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Detecting stock market manipulation via machine learning: Evidence from China Securities Regulatory Commission punishment cases

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  • Liu, Qingbai
  • Wang, Chuanjie
  • Zhang, Ping
  • Zheng, Kaixin

Abstract

In this paper, we apply machine-learning techniques to construct detecting models of stock market manipulation. By combining manually collected China Securities Regulatory Commission punishment cases from 2014 to 2016 with financial information of listed companies, we construct a training set and a test set to compare the detecting ability of support vector machine (SVM) and logistic model. Considering imbalanced data, we further incorporate Borderline Synthetic Minority Oversampling Technique (Borderline SMOTE) to oversample minority class and then find that Borderline SMOTE–SVM performs better than SVM and benchmark model in detecting manipulation. To enhance detecting performance of the models, we innovatively introduce market sentiment indicators which are extracted from analyst rating reports, financial news, and Guba comments into our indicators set. The results indicate that the new indicators generate significant marginal increment to the model accuracy.

Suggested Citation

  • Liu, Qingbai & Wang, Chuanjie & Zhang, Ping & Zheng, Kaixin, 2021. "Detecting stock market manipulation via machine learning: Evidence from China Securities Regulatory Commission punishment cases," International Review of Financial Analysis, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:finana:v:78:y:2021:i:c:s1057521921002143
    DOI: 10.1016/j.irfa.2021.101887
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    Cited by:

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    3. Yuna Hao & Behrang Vand & Benjamin Manrique Delgado & Simone Baldi, 2023. "Market Manipulation in Stock and Power Markets: A Study of Indicator-Based Monitoring and Regulatory Challenges," Energies, MDPI, vol. 16(4), pages 1-28, February.
    4. Pan, Shuiyang & Long, Suwan(Cheng) & Wang, Yiming & Xie, Ying, 2023. "Nonlinear asset pricing in Chinese stock market: A deep learning approach," International Review of Financial Analysis, Elsevier, vol. 87(C).
    5. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
    6. Shilpa Srivastava & Millie Pant & Varuna Gupta, 2023. "Analysis and prediction of Indian stock market: a machine-learning approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1567-1585, August.
    7. Jia, Haibo & Xue, Jun, 2024. "Relationship between administrative punishment and corporate debt financing," Finance Research Letters, Elsevier, vol. 64(C).
    8. Carvajal-Patiño, Daniel & Ramos-Pollán, Raul, 2022. "Synthetic data generation with deep generative models to enhance predictive tasks in trading strategies," Research in International Business and Finance, Elsevier, vol. 62(C).
    9. Wei Liu & Yoshihisa Suzuki & Shuyi Du, 2024. "Forecasting the Stock Price of Listed Innovative SMEs Using Machine Learning Methods Based on Bayesian optimization: Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2035-2068, May.
    10. Yang, Jinyu & Dong, Dayong & Cao, Jiawei, 2024. "Seemingly manipulated anomaly: Evidence from corporate site visits," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    11. Zhu, Xingting & Ma, Xiang & Rehman, Faheem Ur & Liu, Bin, 2024. "Does pension fund ownership reduce market manipulation? Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).

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