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

Guoqiang Zhang, Michael Y. Hu, B. Eddy Patuwo, Daniel C. Indro

Research output: Contribution to journalArticlepeer-review

363 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)16-32
Number of pages17
JournalEuropean Journal of Operational Research
Volume116
Issue number1
DOIs
StatePublished - Jul 1 1999

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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