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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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    Cited by:

    1. 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.
    2. 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.
    3. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    4. 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.
    5. Jie Sun, 2012. "Integration Of Random Sample Selection, Support Vector Machines And Ensembles For Financial Risk Forecasting With An Empirical Analysis On The Necessity Of Feature Selection," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(4), pages 229-246, October.
    6. Li, Hui & Sun, Jie, 2012. "Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples – Evidence from the Chinese hotel industry," Tourism Management, Elsevier, vol. 33(3), pages 622-634.
    7. 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.
    8. 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.
    9. 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.
    10. Kea BARET & Theophilos PAPADIMITRIOU, 2019. "On the Stability and Growth Pact compliance: what is predictable with machine learning?," Working Papers of BETA 2019-48, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    11. Ligang Zhou & Kin Keung Lai, 2017. "AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 69-94, June.
    12. Giacomo Caterini, 2018. "Classifying Firms with Text Mining," DEM Working Papers 2018/09, Department of Economics and Management.
    13. 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.
    14. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2019. "Forecasting transportation demand for the U.S. market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 195-214.
    15. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.
    16. Tian, Shaonan & Yu, Yan, 2017. "Financial ratios and bankruptcy predictions: An international evidence," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 510-526.
    17. repec:ipg:wpaper:2014-473 is not listed on IDEAS
    18. 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.
    19. Vasilios Plakandaras & Elie Bouri & Rangan Gupta, 2019. "Forecasting Bitcoin Returns: Is there a Role for the U.S. – China Trade War?," Working Papers 201980, University of Pretoria, Department of Economics.
    20. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    21. 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.

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