Bankruptcy prediction for credit risk using neural networks: A survey and new results
- Creators
- Atiya, Amir F.
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).
Additional Information
© 2001 IEEE. Manuscript received February 13, 2001; revised March 16, 2001 and March 25, 2001. The author would like to acknowledge the useful discussions with P. Sondhi. The author would also like to acknowledge the support of NSF's Engineering Research Center at Caltech.Attached Files
Published - 0922b4f35185c31d9b000000.pdf
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Additional details
- Eprint ID
- 75897
- Resolver ID
- CaltechAUTHORS:20170408-135833952
- NSF
- Created
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2017-04-14Created from EPrint's datestamp field
- Updated
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2021-11-15Created from EPrint's last_modified field