Business failure prediction using statistical techniques: A review

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Abstract

Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly in financial investment and lending. The potential value of such models has been recently emphasised by the extremely cosdy failure of high profile businesses in both Australia and overseas, such as HIH (Australia) and Enron (USA). Consequently, there has been a significant increase in interest in business failure prediction from both industry and academia.

Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses are the most popular approaches, and there are also a large number of alternatives. In this paper, the various techniques used in previous studies are presented and reviewed, including two alternative techniques that have produced promising results, namely sur vival analysis and decision trees.
Original languageEnglish
Title of host publicationSome recent developments in statistical theory and applications
EditorsK. Kumar, A. Chaturvedi
Place of PublicationFlorida
PublisherBrown Walker Press
Pages1-25
Number of pages25
ISBN (Print)9781612335735
Publication statusPublished - 2012

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Gepp, A., & Kumar, K. (2012). Business failure prediction using statistical techniques: A review. In K. Kumar, & A. Chaturvedi (Eds.), Some recent developments in statistical theory and applications (pp. 1-25). Florida: Brown Walker Press.