JOURNAL ARTICLE

Partisan Return Gap: The Polarized Stock Market in the Time of a Pandemic.

  • Published In: Management Science (INFORMS), 2024, v. 70, n. 8. P. 5091 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Sheng, Jinfei; Sun, Zheng; Wang, Wanyi 3 of 3

Abstract

This article investigates the impact of political polarization on stock returns during the early COVID-19 pandemic by examining differences between firms likely dominated by Democratic (blue stocks) and Republican (red stocks) investors. Using proxies such as firms’ headquarter locations and social-connection-based partisanship (SCP), the study finds that red stocks earned about 20 basis points higher risk-adjusted returns than blue stocks on days with significant COVID-related news, a phenomenon termed the Partisan Return Gap. Fundamental factors like lockdown policies, COVID cases, and firm profitability explain at most 40% of this gap, while behavioral factors linked to polarized political beliefs—measured through social distancing behavior—account for an additional 40%. The return gap is more pronounced on days with high partisan disagreement and is robust across various measures of partisanship, including mutual fund holdings inferred from political contributions. These findings highlight political partisanship as a significant driver of abnormal stock returns during the pandemic, extending the understanding of how political beliefs influence financial markets.

Additional Information

  • Source:Management Science (INFORMS). 2024/08, Vol. 70, Issue 8, p5091
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2023.4913
  • Accession Number:178947314
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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