Name
Title | ||
|---|---|---|
Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis |
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. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0377-2217(98)00051-4 | European Journal of Operational Research |
Keywords | Field | DocType |
Artificial intelligence,Neural networks,Bankruptcy prediction,Classification | Naive Bayes classifier,Bankruptcy prediction,Types of artificial neural networks,Artificial intelligence,Sampling (statistics),Artificial neural network,Classification rate,Cross-validation,Logistic regression,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
116 | 1 | 0377-2217 |
Citations | PageRank | References |
154 | 10.17 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoqiang Peter Zhang | 1 | 270 | 17.96 |
| Michael Y. Hu | 2 | 426 | 55.74 |
| B. Eddy Patuwo | 3 | 261 | 19.98 |
| Daniel C. Indro | 4 | 154 | 10.17 |