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 costly 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 have been the most popular approaches, but there are also a large number of alternative techniques available. In this paper, a comparatively new technique known as survival analysis has been used for business failure prediction. In addition, hybrid models combining survival analysis with either discriminant analysis or logit analysis were trialled, but their empirical performance was poor. Overall, the results suggest that survival analysis techniques provide more information that can be used to further the understanding of the business failure process.
|Number of pages||22|
|Journal||International Research Journal of Finance and Economics|
|Publication status||Published - 1 Jun 2008|
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Financial statement fraud detection using supervised learning methodsAuthor: Gepp, A., 10 Oct 2015
Supervisor: Kumar, K. (Supervisor) & Bhattacharya, S. (External person) (Supervisor)
Student thesis: Doctoral ThesisFile