Improving bankruptcy prediction with Hidden Layer Learning Vector Quantization

A Hidden Layer Learning Vector Quantization (HLVQ), neural network-learning algorithm is used for correcting the outputs of Multilayer Perceptrons (MLP) for predicting corporate bankruptcy. We call this method HLVQ-C, and it is shown that it outperforms both discriminant analysis and traditional neural networks while significantly reducing type I error, which is the type of error that has the highest costs for banks. Moreover, our approach gives an estimation of the prediction robustness thus providing a useful measure of credit risk, which is of great interest for banks, insurance companies and creditors in general. We also show that unbalanced samples, containing more financially sound firms than bankrupt firms, place a strong bias on the classifiers thus leading to a deterioration of type I error accuracy. Although many studies have been published on bankruptcy prediction using neural networks or discriminant analysis, they used mainly US or UK samples of very limited size. Our study is based on industrial French firms, uses a data-set of 583 bankrupt firms over the period 1998-2000 and tests the effects of different proportions of non-bankrupt firms in the sample. Attention was also given to feature selection to reduce the dimensionality of the problem.