A comparison of Japanese failure models: Hybrid neural networks, logit models, and discriminant analysis

Abstract

This article looks at the ability of a relatively new technique, hybrid artificial neural networks (ANNs), to predict Japanese banking and firm failures. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting failure for one year prior to the event. This suggests that for researchers, policymakers, and others interested in early warning systems, the hybrid network may be a useful tool for predicting banking and firm failures.