Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis

被引:366
作者
Zhang, GQ
Hu, MY [1 ]
Patuwo, BE
Indro, DC
机构
[1] Kent State Univ, Grad Sch Management, Coll Business Adm, Kent, OH 44240 USA
[2] Georgia State Univ, Dept Decis Sci, Coll Business, Atlanta, GA 30303 USA
关键词
artificial intelligence; neural networks; bankruptcy prediction; classification;
D O I
10.1016/S0377-2217(98)00051-4
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
In this paper, we present a general framework for understanding the role of artificial neural networks (ANNs) in bankruptcy prediction. We give a comprehensive review of neural network applications in this area and illustrate the link between neural networks and traditional Bayesian classification theory. The method of cross-validation is used to examine the between-sample variation of neural networks for bankruptcy prediction. Based on a matched sample of 220 firms, our findings indicate that neural networks are significantly better than logistic regression models in prediction as well as classification rate estimation. In addition, neural networks are robust to sampling variations in overall classification performance. (C) 1999 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:16 / 32
页数:17
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