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dc.contributor.authorAuria, Lauraen_US
dc.contributor.authorMoro, Rouslan A.en_US
dc.date.accessioned2009-04-09en_US
dc.date.accessioned2009-08-06T13:20:16Z-
dc.date.available2009-08-06T13:20:16Z-
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/10419/27334-
dc.description.abstractThis 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.en_US
dc.language.isoengen_US
dc.publisherDeutsches Institut für Wirtschaftsforschung (DIW) Berlinen_US
dc.relation.ispartofseriesDiscussion papers // German Institute for Economic Research 811en_US
dc.subject.jelC13en_US
dc.subject.jelG33en_US
dc.subject.jelC45en_US
dc.subject.ddc330en_US
dc.subject.keywordCompany ratingen_US
dc.subject.keywordbankruptcy analysisen_US
dc.subject.keywordsupport vector machinesen_US
dc.subject.stwKreditwürdigkeiten_US
dc.subject.stwSupport Vector Machineen_US
dc.subject.stwUnternehmenen_US
dc.subject.stwTheorieen_US
dc.subject.stwDeutschlanden_US
dc.titleSupport Vector Machines (SVM) as a technique for solvency analysisen_US
dc.typeWorking Paperen_US
dc.identifier.ppn576821438en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
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