Business failure prediction using decision trees

Adrian Gepp, Kuldeep Kumar*, Sukanto Bhattacharya

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

71 Citations (Scopus)
2 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)536-555
Number of pages20
JournalJournal of Forecasting
Volume29
Issue number6
Early online date25 Nov 2009
DOIs
Publication statusPublished - Sep 2010

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