Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis

Bankruptcy prediction is one of the major business classification problems. In this paper we use three different techniques: (1) Multivariate discriminant analysis, (2) case-based forecasting, and (3) neural network to predict Korean bankrupt and nonbankrupt firms. The average hit ratios of three methods range from 81.5 to 83.8%. Neural network performs better allan discriminant analysis and the case-based forecasting system. (C) 1997 Elsevier Science Ltd.