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Business failure prediction using decision trees

Author

Listed:
  • Adrian Gepp

    (School of Business, Bond University, Gold Coast, Queensland, Australia)

  • Kuldeep Kumar

    (School of Business, Bond University, Gold Coast, Queensland, Australia)

  • Sukanto Bhattacharya

    (Deakin Business School, Deakin University, Burwood, Victoria, Australia)

Abstract

Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly financial investment and lending. The potential value of such models is emphasised by the extremely costly failure of high-profile companies in the recent past. Consequently, a significant interest has been generated in business failure prediction within academia as well as in the finance industry. Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses have traditionally been the most popular approaches, but there are also a range of promising non-parametric techniques that can alternatively be applied. In this paper, the relatively new technique of decision trees is applied to business failure prediction. The numerical results suggest that decision trees could be superior predictors of business failure as compared to discriminant analysis. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Adrian Gepp & Kuldeep Kumar & Sukanto Bhattacharya, 2010. "Business failure prediction using decision trees," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(6), pages 536-555.
    • Handle: RePEc:jof:jforec:v:29:y:2010:i:6:p:536-555
    DOI: 10.1002/for.1153
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    References listed on IDEAS

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