Firm Bankruptcy Prediction: Experimental Comparison of Isotonic Separation and Other Classification Approaches

Abstract

A newly introduced method called isotonic separation is evaluated in the prediction of firm bankruptcy. Feature reduction methods are first applied to reduce the ratios used in the prediction. Then, various classification methods, including discriminant analysis, neural networks, decision tree induction, learning vector quantization, rough sets, and isotonic separation, are used with the reduced ratios. Experiments show that the isotonic separation method is a viable technique, performing generally better than other methods for short-term bankruptcy prediction.