Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/25247
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dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.contributor.authorLee, Yuh-Jyeen_US
dc.contributor.authorSchäfer, Dorotheaen_US
dc.contributor.authorYeh, Yi-Renen_US
dc.date.accessioned2008-02-20en_US
dc.date.accessioned2009-07-23T15:03:30Z-
dc.date.available2009-07-23T15:03:30Z-
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/10419/25247-
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 show that 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_US
dc.language.isoengen_US
dc.publisher|aSFB 649, Economic Risk|cBerlinen_US
dc.relation.ispartofseries|aSFB 649 discussion paper|x2008,005en_US
dc.subject.jelG30en_US
dc.subject.jelC14en_US
dc.subject.jelG33en_US
dc.subject.jelC45en_US
dc.subject.ddc330en_US
dc.subject.keywordInsolvency Prognosisen_US
dc.subject.keywordSVMsen_US
dc.subject.keywordStatistical Learning Theoryen_US
dc.subject.keywordNon-parametric Classificationen_US
dc.subject.stwKreditwürdigkeiten_US
dc.subject.stwPrognoseverfahrenen_US
dc.subject.stwSupport Vector Machineen_US
dc.subject.stwTheorieen_US
dc.titleThe default risk of firms examined with smooth support vector machinesen_US
dc.type|aWorking Paperen_US
dc.identifier.ppn558749143en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-

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