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

18 Pages Posted: 25 Jun 2009

See all articles by Laura Auria

Laura Auria

affiliation not provided to SSRN

R. A. Moro

German Institute for Economic Research (DIW Berlin); Humboldt University of Berlin - School of Business and Economics

Date Written: August 1, 2008

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

JEL Classification: C13, G33, C45

Suggested Citation

Auria, Laura and Moro, R. A., Support Vector Machines (SVM) as a Technique for Solvency Analysis (August 1, 2008). DIW Berlin Discussion Paper No. 811. Available at SSRN: https://ssrn.com/abstract=1424949 or http://dx.doi.org/10.2139/ssrn.1424949

Laura Auria (Contact Author)

affiliation not provided to SSRN

R. A. Moro

German Institute for Economic Research (DIW Berlin) ( email )

Mohrenstraße 58
Berlin, 10117
Germany

Humboldt University of Berlin - School of Business and Economics

Spandauer Str. 1
Berlin, D-10099
Germany

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