Short communication: Data mining method for listed companies' financial distress prediction

Authors:
Jie Sun;Hui Li
Affiliations:
School of Business Administration, Zhejiang Normal University, Jinhua 321004, Zhejiang Province, PR China;School of Business Administration, Zhejiang Normal University, Jinhua 321004, Zhejiang Province, PR China
Venue:
Knowledge-Based Systems
Year:
2008

Citing 5
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Abstract

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.