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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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    Citations

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    Cited by:

    1. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01314553, HAL.
    2. repec:eee:touman:v:33:y:2012:i:3:p:622-634 is not listed on IDEAS
    3. Misund, Bård, 2015. "Financial Ratios and Prediction on Corporate Bankruptcy in the Atlantic Salmon Industry," UiS Working Papers in Economics and Finance 2015/9, University of Stavanger.
    4. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Documents de travail du Centre d'Economie de la Sorbonne 16026, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    5. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    6. Carlos Serrano-Cinca & Yolanda Fuertes-Callén & Begoña Gutiérrez-Nieto & Beatriz Cuellar-Fernández, 2014. "Path modelling to bankruptcy: causes and symptoms of the banking crisis," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3798-3811, November.
    7. repec:hal:journl:halshs-01314553 is not listed on IDEAS
    8. Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.

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