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http://dspace.mediu.edu.my:8181/xmlui/handle/10419/18111Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.creator | Schäfer, Dirk | - |
| dc.creator | Moro, R. A. | - |
| dc.creator | Härdle, Wolfgang Karl | - |
| dc.date | 2004 | - |
| dc.date.accessioned | 2013-10-16T06:58:20Z | - |
| dc.date.available | 2013-10-16T06:58:20Z | - |
| dc.date.issued | 2013-10-16 | - |
| dc.identifier | http://hdl.handle.net/10419/18111 | - |
| dc.identifier | ppn:385540310 | - |
| dc.identifier.uri | http://koha.mediu.edu.my:8181/xmlui/handle/10419/18111 | - |
| dc.description | The goal of this work is to introduce one of the most successful among recently developed statistical techniques – the support vector machine (SVM) – to the field of corporate bankruptcy analysis. The main emphasis is done on implementing SVMs for analysing predictors in the form of financial ratios. A method is proposed of adapting SVMs to default probability estimation. A survey of practically and commercially applied methods is given. This work proves that support vector machines are capable of extracting useful information from financial data although extensive data sets are required in order to fully utilise their classification power. | - |
| dc.language | eng | - |
| dc.publisher | Deutsches Institut für Wirtschaftsforschung (DIW) Berlin | - |
| dc.relation | DIW-Diskussionspapiere 416 | - |
| dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | - |
| dc.subject | C45 | - |
| dc.subject | G33 | - |
| dc.subject | C14 | - |
| dc.subject | ddc:330 | - |
| dc.subject | Support vector machines | - |
| dc.subject | Company rating | - |
| dc.subject | Default probability estimation | - |
| dc.subject | Kreditwürdigkeit | - |
| dc.subject | Mustererkennung | - |
| dc.subject | Schätzung | - |
| dc.subject | Theorie | - |
| dc.subject | Vereinigte Staaten | - |
| dc.subject | support vector machine | - |
| dc.title | Rating Companies with Support Vector Machines | - |
| dc.type | doc-type:workingPaper | - |
| Appears in Collections: | EconStor | |
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