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Retail Trading and Return Predictability in China

Published online by Cambridge University Press:  12 February 2024

Charles M. Jones ,
Donghui Shi ,
Xiaoyan Zhang  and
Xinran Zhang Open the ORCID record for Xinran Zhang [Opens in a new window]
Charles M. Jones
Affiliation:
Columbia Business School cj88@gsb.columbia.edu
Donghui Shi
Affiliation:
Fudan University Fanhai International School of Finance dhshi@fudan.edu.cn
Xiaoyan Zhang*
Affiliation:
Tsinghua University PBC School of Finance
Xinran Zhang
Affiliation:
Central University of Finance and Economics School of Finance zhangxinran@cufe.edu.cn
*
zhangxiaoyan@pbcsf.tsinghua.edu.cn (corresponding author)
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Abstract

Using comprehensive account-level data, we separate Chinese retail investors into 5 groups and document strong heterogeneity in trading dynamics and performances. Retail investors with smaller account sizes cannot predict future returns correctly, display daily momentum patterns, fail to process public news, and show overconfidence and gambling preferences, while retail investors with larger account balances predict future returns correctly, display contrarian patterns, and incorporate public news in trading. Using performance measures established in previous literature, we find that smaller retail investors suffer from poor stock selection abilities and trading costs, while large retail investors’ stock selection abilities are offset by trading costs.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , First View , pp. 1 - 37
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

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Footnotes

We thank an anonymous referee, Hendrik Bessembinder (the editor), Utpal Bhattacharya, Darwin Choi, Lauren H. Cohen, Ron Kaniel, Terrance Odean, Xintong Zhan, Hao Zhou, and seminar participants at Tsinghua PBC School of Finance, Renmin University, Shanghai Jiaotong University, Fudan University, Shanghai University of Finance and Economics, and conference audiences at the 2019 CIFFP, 2021 CFRC, 2021 CICF, and 2022 ABFER Annual Conferences for their helpful comments and suggestions. Xiaoyan Zhang acknowledges the financial support from the National Natural Science Foundation of China (Grant Nos. 72350710220 and 71790605). Xinran Zhang acknowledges the financial support from the National Natural Science Foundation of China (Grant No. 72303268). All remaining errors are our own.

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