Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural NetworksPublished 1 July 2001
Amir F. Atiya
Citations684
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TL;DR
Inspired by one of the traditional credit risk models developed by Merton (1974), it is shown that the use of novel indicators for the NN system provides a significant improvement in the (out-of-sample) prediction accuracy.
Abstract
The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit risk models developed by Merton (1974), we propose novel indicators for the NN system. We show that the use of these indicators in addition to traditional financial ratio indicators provides a significant improvement in the (out-of-sample) prediction accuracy (from 81.46% to 85.5% for a three-year-ahead forecast).
Keywords
Computer ScienceEconomics, Econometrics and FinanceBusiness, Management and Accounting
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