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Title:Support Vector Machines (SVM) as a technique for solvency analysis PDF Logo
Authors:Auria, Laura
Moro, Rouslan A.
Issue Date:2008
Series/Report no.:Discussion papers // German Institute for Economic Research 811
Abstract:This paper introduces a statistical technique, Support Vector Machines (SVM), which is considered by the Deutsche Bundesbank as an alternative for company rating. A special attention is paid to the features of the SVM which provide a higher accuracy of company classification into solvent and insolvent. The advantages and disadvantages of the method are discussed. The comparison of the SVM with more traditional approaches such as logistic regression (Logit) and discriminant analysis (DA) is made on the Deutsche Bundesbank data of annual income statements and balance sheets of German companies. The out-of-sample accuracy tests confirm that the SVM outperforms both DA and Logit on bootstrapped samples.
Subjects:Company rating
bankruptcy analysis
support vector machines
Document Type:Working Paper
Appears in Collections:DIW Diskussionspapiere
Publikationen von Forscherinnen und Forschern des DIW Berlin

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