Cross-sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock market
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 Fam...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | Financial Innovation |
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| Online Access: | https://doi.org/10.1186/s40854-025-00774-z |
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| _version_ | 1850181131510480896 |
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| author | Zhong‑Qiang Zhou Jiajia Wu Ping Huang Xiong Xiong |
| author_facet | Zhong‑Qiang Zhou Jiajia Wu Ping Huang Xiong Xiong |
| author_sort | Zhong‑Qiang Zhou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7da8b84f39bf47ab90acdd0cb7140e03 |
| institution | OA Journals |
| issn | 2199-4730 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Financial Innovation |
| spelling | doaj-art-7da8b84f39bf47ab90acdd0cb7140e032025-08-20T02:17:58ZengSpringerOpenFinancial Innovation2199-47302025-04-0111112410.1186/s40854-025-00774-zCross-sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock marketZhong‑Qiang Zhou0Jiajia Wu1Ping Huang2Xiong Xiong3School of Applied Economics, Guizhou University of Finance and EconomicsSchool of Applied Economics, Guizhou University of Finance and EconomicsAccounting School, Guizhou University of CommerceCollege of Management and Economics, Tianjin UniversityAbstract 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.https://doi.org/10.1186/s40854-025-00774-zCross-sectional anomaliesConditional asset pricingInvestor sentiment |
| spellingShingle | Zhong‑Qiang Zhou Jiajia Wu Ping Huang Xiong Xiong Cross-sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock market Financial Innovation Cross-sectional anomalies Conditional asset pricing Investor sentiment |
| title | Cross-sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock market |
| title_full | Cross-sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock market |
| title_fullStr | Cross-sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock market |
| title_full_unstemmed | Cross-sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock market |
| title_short | Cross-sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock market |
| title_sort | cross sectional anomalies and conditional asset pricing models based on investor sentiment evidence from the chinese stock market |
| topic | Cross-sectional anomalies Conditional asset pricing Investor sentiment |
| url | https://doi.org/10.1186/s40854-025-00774-z |
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