Business Failure Prediction: A Review and Analysis of the Literature

Abstract

Business failure prediction is a topic of great importance for a lot of people (shareholders, banks, investors, suppliers,…). That’s why a lot of models were developed in order to predict it. Statistical procedures (multiple discriminant analysis, logit or probit) were among the most used methods in this kind of problem. However, parametric statistical methods require the data to have a specific distribution. In addition to the restriction on the distribution involved, multi-collinearity, autocorrelation and heteroscedasticity could lead to problems with the estimated model with some statistical methods. Because of these drawbacks, others methods have been investigated: multicriteria methods (i.e. UTA, Electre tri,…) or machine learning methods (i.e. neural network, genetic algorithm, decision tree, instance based learning,…). Our main target is to provide a review of the literature but also to have a larger view than usually by evoking causes, symptoms and remedies of bankruptcy. We also proposed new perspectives and topics of research.

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