| Authors |
Kakhbod, Kazempour, Livdan, Schuerhoff |
| Year |
2023 |
| Type |
Working Paper |
| 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 |
| URL |
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4428232 |
| Tags |
Archival Empirical |
Asset Pricing, Trading Volume and Market Efficiency |
Financing- and Investment Decisions (Individual) |
Media and Textual Analysis |
Propagation of Noise / Undesirable Outcomes
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