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Finding Anomalies in China

Author

Listed:
  • Hou, Kewei

    (Ohio State U)

  • Qiao, Fang

    (U of International Business and Economics, Beijing)

  • Zhang, Xiaoyan

    (Tsinghua U)

Abstract

To study the cross-section of returns in the Chinese stock market, we follow the anomaly literature and construct 454 strategies between 2000 and 2020, based on 208 firm-level trading and accounting signals. With the conventional single-testing t-statistic cutoff of 1.96, 101 strategies have significant value-weighted raw returns, and 20 remain significant after risk adjustments. To avoid false discoveries, we recalibrate the t-statistic cutoff to 2.85 to accommodate multiple testing. 36 strategies survive the higher hurdle rate in value-weighted raw returns, while none remains significant after risk adjustments. When we use machine learning techniques to combine information from multiple signals, the resulting composite strategies mostly have significant returns after risk adjustments, even with the higher t-statistic cutoff. We relate Chinese anomaly returns to aggregate economic conditions and find that they comove with financial market development, accounting quality, market liquidity, and government regulations.

Suggested Citation

  • Hou, Kewei & Qiao, Fang & Zhang, Xiaoyan, 2023. "Finding Anomalies in China," Working Paper Series 2023-02, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    • Handle: RePEc:ecl:ohidic:2023-02
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    More about this item

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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