Effectiveness of Neural Network Types for Prediction of Business Failure

Abstract

The study examines the effectiveness of different neural networks in predicting bankruptcy filing. Two approaches for training neural networks, Back-Propagation and Optimal Estimation Theory, are considered. Within the back-propagation training method, four different models (Back-Propagation, Functional Link Back-Propagation With Sines, Pruned Back-Propagation, and Cumulative Predictive Back-Propagation) are tested. The neural networks are compared against traditional bankruptcy prediction techniques such as discriminant analysis, logit, and probit. The results show that the level of Type I and Type II errors varies greatly across techniques. The Optimal Estimation Theory neural network has the lowest level of Type I error and the highest level of Type II error while the traditional statistical techniques have the reverse relationship (i.e., high Type I error and low Type II error). The back-propagation neural networks have intermediate levels of Type I and Type II error. We demonstrate that the performance of the neural networks tested is sensitive to the choice of variables selected and that the networks cannot be relied upon to “sift through” variables and focus on the most important variables (network performance based on the combined set of Ohlson and Altman data was frequently worse than their performance with one of the subsets). It is also important to note that the results are quite sensitive to sampling error. The significant variations across replications for some of the models indicate the sensitivity of the models to variations in the data.

Keywords: Ohlson's variables, neural networks, logistic regression, Altman's model, business failure prediction