Analyzing the financial distress of Chinese public companies using probabilistic neural networks and multivariate discriminate analysis

Abstract

Many studies have applied backpropagation feedforward neural networks (BPNNs) as an alternative to multivariate discriminant analysis (MDA) in attempts to predict business distress using relatively small data sets. Although these studies have generally reported the superiority of BPNNs vs. MDA, they seem to ignore the fact that the former suffers from overfitting if the data set is too small compared to the free parameters of the network. We thus suggest an alternative approach that involves use of a probabilistic neural network (PNN). From our study of financially distressed Chinese public companies, we found that both the PNN and MDA algorithms provide good classifications. Relative to MDA, however, the PNN method provides better prediction, and, at the same time, does not require multivariate normality of the data. Our results appear to offer an improvement from those of earlier efforts that employ MDA, BPNN, and other models. In particular, PNN was here able to predict company distress with greater than 87.5% short-term accuracy, and 81.3% medium-term accuracy.