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

Zhong‑Qiang Zhou (), Jiajia Wu (), Ping Huang () and Xiong Xiong ()
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Zhong‑Qiang Zhou: Guizhou University of Finance and Economics
Jiajia Wu: Guizhou University of Finance and Economics
Ping Huang: Guizhou University of Commerce
Xiong Xiong: Tianjin University

Financial Innovation, 2025, vol. 11, issue 1, 1-24

Abstract: Abstract 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.

Keywords: Cross-sectional anomalies; Conditional asset pricing; Investor sentiment (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1186/s40854-025-00774-z

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