Support Vector Machines (SVM) as a Technique for Solvency Analysis

Laura Auria and Rouslan A. Moro

No 811, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

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.

Keywords: Company rating; bankruptcy analysis; support vector machines (search for similar items in EconPapers)
JEL-codes: C13 C45 G33 (search for similar items in EconPapers)
Pages: 16 p.
Date: 2008
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-rmg
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Citations: View citations in EconPapers (23)

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