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Finfluencers

Author

Listed:
  • Ali Kakhbod

    (University of California, Berkeley)

  • Seyed Mohammad Kazempour

    (Rice University-Jesse H. Jones Graduate School of Business)

  • Dmitry Livdan

    (University of California, Berkeley)

  • Norman Schuerhoff

    (Swiss Finance Institute - HEC Lausanne)

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 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.

Suggested Citation

  • Ali Kakhbod & Seyed Mohammad Kazempour & Dmitry Livdan & Norman Schuerhoff, 2023. "Finfluencers," Swiss Finance Institute Research Paper Series 23-30, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2330
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