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Predicting Extreme Financial Risks on Imbalanced Dataset: A Combined Kernel FCM and Kernel SMOTE Based SVM Classifier

Author

Listed:
  • Xun Huang

    (Chengdu University
    Institute of Chinese Financial Studies, Southwest University of Finance and Economics)

  • Cheng-Zhao Zhang

    (Chengdu Polytechnic)

  • Jia Yuan

    (Chengdu Institute of Public Administration)

Abstract

Extreme financial risk prediction is an important component of risk management in financial markets. In this study, taking the China Securities Index 300 (CSI300) as an example, we set out to introduce the kernel method into fuzzy c-mean algorithm (FCM) and synthetic minority over-sampling technique (SMOTE) and combine them with support vector machine (SVM) to propose a hybrid model of KFCM-KSMOTE-SVM for predicting extreme financial risks, which is compared with other various prediction models. In addition, we investigate the influence on the prediction performance of KFCM-KSMOTE-SVM exerted by its parameters. The empirical results present that KFCM-KSMOTE-SVM outperforms other various prediction models significantly, which verifies that KFCM-KSMOTE-SVM can solve the class imbalance problem in financial markets and is more appropriate for predicting extreme financial risks. Meanwhile, parameter set plays an important role in constructing KFCM-KSMOTE-SVM prediction model. Besides, the experiment on Shanghai Stock Exchange Composite Index also proves that KFCM-KSMOTE-SVM has strong robustness on predicting extreme financial risks.

Suggested Citation

  • Xun Huang & Cheng-Zhao Zhang & Jia Yuan, 2020. "Predicting Extreme Financial Risks on Imbalanced Dataset: A Combined Kernel FCM and Kernel SMOTE Based SVM Classifier," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 187-216, June.
    • Handle: RePEc:kap:compec:v:56:y:2020:i:1:d:10.1007_s10614-020-09975-3
    DOI: 10.1007/s10614-020-09975-3
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    5. Haitao Lu & Xiaofeng Hu, 2024. "Enhancing Financial Risk Prediction for Listed Companies: A Catboost-Based Ensemble Learning Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 9824-9840, June.

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