Download This PaperFinfluencers
57 Pages Posted: 3 May 2023 Last revised: 18 Jul 2023
Date Written: July 5, 2023
Abstract
Tweet-level data from a social media platform reveals low average accuracy and high dispersion in the quality of advice by financial influencers, or “finfluencers”: 28% of finfluencers are skilled, generating 2.6% monthly abnormal returns, 16% are unskilled, and 56% have negative skill (“antiskill”) generating -2.3% monthly abnormal returns. Consistent with homophily shaping finfluencers’ social networks, antiskilled finfluencers have more followers and more influence on retail trading than skilled finfluencers. The advice by antiskilled finfluencers creates overly optimistic beliefs most times and persistent swings in followers’ beliefs. Consequently, finfluencers cause excessive trading and inefficient prices such that a contrarian strategy yields 1.2% monthly out-of-sample performance
Keywords: Finfluencers, social media, mixture modeling, retail traders, homophily, belief bias
JEL Classification: G12, G14, G41
Suggested Citation: Suggested Citation
Dmitry Livdan
University of California, Berkeley ( email )
545 Student Services Building, #1900
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Berkeley, CA 94720
United States
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