Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/27281 
Erscheinungsjahr: 
2007
Schriftenreihe/Nr.: 
DIW Discussion Papers No. 757
Verlag: 
Deutsches Institut für Wirtschaftsforschung (DIW), Berlin
Zusammenfassung: 
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.
Schlagwörter: 
Insolvency Prognosis
SVMs
Statistical Learning Theory
Non-parametric Classfication
JEL: 
G30
C14
G33
C45
Dokumentart: 
Working Paper

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