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Predicting Corporate Failure Using a Neural Network Approach

Author

Listed:
  • J.E. Boritz
  • D.B. Kennedy
  • Augusto de Miranda e Albuquerque

Abstract

This paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and non‐failed classes. Two techniques, back‐propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross‐section of industries. The results obtained with the neural networks are compared with other well‐known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of ‘disproportionate sampling’ by creating ‘training’ and ‘testing’ populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the ‘magical’ results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.

Suggested Citation

  • J.E. Boritz & D.B. Kennedy & Augusto de Miranda e Albuquerque, 1995. "Predicting Corporate Failure Using a Neural Network Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(2), pages 95-111, June.
  • Handle: RePEc:wly:isacfm:v:4:y:1995:i:2:p:95-111
    DOI: 10.1002/j.1099-1174.1995.tb00083.x
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    File URL: https://doi.org/10.1002/j.1099-1174.1995.tb00083.x
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    References listed on IDEAS

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    1. Kurt M. Fanning & Kenneth O. Cogger, 1994. "A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 3(4), pages 241-252, December.
    2. Ting†Peng Liang & John S. Chandler & Ingoo Han & Jinsheng Roan, 1992. "An empirical investigation of some data effects on the classification accuracy of probit, ID3, and neural networks," Contemporary Accounting Research, John Wiley & Sons, vol. 9(1), pages 306-328, September.
    3. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    4. Gessner, Guy & Malhotra, Naresh K. & Kamakura, Wagner A. & Zmijewski, Mark E., 1988. "Estimating models with binary dependent variables: Some theoretical and empirical observations," Journal of Business Research, Elsevier, vol. 16(1), pages 49-65, January.
    5. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    6. Beaver, Wh, 1968. "Market Prices, Financial Ratios, And Prediction Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 179-192.
    7. Duane B. Kennedy, 1992. "Classification techniques in accounting research: Empirical evidence of comparative performance," Contemporary Accounting Research, John Wiley & Sons, vol. 8(2), pages 419-442, March.
    8. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
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    Cited by:

    1. Balcaen S. & Ooghe H., 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classic statistical methods?," Vlerick Leuven Gent Management School Working Paper Series 2004-16, Vlerick Leuven Gent Management School.
    2. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    3. Paul P. M. Pompe & Jan Bilderbeek, 2005. "Bankruptcy prediction: the influence of the year prior to failure selected for model building and the effects in a period of economic decline," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 13(2), pages 95-112, June.
    4. Jayasekera, Ranadeva, 2018. "Prediction of company failure: Past, present and promising directions for the future," International Review of Financial Analysis, Elsevier, vol. 55(C), pages 196-208.
    5. Ehsan Habib Feroz & Taek Mu Kwon & Victor S. Pastena & Kyungjoo Park, 2000. "The efficacy of red flags in predicting the SEC's targets: an artificial neural networks approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(3), pages 145-157, September.
    6. 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.
    7. Ghassan Hossari, 2012. "Optimising errors in signaling corporate collapse using MCCCRA," International Journal of Accounting and Information Management, Emerald Group Publishing, vol. 20(3), pages 300-316, July.
    8. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    9. 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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