|
EconStor >
Deutsches Institut für Wirtschaftsforschung (DIW), Berlin >
DIW-Diskussionspapiere >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10419/27281
|
| | |
Full metadata record
| DC Field | | Value | | Language |
| dc.contributor.author | | Härdle, Wolfgang Karl | | en_US |
| dc.contributor.author | | Lee, Yuh-Jye | | en_US |
| dc.contributor.author | | Schäfer, Dorothea | | en_US |
| dc.contributor.author | | Yeh, Yi-Ren | | en_US |
| dc.date.accessioned | | 2008-01-30 | | en_US |
| dc.date.accessioned | | 2009-08-06T13:19:35Z | | - |
| dc.date.available | | 2009-08-06T13:19:35Z | | - |
| dc.date.issued | | 2007 | | en_US |
| dc.identifier.uri | | http://hdl.handle.net/10419/27281 | | - |
| dc.description.abstract | | In 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_US |
| dc.language.iso | | eng | | en_US |
| dc.publisher | | Deutsches Institut für Wirtschaftsforschung (DIW) Berlin | | en_US |
| dc.relation.ispartofseries | | Discussion papers // German Institute for Economic Research 757 | | en_US |
| dc.subject.jel | | G30 | | en_US |
| dc.subject.jel | | C14 | | en_US |
| dc.subject.jel | | G33 | | en_US |
| dc.subject.jel | | C45 | | en_US |
| dc.subject.ddc | | 330 | | en_US |
| dc.subject.keyword | | Insolvency Prognosis | | en_US |
| dc.subject.keyword | | SVMs | | en_US |
| dc.subject.keyword | | Statistical Learning Theory | | en_US |
| dc.subject.keyword | | Non-parametric Classfication | | en_US |
| dc.subject.stw | | Kreditwürdigkeit | | en_US |
| dc.subject.stw | | Prognoseverfahren | | en_US |
| dc.subject.stw | | Support Vector Machine | | en_US |
| dc.subject.stw | | Theorie | | en_US |
| dc.title | | The default risk of firms examined with Smooth Support Vector Machines; | | en_US |
| dc.type | | Working Paper | | en_US |
| dc.identifier.ppn | | 557548136 | | en_US |
| dc.rights | | http://www.econstor.eu/dspace/Nutzungsbedingungen | | - |
| Appears in Collections: | | Publikationen von Forscherinnen und Forschern des DIW DIW-Diskussionspapiere
|
Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.
|