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A comparison of the relative costs of financial distress models: artificial neural networks, logit and multivariate discriminant analysis

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  • Harlan L. Etheridge
  • Ram S. Sriram

Abstract

This study uses two artificial neural networks (ANNs), categorical learning/instar ANNs and probabilistic (PNN) ANNs, suitable for classification and prediction type issues, and compares them to traditional multivariate discriminant analysis (MDA) and logit to examine financial distress one to three years prior to failure. The results indicate that traditional MDA and logit perform best with the lowest overall error rates. However, when the relative error costs are considered, the ANNs perform better than traditional logit or MDA. Also, as the time period moves farther away from the eventual failure date, ANNs perform more accurately and with lower relative error costs than logit or MDA. This supports the conclusion that for auditors and other evaluators interested in early warning techniques, categorical learning network and probabilistic ANNs would be useful. © 1997 John Wiley & Sons, Ltd.

Suggested Citation

  • Harlan L. Etheridge & Ram S. Sriram, 1997. "A comparison of the relative costs of financial distress models: artificial neural networks, logit and multivariate discriminant analysis," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 6(3), pages 235-248, September.
    • Handle: RePEc:wly:isacfm:v:6:y:1997:i:3:p:235-248
    DOI: 10.1002/(SICI)1099-1174(199709)6:33.0.CO;2-N
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    Cited by:

    1. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.
    2. Daniel E. O'Leary, 2010. "Intelligent Systems in Accounting, Finance and Management: ISI journal and proceeding citations, and research issues from most‐cited papers," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 17(1), pages 41-58, January.
    3. Amelia A. Baldwin & Carol E. Brown & Brad S. Trinkle, 2006. "Opportunities for artificial intelligence development in the accounting domain: the case for auditing," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 14(3), pages 77-86, July.
    4. Daniel E. O'Leary, 2009. "Downloads and citations in Intelligent Systems in Accounting, Finance and Management," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 21-31, January.

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