IDEAS home Printed from https://ideas.repec.org/p/swn/wpaper/2018-07.html
   My bibliography  Save this paper

Does connection with @realDonaldTrump affect stock prices?

Author

Listed:
  • Rui Fan

    () (School of Management, Swansea University)

  • Oleksandr Talavera

    () (School of Management, Swansea University)

  • Vu Tran

    () (School of Management, Swansea University)

Abstract

This study investigates whether investors react to signals of an association between a firm and Donald Trump indicated by tweets containing both words 'Trump' (or '@realDonaldTrump') and a S&P500 firm name. Our results reveal that a large number of such tweets ignite speculations about a political connection between a firm and the US's President, thus affecting investors' trading behaviors. In particular, a rise in these tweets induces significant increases in trading volatility and trading volume. However, we do not find such great impacts on stock returns, suggesting that the speculations are more likely to spread among small non-institutional investors. Further investigations show that sentiments embedded in these tweets have limited influence. There is evidence of a changing market behavior towards such tweets centered by the President's inauguration.

Suggested Citation

  • Rui Fan & Oleksandr Talavera & Vu Tran, 2018. "Does connection with @realDonaldTrump affect stock prices?," Working Papers 2018-07, Swansea University, School of Management.
    • Handle: RePEc:swn:wpaper:2018-07
    as

    Download full text from publisher

    File URL: https://rahwebdav.swan.ac.uk/repec/pdf/WP2018-07.pdf
    File Function: First version, 2018
    Download Restriction: no

    References listed on IDEAS

    as
    1. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    2. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    3. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    4. Mara Faccio, 2010. "Differences between Politically Connected and Nonconnected Firms: A Cross‐Country Analysis," Financial Management, Financial Management Association International, vol. 39(3), pages 905-928, September.
    5. Lily H. Fang & Joel Peress & Lu Zheng, 2014. "Does Media Coverage of Stocks Affect Mutual Funds' Trading and Performance?," Review of Financial Studies, Society for Financial Studies, vol. 27(12), pages 3441-3466.
    6. Yuriy Gorodnichenko & Tho Pham & Oleksandr Talavera, 2018. "Social Media, Sentiment and Public Opinions: Evidence from #Brexit and #USElection," NBER Working Papers 24631, National Bureau of Economic Research, Inc.
    7. Ruben Enikolopov & Maria Petrova & Konstantin Sonin, 2018. "Social Media and Corruption," American Economic Journal: Applied Economics, American Economic Association, vol. 10(1), pages 150-174, January.
    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. Timm O. Sprenger & Philipp G. Sandner & Andranik Tumasjan & Isabell M. Welpe, 2014. "News or Noise? Using Twitter to Identify and Understand Company-specific News Flow," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 41(7-8), pages 791-830, September.
    10. Werner Antweiler & Murray Z. Frank, 2004. "Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards," Journal of Finance, American Finance Association, vol. 59(3), pages 1259-1294, June.
    11. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    12. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    13. Narjess Boubakri & Jean-Claude Cosset & Walid Saffar, 2012. "The Impact Of Political Connections On Firms’ Operating Performance And Financing Decisions," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 35(3), pages 397-423, September.
    14. 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.
    15. Hirshleifer, David & Teoh, Siew Hong, 2003. "Limited attention, information disclosure, and financial reporting," Journal of Accounting and Economics, Elsevier, vol. 36(1-3), pages 337-386, December.
    16. Casey Dougal & Joseph Engelberg & Diego García & Christopher A. Parsons, 2012. "Journalists and the Stock Market," Review of Financial Studies, Society for Financial Studies, vol. 25(3), pages 639-679.
    17. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    18. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
    19. Joseph E. Engelberg & Christopher A. Parsons, 2011. "The Causal Impact of Media in Financial Markets," Journal of Finance, American Finance Association, vol. 66(1), pages 67-97, February.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Twitter; US Election; stock market; investor sentiment; text classification; computational linguistics.;

    JEL classification:

    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:swn:wpaper:2018-07. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Syed Shabi-Ul-Hassan). General contact details of provider: http://edirc.repec.org/data/edswauk.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.