Social Media Analysts' Skill: Evidence from Text-Implied Beliefs

Journal of Financial and Quantitative Analysis, forthcoming

75 Pages Posted: 29 Mar 2021 Last revised: 9 Dec 2024

See all articles by Chukwuma Dim

Chukwuma Dim

George Washington University

Date Written: December 17, 2020

Abstract

This paper documents that 56% of nonprofessional social media investment analysts (SMAs) are skilled and declare beliefs that generate positive abnormal returns, while 44% produce negative abnormal returns. 13% of all SMAs are high-skill type and produce a one-week three-factor alpha of 61 bps, while the remaining 87% generate only 6 bps. The distinctive features of high-skill SMAs are primarily firm and industry specializations. Although SMAs tend to extrapolate and herd, their expectations are not systematically wrong. For higher-skilled SMAs compared to the less-skilled ones, extrapolation fades more quickly, and herding is lower, consistent with theory.

Keywords: Nonprofessional analysts, Expectation formation, Social networks, Social finance, Machine learning, Textual analysis

JEL Classification: G11, G12, G14

Suggested Citation

Dim, Chukwuma, Social Media Analysts' Skill: Evidence from Text-Implied Beliefs (December 17, 2020). Journal of Financial and Quantitative Analysis, forthcoming, Available at SSRN: https://ssrn.com/abstract=3813252 or http://dx.doi.org/10.2139/ssrn.3813252

Chukwuma Dim (Contact Author)

George Washington University ( email )

2121 I Street NW
Washington, DC 20052
United States

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