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An analysis of the impact of President Trump’s tweets on the DJIA and S&P 500 using machine learning and sentiment analysis

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  • Kinyua, Johnson D.
  • Mutigwe, Charles
  • Cushing, Daniel J.
  • Poggi, Michael

Abstract

We analyze the immediate impact of President Trump’s tweets on two US stock market indices using a 30-minute event window for each tweet and intra-day market data. The tweets and intra-day market data, are used to study market reactions using sentiment analysis, and machine learning (ML) classification and regression. The results show a significant negative reaction when President Trump tweeted during open market hours. We also found that tweets with a strong positive or strong negative sentiment had positive market reactions. ML regressors use the tweets and market data to predict the post-tweet market index averages and post-tweet market trends.

Suggested Citation

  • Kinyua, Johnson D. & Mutigwe, Charles & Cushing, Daniel J. & Poggi, Michael, 2021. "An analysis of the impact of President Trump’s tweets on the DJIA and S&P 500 using machine learning and sentiment analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
  • Handle: RePEc:eee:beexfi:v:29:y:2021:i:c:s2214635020303762
    DOI: 10.1016/j.jbef.2020.100447
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    References listed on IDEAS

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    1. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    2. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    3. 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.
    4. 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.
    5. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    6. Zhipeng Liang & Hao Chen & Junhao Zhu & Kangkang Jiang & Yanran Li, 2018. "Adversarial Deep Reinforcement Learning in Portfolio Management," Papers 1808.09940, arXiv.org, revised Nov 2018.
    7. 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.
    8. David W. Lu, 2017. "Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks," Papers 1707.07338, arXiv.org.
    9. Brown, Gregory W. & Cliff, Michael T., 2004. "Investor sentiment and the near-term stock market," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 1-27, January.
    10. Rui Fan & Oleksandr Talavera & Vu Tran, 2018. "Does connection with @realDonaldTrump affect stock prices?," Working Papers 2018-07, Swansea University, School of Management.
    11. Shtub, Avraham & Versano, Ronen, 1999. "Estimating the cost of steel pipe bending, a comparison between neural networks and regression analysis," International Journal of Production Economics, Elsevier, vol. 62(3), pages 201-207, September.
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    Cited by:

    1. Daniel Perico Ortiz, 2023. "Economic policy statements, social media, and stock market uncertainty: An analysis of Donald Trump’s tweets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(2), pages 333-367, June.
    2. Kumar, Satish & Rao, Sandeep & Goyal, Kirti & Goyal, Nisha, 2022. "Journal of Behavioral and Experimental Finance: A bibliometric overview," Journal of Behavioral and Experimental Finance, Elsevier, vol. 34(C).
    3. Killins, Robert N. & Ngo, Thanh & Wang, Hongxia, 2022. "Politics and equity markets: Evidence from Canada," Journal of Multinational Financial Management, Elsevier, vol. 63(C).
    4. Machus, Tobias & Mestel, Roland & Theissen, Erik, 2022. "Heroes, just for one day: The impact of Donald Trump’s tweets on stock prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 33(C).
    5. Vijay S. Sampath & Arthur J. O’Connor & Calvester Legister, 2022. "Moral leadership and investor attention: An empirical assessment of the potus’s tweets on firms’ market returns," Review of Quantitative Finance and Accounting, Springer, vol. 58(3), pages 881-910, April.
    6. Perico Ortiz, Daniel, 2021. "The high frequency impact of economic policy narratives on stock market uncertainty," FAU Discussion Papers in Economics 02/2021, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

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