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Government Stock Purchase Undermines Price Informativeness: Evidence from China’s “National Team”

Published online by Cambridge University Press:  08 May 2023

Tri Vi Dang ,
Wei Li Open the ORCID record for Wei Li [Opens in a new window]  and
Yongqin Wang
Tri Vi Dang
Affiliation:
Columbia University Department of Economics td2332@columbia.edu
Wei Li*
Affiliation:
University of International Business and Economics School of Insurance and Economics
Yongqin Wang
Affiliation:
Fudan University China Center for Economic Studies School of Economics and Fanhai International School of Finance (FISF) yongqinwang@fudan.edu.cn
*
weili.uibe@gmail.com (corresponding author)
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Abstract

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We use the 2015 Chinese stock market crash to study the effects of government stock purchases. The Chinese government purchased stocks to stabilize the markets through state-owned financial institutions known as the “National Team.” We find that the intervention led to reduced volatility and price informativeness. These impacts are driven by the disclosure of government portfolios. Consistent with investors having a stronger incentive to acquire government intervention information instead of fundamental news, we find reduced information production and information asymmetry following intervention disclosure. The article suggests that government stock purchases involve a trade-off between stability and informational efficiency.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 59 , Issue 5 , August 2024 , pp. 2340 - 2374
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We would like to thank Xiongfeng Ai, John Campbell, Greg Cox, Yun Dai, Julan Du, Hanming Fang, Huasheng Gao, Oliver Hart, Hua He, Zhiguo He, Harrison Hong, Stephen Morris, Greg Phelan, Yi Shen, Zhe Shen, Kaisi Sun (discussant), Lucy White, Ruojun Wu, Wei Xiong, Kai Yang, Siyuan Yang (discussant), Xiaoyang Zhuo (discussant), as well as seminar participants at Fudan University, Harvard University, Xiamen University, East China Normal University, 2019 TCFA conference, 2021 Camphor English Conference for Finance, 2022 Financial Engineering and Quantitative Finance Conference (Xiamen University), 2022 Asian Meeting of the Econometric Society in China (AMES 2022), and 2022 AsianFA Annual Conference for helpful comments and suggestions. We are particularly grateful to Jennifer Conrad (the editor), Martijn Cremers (a referee), and Yuhang Xing (a referee) for their insightful suggestions that tremendously improved the paper. All errors are our own. Dang acknowledges financial support from Hong Kong Institute for Monetary and Financial Research (HKIMR). Li acknowledges the financial support from the National Natural Science Foundation of China (Grant No. 72303067). Wang acknowledges financial support from the Natural Science Foundation of China (72073034 and 72121002).

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