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Business failure prediction using decision trees

  • 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)

Registered author(s):

    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.

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    File URL: http://hdl.handle.net/10.1002/for.1153
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    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 29 (2010)
    Issue (Month): 6 ()
    Pages: 536-555

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    Handle: RePEc:jof:jforec:v:29:y:2010:i:6:p:536-555
    DOI: 10.1002/for.1153
    Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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