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Business failure prediction using decision trees
Journal of forecasting
  • Adrian Gepp, Bond University
  • Kuldeep Kumar, Bond University
  • Sukanto Bhattacharya, Deakin University
Date of this Version
Document Type
Journal Article
Publication Details

Submitted version - Uncorrected Proof

Gepp, A., Kumar, K. & Bhattacharya, S. (2009). Business failure prediction using decision tress. Journal of forecasting, 20pp.

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2009 HERDC submission. FoR code: 1403

© Copyright 2009 John Wiley & Sons, Ltd.

This is the pre-peer reviewed version of the article article, which has been published in final form at This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

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.
Citation Information
Adrian Gepp, Kuldeep Kumar and Sukanto Bhattacharya. "Business failure prediction using decision trees" Journal of forecasting Vol. 29 Iss. 6 (2009) p. 1 - 21 ISSN: 1099-131X
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