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The Default Risk of Firms Examined with Smooth Support Vector Machines

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

No 757, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

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 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.

Keywords: Insolvency Prognosis; SVMs; Statistical Learning Theory; Non-parametric Classification (search for similar items in EconPapers)
JEL-codes: G30 C14 G33 C45 (search for similar items in EconPapers)
Pages: 30 p.
Date: 2007
New Economics Papers: this item is included in nep-ban, nep-bec, nep-cfn, nep-ecm and nep-rmg
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