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The Default Risk of Firms Examined with Smooth Support Vector Machines

Discussion Papers 757, 30 S.

Wolfgang Härdle, Yuh-Jye Lee, Dorothea Schäfer, Yi-Ren Yeh

2007. Dec.

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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.

Dorothea Schäfer

Research Director Financial Markets in the Communications Department

JEL-Classification: G30;C14;G33;C45
Keywords: Insolvency Prognosis, SVMs, Statistical Learning Theory, Non-parametric Classification
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