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Do actions speak louder than words? Evidence from microblogs

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  • Goutte, Maud-Rose

Abstract

This research identifies the determinants of investors’ future beliefs by analyzing more than 50 million tweets on thousands of stocks from the microblogging platform StockTwits. To do so, I divide tweets into two categories: beliefs, representing the average sentiment of all investors regarding a particular stock, and actions, representing the actual transactions disclosed by StockTwits’ users in their tweets. The results show that investors’ next-period beliefs are positively impacted both by beliefs and actions of the current day. The quality of the investment advice strengthens this effect. More communication between investors leads to greater diversity in beliefs and more uncertainty.

Suggested Citation

  • Goutte, Maud-Rose, 2022. "Do actions speak louder than words? Evidence from microblogs," Journal of Behavioral and Experimental Finance, Elsevier, vol. 33(C).
    • Handle: RePEc:eee:beexfi:v:33:y:2022:i:c:s2214635021001611
    DOI: 10.1016/j.jbef.2021.100619
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    References listed on IDEAS

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    1. Foerster, Manuel, 2018. "Finite languages, persuasion bias, and opinion fluctuations," Journal of Economic Behavior & Organization, Elsevier, vol. 149(C), pages 46-57.
    2. Timm O. Sprenger & Andranik Tumasjan & Philipp G. Sandner & Isabell M. Welpe, 2014. "Tweets and Trades: the Information Content of Stock Microblogs," European Financial Management, European Financial Management Association, vol. 20(5), pages 926-957, November.
    3. Benjamin Enke & Florian Zimmermann, 2019. "Correlation Neglect in Belief Formation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(1), pages 313-332.
    4. Siganos, Antonios & Vagenas-Nanos, Evangelos & Verwijmeren, Patrick, 2017. "Divergence of sentiment and stock market trading," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 130-141.
    5. Glenn Ellison & Drew Fudenberg, 1995. "Word-of-Mouth Communication and Social Learning," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 93-125.
    6. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    7. Andrei, Daniel & Cujean, Julien, 2017. "Information percolation, momentum and reversal," Journal of Financial Economics, Elsevier, vol. 123(3), pages 617-645.
    8. Leonardo Bursztyn & Florian Ederer & Bruno Ferman & Noam Yuchtman, 2014. "Understanding Mechanisms Underlying Peer Effects: Evidence From a Field Experiment on Financial Decisions," Econometrica, Econometric Society, vol. 82(4), pages 1273-1301, July.
    9. Barberis, Nicholas & Greenwood, Robin & Jin, Lawrence & Shleifer, Andrei, 2015. "X-CAPM: An extrapolative capital asset pricing model," Journal of Financial Economics, Elsevier, vol. 115(1), pages 1-24.
    10. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    11. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 909-968.
    12. Pedro Bordalo & Nicola Gennaioli & Rafael La Porta & Andrei Shleifer, 2019. "Diagnostic Expectations and Stock Returns," Journal of Finance, American Finance Association, vol. 74(6), pages 2839-2874, December.
    13. Sebastien Pouget & Julien Sauvagnat & Stephane Villeneuve, 2017. "A Mind Is a Terrible Thing to Change: Confirmatory Bias in Financial Markets," The Review of Financial Studies, Society for Financial Studies, vol. 30(6), pages 2066-2109.
    14. Broadstock, David C. & Zhang, Dayong, 2019. "Social-media and intraday stock returns: The pricing power of sentiment," Finance Research Letters, Elsevier, vol. 30(C), pages 116-123.
    15. Pooya Molavi & Alireza Tahbaz‐Salehi & Ali Jadbabaie, 2018. "A Theory of Non‐Bayesian Social Learning," Econometrica, Econometric Society, vol. 86(2), pages 445-490, March.
    16. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    17. Harrison Hong & Jeffrey D. Kubik & Jeremy C. Stein, 2005. "Thy Neighbor's Portfolio: Word‐of‐Mouth Effects in the Holdings and Trades of Money Managers," Journal of Finance, American Finance Association, vol. 60(6), pages 2801-2824, December.
    18. Devenow, Andrea & Welch, Ivo, 1996. "Rational herding in financial economics," European Economic Review, Elsevier, vol. 40(3-5), pages 603-615, April.
    19. Zhi Da & Xing Huang, 2020. "Harnessing the Wisdom of Crowds," Management Science, INFORMS, vol. 66(5), pages 1847-1867, May.
    20. Chen, Cathy Yi-Hsuan & Després, Roméo & Guo, Li & Renault, Thomas, 2019. "What makes cryptocurrencies special? Investor sentiment and return predictability during the bubble," IRTG 1792 Discussion Papers 2019-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    21. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
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    More about this item

    Keywords

    Sentiment analysis; NLP; Text analysis; Microblogs; Text classification; Herding; Word-of-mouth;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G40 - Financial Economics - - Behavioral Finance - - - General

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