Business failure prediction: a comparison of classification methodsBusiness failure prediction: a comparison of classification methods Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-182002enBusiness failure prediction is one of the most essential problems in the field of finance. The research on developing business failure prediction models has been focused on building classification models to distinguish among failed and non—failed firms. Such models are of major importance to financial decision makers (credit managers, managers of firms, investors, etc.); they serve as early warning systems of the failure probability of a corporate entity. The significance of business failure prediction models has been a major motivation for researchers to develop efficient approaches for the development of such models. This paper considers several such approaches, including multicriteria decision aid (MCDA) techniques, linear programming and performs a thorough comparison to traditional statistical techniques such as linear discriminant analysis and logit analysis. The comparison is performed using a sample of 144 US firms for a period of up to five years prior to failure.http://creativecommons.org/licenses/by/4.0/Operational Research, An International Journal23303-319 Michael Doumpos Δουμπος Μιχαλης Zopounidis Konstantinos Ζοπουνιδης Κωνσταντινος Springer Verlag Business failure prediction Multicriteria decision aid Multivariate statistical techniques Comparison