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Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation

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  • Ligang Zhou
  • Kin Lai
  • Jerome Yen

Abstract

Due to the economic significance of bankruptcy prediction of companies for financial institutions, investors and governments, many quantitative methods have been used to develop effective prediction models. Support vector machine (SVM), a powerful classification method, has been used for this task; however, the performance of SVM is sensitive to model form, parameter setting and features selection. In this study, a new approach based on direct search and features ranking technology is proposed to optimise features selection and parameter setting for 1-norm and least-squares SVM models for bankruptcy prediction. This approach is also compared to the SVM models with parameter optimisation and features selection by the popular genetic algorithm technique. The experimental results on a data set with 2010 instances show that the proposed models are good alternatives for bankruptcy prediction.

Suggested Citation

  • Ligang Zhou & Kin Lai & Jerome Yen, 2014. "Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(3), pages 241-253.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:3:p:241-253
    DOI: 10.1080/00207721.2012.720293
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    Cited by:

    1. Gianfranco Lombardo & Mattia Pellegrino & George Adosoglou & Stefano Cagnoni & Panos M. Pardalos & Agostino Poggi, 2022. "Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks," Future Internet, MDPI, vol. 14(8), pages 1-23, August.
    2. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
    3. Xiaobo Tang & Shixuan Li & Mingliang Tan & Wenxuan Shi, 2020. "Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 769-787, August.
    4. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    5. A. Mendes & R. L. Cardoso & P. C. Mário & A. L. Martinez & F. R. Ferreira, 2014. "Insolvency Prediction In The Presence Of Data Inconsistencies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(3), pages 155-167, July.
    6. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).

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