Bankruptcy prediction for credit risk using neural networks: A survey and new results
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- 1 July 2001
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 12 (4) , 929-935
- https://doi.org/10.1109/72.935101
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
This publication has 44 references indexed in Scilit:
- Predicting bankruptcies with the self-organizing mapNeurocomputing, 1998
- Statistical Classification Methods in Consumer Credit Scoring: A ReviewJournal of the Royal Statistical Society Series A: Statistics in Society, 1997
- Feedforward neural networks in the classification of financial informationThe European Journal of Finance, 1997
- Neural network prediction analysis: The bankruptcy caseNeurocomputing, 1996
- Pricing Derivatives on Financial Securities Subject to Credit RiskThe Journal of Finance, 1995
- Predicting japanese corporate bankruptcy in terms of financial data using neural networkComputers & Industrial Engineering, 1994
- Neural network performance on the bankruptcy classification problemComputers & Industrial Engineering, 1993
- Neural Networks: A New Tool for Predicting Thrift Failures*Decision Sciences, 1992
- Financial Ratios and the Probabilistic Prediction of BankruptcyJournal of Accounting Research, 1980
- Financial Ratios As Predictors of FailureJournal of Accounting Research, 1966