Data mining method for listed companies' financial distress prediction

被引：108

作者：

Sun, Jie ^{[1
]}

Li, Hui ^{[1
]}

机构：

[1] Zhejiang Normal Univ, Sch Business Adm, Jinhua 321004, Zhejiang Prov, Peoples R China

来源：

KNOWLEDGE-BASED SYSTEMS | 2008年 / 21卷 / 01期

基金：

中国国家自然科学基金;

关键词：

financial distress prediction; data mining; decision tree; attribute-oriented induction;

D O I：

10.1016/j.knosys.2006.11.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (c) 2006 Elsevier B.V. All rights reserved.

引用

页码：1 / 5

页数：5

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