Arbeitspapier
The default risk of firms examined with Smooth Support Vector Machines;
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: DIW Discussion Papers ; No. 757
- Klassifikation
-
Wirtschaft
Corporate Finance and Governance: General
Semiparametric and Nonparametric Methods: General
Bankruptcy; Liquidation
Neural Networks and Related Topics
- Thema
-
Insolvency Prognosis
SVMs
Statistical Learning Theory
Non-parametric Classfication
Kreditwürdigkeit
Prognoseverfahren
Support Vector Machine
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Härdle, Wolfgang Karl
Lee, Yuh-Jye
Schäfer, Dorothea
Yeh, Yi-Ren
- Ereignis
-
Veröffentlichung
- (wer)
-
Deutsches Institut für Wirtschaftsforschung (DIW)
- (wo)
-
Berlin
- (wann)
-
2007
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Härdle, Wolfgang Karl
- Lee, Yuh-Jye
- Schäfer, Dorothea
- Yeh, Yi-Ren
- Deutsches Institut für Wirtschaftsforschung (DIW)
Entstanden
- 2007
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