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dc.contributor.authorAuria, Lauraen
dc.contributor.authorMoro, Rouslan A.en
dc.date.accessioned2009-04-09-
dc.date.accessioned2009-08-06T13:20:16Z-
dc.date.available2009-08-06T13:20:16Z-
dc.date.issued2008-
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
dc.language.isoengen
dc.publisher|aDeutsches Institut für Wirtschaftsforschung (DIW) |cBerlinen
dc.relation.ispartofseries|aDIW Discussion Papers |x811en
dc.subject.jelC13en
dc.subject.jelG33en
dc.subject.jelC45en
dc.subject.ddc330en
dc.subject.keywordCompany ratingen
dc.subject.keywordbankruptcy analysisen
dc.subject.keywordsupport vector machinesen
dc.subject.stwKreditwürdigkeiten
dc.subject.stwSupport Vector Machineen
dc.subject.stwUnternehmenen
dc.subject.stwTheorieen
dc.subject.stwDeutschlanden
dc.titleSupport Vector Machines (SVM) as a technique for solvency analysis-
dc.type|aWorking Paperen
dc.identifier.ppn576821438en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:diw:diwwpp:dp811en

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