Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/27281 
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dc.contributor.authorHärdle, Wolfgang Karlen
dc.contributor.authorLee, Yuh-Jyeen
dc.contributor.authorSchäfer, Dorotheaen
dc.contributor.authorYeh, Yi-Renen
dc.date.accessioned2008-01-30-
dc.date.accessioned2009-08-06T13:19:35Z-
dc.date.available2009-08-06T13:19:35Z-
dc.date.issued2007-
dc.identifier.urihttp://hdl.handle.net/10419/27281-
dc.description.abstractIn the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample influence the precision of prediction. Furthermore we showthat oversampling can be employed to gear the tradeoff between error types. Finally, we illustrate graphically how different variants of SSVM can be used jointly to support the decision task of loan officers.en
dc.language.isoengen
dc.publisher|aDeutsches Institut für Wirtschaftsforschung (DIW) |cBerlinen
dc.relation.ispartofseries|aDIW Discussion Papers |x757en
dc.subject.jelG30en
dc.subject.jelC14en
dc.subject.jelG33en
dc.subject.jelC45en
dc.subject.ddc330en
dc.subject.keywordInsolvency Prognosisen
dc.subject.keywordSVMsen
dc.subject.keywordStatistical Learning Theoryen
dc.subject.keywordNon-parametric Classficationen
dc.subject.stwKreditwürdigkeiten
dc.subject.stwPrognoseverfahrenen
dc.subject.stwSupport Vector Machineen
dc.subject.stwTheorieen
dc.titleThe default risk of firms examined with Smooth Support Vector Machines;-
dc.type|aWorking Paperen
dc.identifier.ppn557548136en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:diw:diwwpp:dp757en

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