A data mining method for forecasting financial distress of chinese listed companies

Using Chinese listed company as the research object, seventy-three companies in special trade and that much in normal trade from 1999 to 2000 are selected as the training sample, then forty-three companies in special trade and in normal trade of 2002 are selected as the test sample in this paper. Fifteen financial ratios two years before the finance distress have been analyzed. Three independent methods-discriminant analysis, logistic regression and neural network have been used in data mining process. It has been proved that the method of neural network is better than others. Combing the merits of the above methods, a hybrid model that increases prediction performance has be set up. The empirical tests show that it can produce higher predictive accuracy than single one.