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<p>This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models. Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression, we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market. Our results demonstrate that conditional models significantly outperform their unconditional counterparts. Notably, investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period. Additionally, it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs. We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events.</p>

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Cross-sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock market

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月16日

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  • Zhong‑Qiang Zhou,
  • Jiajia Wu,
  • Ping Huang,
  • Xiong Xiong

摘要

This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models. Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression, we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market. Our results demonstrate that conditional models significantly outperform their unconditional counterparts. Notably, investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period. Additionally, it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs. We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events.