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Presidential candidates linguistic tone: The impact on the financial markets

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  • Marinč, Matej
  • Massoud, Nadia
  • Ichev, Riste
  • Valentinčič, Aljoša

Abstract

We investigate U.S. presidential candidates’ unprecedented use of Twitter to compliment on specific firms during election campaigns. We examine the linguistic tone used in presidential candidates’ tweets and we find that it significantly affects stock market reaction. Trump’s tweets bearing positive linguistic tone about specific firms shows daily CAR of 0.20%, whereas the positive tone from all Republican yields a CAR of 0.24%. Linguistic tone effects are particularly significant when media firms are excluded from the sample. Our results suggest that investors recognize the importance for firm value of information transmitted on social media by an influential source.

Suggested Citation

  • Marinč, Matej & Massoud, Nadia & Ichev, Riste & Valentinčič, Aljoša, 2021. "Presidential candidates linguistic tone: The impact on the financial markets," Economics Letters, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:ecolet:v:204:y:2021:i:c:s0165176521001531
    DOI: 10.1016/j.econlet.2021.109876
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    1. Bernard, Vl, 1987. "Cross-Sectional Dependence And Problems In Inference In Market-Based Accounting Research," Journal of Accounting Research, Wiley Blackwell, vol. 25(1), pages 1-48.
    2. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    3. Boehmer, Ekkehart & Masumeci, Jim & Poulsen, Annette B., 1991. "Event-study methodology under conditions of event-induced variance," Journal of Financial Economics, Elsevier, vol. 30(2), pages 253-272, December.
    4. Child, Travers Barclay & Massoud, Nadia & Schabus, Mario & Zhou, Yifan, 2021. "Surprise election for Trump connections," Journal of Financial Economics, Elsevier, vol. 140(2), pages 676-697.
    5. Zheludev, Ilya & Smith, Robert & Aste, Tomaso, 2014. "When can social media lead financial markets?," LSE Research Online Documents on Economics 57376, London School of Economics and Political Science, LSE Library.
    6. Born, Jeffery A. & Myers, David H. & Clark, William J., 2017. "Trump tweets and the efficient Market Hypothesis," Algorithmic Finance, IOS Press, vol. 6(3-4), pages 103-109.
    7. Heleen Brans & Bert Scholtens, 2020. "Under his thumb the effect of president Donald Trump’s Twitter messages on the US stock market," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-11, March.
    8. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    9. Wagner, Alexander F. & Zeckhauser, Richard J. & Ziegler, Alexandre, 2018. "Company stock price reactions to the 2016 election shock: Trump, taxes, and trade," Journal of Financial Economics, Elsevier, vol. 130(2), pages 428-451.
    10. Lily Fang & Joel Peress, 2009. "Media Coverage and the Cross‐section of Stock Returns," Journal of Finance, American Finance Association, vol. 64(5), pages 2023-2052, October.
    11. Qi Ge & Alexander Kurov & Marketa Halova Wolfe, 2019. "Do Investors Care About Presidential Company‐Specific Tweets?," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 42(2), pages 213-242, July.
    12. Jennifer Conrad & Bradford Cornell & Wayne R. Landsman, 2002. "When Is Bad News Really Bad News?," Journal of Finance, American Finance Association, vol. 57(6), pages 2507-2532, December.
    13. 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.
    14. Carl Ajjoub & Thomas Walker & Yunfei Zhao, 2020. "Social media posts and stock returns: The Trump factor," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 17(2), pages 185-213, June.
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    More about this item

    Keywords

    Twitter; Linguistic tone; Event study; Stock price reaction; Investor sentiment;
    All these keywords.

    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
    • P16 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Capitalist Institutions; Welfare State

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