variable, neural networks produce higher overall classification rates than ordinal legit models, but do not more accurately classify distressed firms. As a result, we can not clearly state that neural networks are superior to regression when predicting more than one level of financial distress.