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Machine learning, anomalies, and the expected market return: Evidence from China

Author

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  • Du, Qingjie
  • Wang, Yang
  • Wei, Chishen
  • Wei, K.C. John

Abstract

We investigate whether machine learning (ML) techniques that forecast overall U.S. market returns using cross-sectional stock return anomalies in Dong et al. (2022) are useful for the China equity market. We successfully forecast out-of-sample R2 of the market return in China using a combined version of ordinary least squares and an elastic net model. However, the other four ML methods cannot forecast the market return. Overall, our exercise highlights the potential of ML techniques, but also calls for future research to rule out the possibility of model mining.

Suggested Citation

  • Du, Qingjie & Wang, Yang & Wei, Chishen & Wei, K.C. John, 2023. "Machine learning, anomalies, and the expected market return: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:pacfin:v:82:y:2023:i:c:s0927538x23002391
    DOI: 10.1016/j.pacfin.2023.102168
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    References listed on IDEAS

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    1. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
    4. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    5. Titman, Sheridan & Wei, Chishen & Zhao, Bin, 2022. "Corporate actions and the manipulation of retail investors in China: An analysis of stock splits," Journal of Financial Economics, Elsevier, vol. 145(3), pages 762-787.
    6. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    7. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
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    Cited by:

    1. Wang, Jianqiu & Wang, Zhuo & Wu, Ke, 2025. "Forecasting stock market return with anomalies: Evidence from China," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1278-1295.

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    Keywords

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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