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Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies

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Author Info

  • Wolfgang Härdle

    (CASE, Humboldt University, Berlin, Germany)

  • Yuh-Jye Lee

    (Department of Computer Science Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan)

  • Dorothea Schäfer

    (German Institute of Economic Research, Berlin, Germany)

  • Yi-Ren Yeh

    (Department of Computer Science Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan)

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 objectives 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 the selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample influence the precision of prediction. Moreover, we show that oversampling can be employed to control the trade-off between error types, and we compare SSVM with both logistic and discriminant analysis. Finally, we illustrate graphically how different models can be used jointly to support the decision-making process of loan officers. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1109
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 28 (2009)
Issue (Month): 6 ()
Pages: 512-534

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Handle: RePEc:jof:jforec:v:28:y:2009:i:6:p:512-534

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.
  2. Vasilios Plakandaras & Rangan Gupta & Periklis Gogas & Theophilos Papadimitriou, 2014. "Forecasting the U.S. Real House Price Index," Working Papers 201418, University of Pretoria, Department of Economics.
  3. Alessandra Amendola & Marialuisa Restaino & Luca Sensini, 2010. "Variable Selection In Forecasting Models For Corporate Bankruptcy," Working Papers 3_216, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
  4. Wolfgang Karl Härdle & Dedy Dwi Prastyo & Christian Hafner, 2012. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," SFB 649 Discussion Papers SFB649DP2012-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  5. Wolfgang Karl Härdle & Dedy Dwi Prastyo, 2013. "Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry," SFB 649 Discussion Papers SFB649DP2013-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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