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Social media bots and stock markets

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  • Rui Fan
  • Oleksandr Talavera
  • Vu Tran

Abstract

This study examines the link between information spread by social media bots and stock trading. Based on a large sample of tweets mentioning 55 companies in the FTSE 100 composites, we find significant relations between bot tweets and stock returns, volatility, and trading volume at both daily and intraday levels. These results are also confirmed by an event study of stock response following abnormal increases in the volume of tweets. The findings are robust to various specifications, including controlling for traditional news channel, alternative measures of volatility, information flows in pretrading hours, and different measures of sentiment.

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  • Rui Fan & Oleksandr Talavera & Vu Tran, 2020. "Social media bots and stock markets," European Financial Management, European Financial Management Association, vol. 26(3), pages 753-777, June.
    • Handle: RePEc:bla:eufman:v:26:y:2020:i:3:p:753-777
    DOI: 10.1111/eufm.12245
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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. What moves markets more, Twitter or traditional news?
      by ? in EUROPP European Politics and Policy on 2018-12-08 07:29:35

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    Cited by:

    1. Menghan Zhang & Xue Qi & Ze Chen & Jun Liu, 2022. "Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter," IJERPH, MDPI, vol. 19(3), pages 1-14, January.
    2. Costas Milas & Theodore Panagiotidis & Theologos Dergiades, 2018. "Twitter versus Traditional News Media: Evidence for the Sovereign Bond Markets," Working Paper series 18-42, Rimini Centre for Economic Analysis.
    3. Rui Fan & Oleksandr Talavera & Vu Tran, 2023. "Social media and price discovery: The case of cross‐listed firms," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(1), pages 151-167, February.
    4. Fan, Rui & Talavera, Oleksandr & Tran, Vu, 2023. "Information flows and the law of one price," International Review of Financial Analysis, Elsevier, vol. 85(C).
    5. Bennett, Donyetta & Mekelburg, Erik & Williams, T.H., 2023. "BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing," Research in International Business and Finance, Elsevier, vol. 65(C).
    6. Costas Milas & Theodore Panagiotidis & Theologos Dergiades, 2021. "Does It Matter Where You Search? Twitter versus Traditional News Media," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(7), pages 1757-1795, October.
    7. Liang, Qi & Sun, Wenjia & Li, Wenyu & Yu, Fengyan, 2021. "Media effects matter: Macroeconomic announcements in the gold futures market," Economic Modelling, Elsevier, vol. 96(C), pages 1-12.
    8. Kai-Cheng Yang & Emilio Ferrara & Filippo Menczer, 2022. "Botometer 101: social bot practicum for computational social scientists," Journal of Computational Social Science, Springer, vol. 5(2), pages 1511-1528, November.
    9. Dosumu, Oluwatoyin Esther & Sakariyahu, Rilwan & Oyekola, Olayinka & Lawal, Rodiat, 2023. "Panic bank runs, global market contagion and the financial consequences of social media," Economics Letters, Elsevier, vol. 228(C).

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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