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Using neural networks to predict corporate failure

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  • Daniel E. O’Leary

Abstract

Predicting corporate failure or bankruptcy is one of the most important problems facing business and government. The recent Savings and Loan crisis is one example, where bankruptcies cost the United States billions of dollars and became a national political issue. This paper provides a ‘meta analysis’ of the use of neural networks to predict corporate failure. Fifteen papers are reviewed and compared in order to investigate ‘what works and what doesn’t work’. The studies are compared for their formulations including aspects such as the impact of using different percentages of bankrupt firms, the software they used, the input variables, the nature of the hidden layer used, the number of nodes in the hidden layer, the output variables, training and testing and statistical analysis of results. Then the findings are compared across a number of dimensions, including, similarity of comparative solutions, number of correct classifications, impact of hidden layers, and the impact of the percentage of bankrupt firms. © 1998 John Wiley & Sons, Ltd.

Suggested Citation

  • Daniel E. O’Leary, 1998. "Using neural networks to predict corporate failure," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 7(3), pages 187-197, September.
    • Handle: RePEc:wly:isacfm:v:7:y:1998:i:3:p:187-197
    DOI: 10.1002/(SICI)1099-1174(199809)7:33.0.CO;2-7
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    Cited by:

    1. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    2. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.

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