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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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  • 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.

Suggested Citation

  • Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
  • Handle: RePEc:jof:jforec:v:28:y:2009:i:6:p:512-534
    DOI: 10.1002/for.1109
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    References listed on IDEAS

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    1. repec:eee:touman:v:33:y:2012:i:3:p:622-634 is not listed on IDEAS
    2. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    3. 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.
    4. repec:wly:jforec:v:36:y:2017:i:2:p:109-121 is not listed on IDEAS
    5. repec:ipg:wpaper:2014-473 is not listed on IDEAS
    6. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2016. "The Term Premium as a Leading Macroeconomic Indicator," Working Papers 201613, University of Pretoria, Department of Economics.
    7. 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.
    8. Theophilos Papadimitriou & Periklis Gogas & Vasilios Plakandaras, 2016. "Testing Exchange Rate Models in a Small Open Economy: an SVR Approach," Bulletin of Applied Economics, Risk Market Journals, vol. 3(2), pages 9-29.
    9. repec:eee:reveco:v:51:y:2017:i:c:p:510-526 is not listed on IDEAS
    10. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
    11. Maciej Zieba & Wolfgang K. Härdle, 2016. "Beta-boosted ensemble for big credit scoring data," SFB 649 Discussion Papers SFB649DP2016-052, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    12. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    13. 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.
    14. repec:kap:compec:v:50:y:2017:i:1:d:10.1007_s10614-016-9581-4 is not listed on IDEAS
    15. 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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