Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
复制标题
DOI：
10.1016/s0377-2217(98)00051-4
复制
发表时间：
1999-07
期刊：
影响因子：
--
通讯作者：
G. Zhang;Michael Y. Hu;B. E. Patuwo;Daniel C. Indro
中科院分区：
文献类型：
--
作者：
G. Zhang;Michael Y. Hu;B. E. Patuwo;Daniel C. Indro
在本文中，我们提出了一个通用的框架来理解人工神经网络（ANN）在破产预测中的作用。我们给出了一个全面的回顾神经网络在这一领域的应用，并说明神经网络和传统的贝叶斯分类理论之间的联系。交叉验证的方法被用来检查样本间的变化的神经网络的破产预测。基于220家公司的匹配样本，我们的研究结果表明，神经网络在预测和分类率估计方面明显优于逻辑回归模型。此外，神经网络对总体分类性能的采样变化具有鲁棒性。
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.