Politically Affiliated Analysts

Author:

Kong Dongmin12ORCID,Lin Chen3ORCID,Liu Shasha4ORCID,Xuan Yuhai5ORCID

Affiliation:

1. School of Finance, Zhongnan University of Economics and Law, Wuhan 430073, China;

2. School of Economics, Huazhong University of Science and Technology, Wuhan 430074, China;

3. Faculty of Business and Economics, The University of Hong Kong, Pokfulam, Hong Kong;

4. School of Management, Jinan University, Guangzhou 510632, China;

5. The Paul Merage School of Business, University of California, Irvine, Irvine, California 92697

Abstract

Government ownership of financial intermediaries is pervasive around the world. In this study, we examine the impact of common government ownership between the brokerage and listed firms on the information production role of brokerage firms. We show that affiliated analysts tend to issue more optimistic recommendations for stocks of firms controlled by the same government entity that controls their brokerage firms. This optimistic bias is particularly pronounced during periods of economic shocks. Our study demonstrates this by utilizing additional tariff impositions and tariff exemptions during the U.S.–China trade war as exogenous negative and positive shocks, respectively. Additionally, our study indicates that stocks recommended by politically affiliated analysts tend to underperform those recommended by independent analysts, implying that the optimism stems from conflicts of interest rather than superior information. Furthermore, our research highlights that sophisticated investors perceive the potential bias and incorporate it into their trading. Consistent with an exchange of favors story, politically affiliated brokerage firms receive a larger allocation during the issuance of local government debt, whereas governments subscribe for more shares during seasoned equity offerings by these affiliated brokerage firms. This paper was accepted by Kay Giesecke, finance. Funding: The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China [Grants 71972088, 72132002, and 71991473], the National Social Science Foundation of China [Grants 21ZDA010 and 22VRC145], and the Innovation and Talent Base for Digital Technology and Finance [Grant B21038]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00579 .

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

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