The Default Risk of Firms Examined with Smooth Support Vector Machines
AbstractIn 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.
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Bibliographic InfoPaper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 757.
Length: 30 p.
Date of creation: 2007
Date of revision:
Insolvency Prognosis; SVMs; Statistical Learning Theory; Non-parametric Classification;
Other versions of this item:
- Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2008. "The Default Risk of Firms Examined with Smooth Support Vector Machines," SFB 649 Discussion Papers SFB649DP2008-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- G30 - Financial Economics - - Corporate Finance and Governance - - - General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-01-26 (All new papers)
- NEP-BAN-2008-01-26 (Banking)
- NEP-BEC-2008-01-26 (Business Economics)
- NEP-CFN-2008-01-26 (Corporate Finance)
- NEP-ECM-2008-01-26 (Econometrics)
- NEP-RMG-2008-01-26 (Risk Management)
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