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

*(English)*Zbl 0995.62506Summary: 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.

##### MSC:

62P05 | Applications of statistics to actuarial sciences and financial mathematics |

62M45 | Neural nets and related approaches to inference from stochastic processes |

91B28 | Finance etc. (MSC2000) |

68T05 | Learning and adaptive systems in artificial intelligence |

62P20 | Applications of statistics to economics |

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\textit{G. Zhang} et al., Eur. J. Oper. Res. 116, No. 1, 16--32 (1999; Zbl 0995.62506)

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