Data mining method for listed companies’ financial distress prediction

Data mining technique is capable of mining valuable knowledge from large and changeable database. This paper puts forward a data mining method combining attribute-oriented induction, information gain, and decision tree, which is suitable for preprocessing financial data and constructing decision tree model for financial distress prediction. On the base of financial ratios attributes and one class attribute, adopting entropy-based discretization method, a data mining model for listed companies’ financial distress prediction is designed. The empirical experiment with 35 financial ratios and 135 pairs of listed companies as initial samples got satisfying result, which testifies the feasibility and validity of the proposed data mining method for listed companies’ financial distress prediction.