IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v204y2021ics0165176521001531.html
   My bibliography  Save this article

Presidential candidates linguistic tone: The impact on the financial markets

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176521001531
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2021.109876?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anand, Abhinav & Pathak, Jalaj, 2022. "The role of Reddit in the GameStop short squeeze," Economics Letters, Elsevier, vol. 211(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pham, Duong Phuong Thao & Huynh, Ngoc Quang Anh & Duong, Duy, 2022. "The impact of US presidents on market returns: Evidence from Trump's tweets," Research in International Business and Finance, Elsevier, vol. 62(C).
    2. David Kreitmeir & Nathan Lane & Paul A. Raschky, 2020. "The Value of Names - Civil Society, Information, and Governing Multinationals on the Global Periphery," SoDa Laboratories Working Paper Series 2020-10, Monash University, SoDa Laboratories.
    3. 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.
    4. 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.
    5. Huynh, Toan Luu Duc, 2021. "Does Bitcoin React to Trump’s Tweets?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).
    6. Barakat, Ahmed & Ashby, Simon & Fenn, Paul & Bryce, Cormac, 2019. "Operational risk and reputation in financial institutions: Does media tone make a difference?," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 1-24.
    7. Francisco Guijarro & Ismael Moya-Clemente & Jawad Saleemi, 2019. "Liquidity Risk and Investors’ Mood: Linking the Financial Market Liquidity to Sentiment Analysis through Twitter in the S&P500 Index," Sustainability, MDPI, vol. 11(24), pages 1-13, December.
    8. Ferdinand Graf, 2011. "Mechanically Extracted Company Signals and their Impact on Stock and Credit Markets," Working Paper Series of the Department of Economics, University of Konstanz 2011-18, Department of Economics, University of Konstanz.
    9. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    10. Yuting Chen & Don Bredin & Valerio Potì & Roman Matkovskyy, 2022. "COVID risk narratives: a computational linguistic approach to the econometric identification of narrative risk during a pandemic," Digital Finance, Springer, vol. 4(1), pages 17-61, March.
    11. 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.
    12. Jūra Liaukonytė & Alminas Žaldokas, 2022. "Background Noise? TV Advertising Affects Real-Time Investor Behavior," Management Science, INFORMS, vol. 68(4), pages 2465-2484, April.
    13. Tsai, Feng-Tse & Lu, Hsin-Min & Hung, Mao-Wei, 2016. "The impact of news articles and corporate disclosure on credit risk valuation," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 100-116.
    14. Wu, Yanling & Tian, Gary Gang, 2021. "Public relations expenditure, media tone, and regulatory decisions," Journal of Corporate Finance, Elsevier, vol. 66(C).
    15. Du, Hanyu & Hao, Jing & He, Feng & Xi, Wenze, 2022. "Media sentiment and cross-sectional stock returns in the Chinese stock market," Research in International Business and Finance, Elsevier, vol. 60(C).
    16. Zhi Da & Borja Larrain & Clemens Sialm & José Tessada, 2016. "Coordinated Noise Trading: Evidence from Pension Fund Reallocations," NBER Working Papers 22161, National Bureau of Economic Research, Inc.
    17. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    18. Stefan Claus & Massimo Stella, 2022. "Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts," Future Internet, MDPI, vol. 14(10), pages 1-18, October.
    19. Klaus, Jürgen & Koser, Christoph, 2021. "Measuring Trump: The Volfefe Index and its impact on European financial markets," Finance Research Letters, Elsevier, vol. 38(C).
    20. Ankur Sinha & Satishwar Kedas & Rishu Kumar & Pekka Malo, 2022. "SEntFiN 1.0: Entity‐aware sentiment analysis for financial news," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(9), pages 1314-1335, September.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:204:y:2021:i:c:s0165176521001531. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.