Support Vector Machines (SVM) as a Technique for Solvency Analysis
affiliation not provided to SSRN
R. A. Moro
German Institute for Economic Research (DIW Berlin); Humboldt University of Berlin - School of Business and Economics
August 1, 2008
DIW Berlin Discussion Paper No. 811
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.
Number of Pages in PDF File: 18
Keywords: company rating, bankruptcy analysis, support vector machines
JEL Classification: C13, G33, C45working papers series
Date posted: June 25, 2009
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