A Comparison of Corporate Failure Models in Australia: Hybrid Neural Networks, Logit Models and Discriminant Analysis

摘要

This study investigated whether two artificial neural networks (ANNs), multilayer perceptron (MLP) and hybrid networks using statistical and ANN approaches, can outperform traditional statistical models for predicting corporate failures in Australia one year prior to the financial distress. The results suggest that hybrid neural networks outperform all other models. Therefore, hybrid neural network model is a very promising tool for failure prediction. This supports the conclusion that for shareholders, policymakers and others interested in early warning systems, hybrid networks would be useful.