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Social media effect, investor recognition and the cross-section of stock returns

Meng, Xiangtong; Zhang, Wei; Li, Youwei; Cao, Xing; Feng, Xu

Authors

Xiangtong Meng

Wei Zhang

Xing Cao

Xu Feng



Abstract

Investor recognition affects cross-sectional stock returns. In informationally incomplete markets, investors have limited recognition of all securities, and their holding of stocks with low recognition requires compensation for being imperfectly diversified. Using the number of posts on the Chinese social media platform Guba to measure investor recognition of stocks, this paper provides a direct test of Merton’s investor recognition hypothesis. We find a significant social media premium in the Chinese stock market. We further find that including a social media factor based on this premium significantly improves the explanatory power of Fama-French factor models of cross-sectional stock returns, and these results are robust when we control for the mass media effect and liquidity effect. Finally, we find that investment strategies based on the social media factor earn sizable risk-adjusted returns, which signifies the importance of the social media premium in portfolio management.

Citation

Meng, X., Zhang, W., Li, Y., Cao, X., & Feng, X. (2020). Social media effect, investor recognition and the cross-section of stock returns. International review of financial analysis, https://doi.org/10.1016/j.irfa.2019.101432

Journal Article Type Article
Acceptance Date Nov 26, 2019
Online Publication Date Dec 9, 2019
Publication Date 2020-01
Deposit Date Dec 9, 2019
Publicly Available Date Jun 10, 2021
Journal International Review of Financial Analysis
Print ISSN 1057-5219
Publisher Elsevier
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1016/j.irfa.2019.101432
Keywords Social media; Investor recognition; Asset pricing
Public URL https://hull-repository.worktribe.com/output/3303544
Publisher URL https://www.sciencedirect.com/science/article/pii/S1057521919304818?via%3Dihub

Files

This file is under embargo until Jun 10, 2021 due to copyright reasons.

Contact Youwei.Li@hull.ac.uk to request a copy for personal use.




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