Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/25247 
Year of Publication: 
2008
Series/Report no.: 
SFB 649 Discussion Paper No. 2008,005
Publisher: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
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 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.
Subjects: 
Insolvency Prognosis
SVMs
Statistical Learning Theory
Non-parametric Classification
JEL: 
G30
C14
G33
C45
Document Type: 
Working Paper

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