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The Effects of Donald Trump’s Tweets on The Stock Exchange

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Listed:
  • Yardley, Ben

Abstract

Donald Trump is a huge personality in a unique social, political and financial situation. Through twitter he is able to influence his following instantly with no regulation, often with large unforeseen repercussions. The aim of this report is to identify Trump’s company specific tweets, quantify the mood and feeling of the tweets through sentiment analysis and then investigate the correlation between this and the effect is on the stock prices of these companies. This will be done through a regression of the sentiment and the returns of the company over serval time periods. Further investigation will the take place in order to analyse how neutrally classified tweets are impacted, as well as the effect of the number likes and retweets a tweet has on the stock price of a company.

Suggested Citation

  • Yardley, Ben, 2020. "The Effects of Donald Trump’s Tweets on The Stock Exchange," MPRA Paper 102578, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:102578
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    References listed on IDEAS

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    1. Gili Yen & Cheng-few Lee, 2008. "Efficient Market Hypothesis (EMH): Past, Present and Future," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 305-329.
    2. Dang, Ha V. & Lin, Mi, 2016. "Herd mentality in the stock market: On the role of idiosyncratic participants with heterogeneous information," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 247-260.
    3. repec:bla:jfinan:v:43:y:1988:i:4:p:1041-48 is not listed on IDEAS
    4. Michael Rechenthin & W. Nick Street & Padmini Srinivasan, 2013. "Stock chatter: Using stock sentiment to predict price direction," Algorithmic Finance, IOS Press, vol. 2(3-4), pages 169-196.
    5. 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.
    6. Cheng, Pao L & Deets, M King, 1971. "Portfolio Returns and the Random Walk Theory," Journal of Finance, American Finance Association, vol. 26(1), pages 11-30, March.
    7. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    8. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    9. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    10. repec:pri:cepsud:91malkiel is not listed on IDEAS
    11. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    12. Fama, Eugene F., 1998. "Market efficiency, long-term returns, and behavioral finance," Journal of Financial Economics, Elsevier, vol. 49(3), pages 283-306, September.
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    JEL classification:

    • A14 - General Economics and Teaching - - General Economics - - - Sociology of Economics
    • A22 - General Economics and Teaching - - Economic Education and Teaching of Economics - - - Undergraduate
    • G0 - Financial Economics - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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