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Investor Attention and Stock Returns

Published online by Cambridge University Press:  05 November 2021

Jian Chen Open the ORCID record for Jian Chen [Opens in a new window] ,
Guohao Tang Open the ORCID record for Guohao Tang [Opens in a new window] ,
Jiaquan Yao  and
Guofu Zhou Open the ORCID record for Guofu Zhou [Opens in a new window]
Jian Chen
Affiliation:
Xiamen University School of Economicsjchenl@xmu.edu.cn
Guohao Tang
Affiliation:
Hunan University College of Finance and Statisticsghtang@hnu.edu.cn
Jiaquan Yao*
Affiliation:
Jinan University School of Management
Guofu Zhou
Affiliation:
Washington University in St. Louis Olin School of Businesszhou@wustl.edu
*
jiaquanyao@gmail.com
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Abstract

We propose an investor attention index based on proxies in the literature and find that it predicts the stock market risk premium significantly, both in sample and out of sample, whereas every proxy individually has little predictive power. The index is extracted using partial least squares, but the results are similar by the scaled principal component analysis. Moreover, the index can deliver sizable economic gains for mean-variance investors in asset allocation. The predictive power of the investor attention index stems primarily from the reversal of temporary price pressure and from the stronger forecasting ability for high-variance stocks.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 57 , Issue 2 , March 2022 , pp. 455 - 484
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

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Footnotes

We are grateful to Hendrik Bessembinder (the editor) and an anonymous referee for very insightful and helpful comments that improved the article substantially. We also thank Koustav De (discussant), Joey Engelberg, Michael Hasler, Tse-Chun Lin, Lin Peng, Dragon Tang, and Qunzi Zhang (discussant); seminar participants at Beijing University, Emory University, Georgia State University, Indiana University, Renmin University of China, Shanghai University of Finance and Economics, Sichuan University, Southwestern University of Finance and Economics, Washington University in St. Louis, and Zhejiang University; and conference participants at the 2018 Conference on Financial Predictability and Big Data, the 2019 China Finance Review International Conference, the 2019 Chinese Finance Annual Meeting, and the 2019 Financial Management Association Annual Meeting for insightful comments. Chen, Tang, and Yao acknowledge financial support from the National Natural Science Foundation of China (Grants 71671148, 72003062, and 71502152), respectively. Part of the work was undertaken while Chen and Tang were visiting Washington University in St. Louis.

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