Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis |
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| Affiliation: | 1. National Research University Higher School of Economics, 101000, 20 Myasnitskaya str., Moscow, Russia;2. “Globalstar – SpaceTelecommunications” Joint Stock Company, 117485, 7 Butelerova str., Moscow, Russia;1. School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China;2. Key Laboratory of Big Data Mining and Knowledge Management, CAS, Beijing 100190, China;3. CAS Research Center on Fictitious Economy and Data Science, Beijing 100190, China;4. The College of Information Science and Technology, University of Nebraska at Omaha, NE 68182, USA;5. College of Electrical & Information Engineering, Southwest Minzu University, Chengdu, Sichuan 610041, China |
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| 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. |
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