57 Pages Posted: 3 May 2023 Last revised: 4 May 2023
Date Written: March 1, 2023
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 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' belief bias. Consequently, finfluencers cause excessive trading and inefficient prices such that a contrarian strategy yields 1.2% monthly out-of-sample performance.
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