IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2101.02587.html
   My bibliography  Save this paper

Mining the Relationship Between COVID-19 Sentiment and Market Performance

Author

Listed:
  • Ziyuan Xia
  • Jeffery Chen
  • Anchen Sun

Abstract

At the beginning of the COVID-19 outbreak in March, we observed one of the largest stock market crashes in history. Within the months following this, a volatile bullish climb back to pre-pandemic performances and higher. In this paper, we study the stock market behavior during the initial few months of the COVID-19 pandemic in relation to COVID-19 sentiment. Using text sentiment analysis of Twitter data, we look at tweets that contain key words in relation to the COVID-19 pandemic and the sentiment of the tweet to understand whether sentiment can be used as an indicator for stock market performance. There has been previous research done on applying natural language processing and text sentiment analysis to understand the stock market performance, given how prevalent the impact of COVID-19 is to the economy, we want to further the application of these techniques to understand the relationship that COVID-19 has with stock market performance. Our findings show that there is a strong relationship to COVID-19 sentiment derived from tweets that could be used to predict stock market performance in the future.

Suggested Citation

  • Ziyuan Xia & Jeffery Chen & Anchen Sun, 2021. "Mining the Relationship Between COVID-19 Sentiment and Market Performance," Papers 2101.02587, arXiv.org, revised Mar 2023.
    • Handle: RePEc:arx:papers:2101.02587
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2101.02587
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Baek, Seungho & Mohanty, Sunil K. & Glambosky, Mina, 2020. "COVID-19 and stock market volatility: An industry level analysis," Finance Research Letters, Elsevier, vol. 37(C).
    2. Salisu, Afees A. & Vo, Xuan Vinh, 2020. "Predicting stock returns in the presence of COVID-19 pandemic: The role of health news," International Review of Financial Analysis, Elsevier, vol. 71(C).
    3. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.
    4. Topcu, Mert & Gulal, Omer Serkan, 2020. "The impact of COVID-19 on emerging stock markets," Finance Research Letters, Elsevier, vol. 36(C).
    5. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    6. Keane, Michael & Neal, Timothy, 2021. "Consumer panic in the COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 220(1), pages 86-105.
    7. Scott R Baker & Nicholas Bloom & Steven J Davis & Kyle Kost & Marco Sammon & Tasaneeya Viratyosin & Jeffrey Pontiff, 0. "The Unprecedented Stock Market Reaction to COVID-19," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(4), pages 742-758.
    8. repec:pri:cepsud:91malkiel is not listed on IDEAS
    9. B. W. Wanjawa & L. Muchemi, 2014. "ANN Model to Predict Stock Prices at Stock Exchange Markets," Papers 1502.06434, arXiv.org.
    10. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    11. Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2020. "Time Series Analysis of COVID-19 Infection Curve: A Change-Point Perspective," Papers 2007.04553, arXiv.org.
    12. Jiayu Qiu & Bin Wang & Changjun Zhou, 2020. "Forecasting stock prices with long-short term memory neural network based on attention mechanism," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-15, January.
    13. Haroon, Omair & Rizvi, Syed Aun R., 2020. "COVID-19: Media coverage and financial markets behavior—A sectoral inquiry," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    14. Min Liu & Wei-Chong Choo & Chien-Chiang Lee, 2020. "The Response of the Stock Market to the Announcement of Global Pandemic," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(15), pages 3562-3577, December.
    15. Fama, Eugene F, et al, 1969. "The Adjustment of Stock Prices to New Information," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 10(1), pages 1-21, February.
    16. 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.
    17. Viktor Stojkoski & Zoran Utkovski & Petar Jolakoski & Dragan Tevdovski & Ljupco Kocarev, 2020. "Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: Evidence from Bayesian model averaging," Papers 2004.07947, arXiv.org, revised Jan 2022.
    18. Gunther Capelle-Blancard & Adrien Desroziers, 2020. "The stock market is not the economy? Insights from the Covid-19 crisis," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03252106, HAL.
    19. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    20. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    Full references (including those not matched with items on IDEAS)

    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. Mahata, Ajit & Rai, Anish & Nurujjaman, Md. & Prakash, Om, 2021. "Modeling and analysis of the effect of COVID-19 on the stock price: V and L-shape recovery," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    2. Umar, Zaghum & Manel, Youssef & Riaz, Yasir & Gubareva, Mariya, 2021. "Return and volatility transmission between emerging markets and US debt throughout the pandemic crisis," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    3. Díaz, Fernando & Henríquez, Pablo A. & Winkelried, Diego, 2022. "Stock market volatility and the COVID-19 reproductive number," Research in International Business and Finance, Elsevier, vol. 59(C).
    4. Jialei Jiang & Eun-Mi Park & Seong-Taek Park, 2021. "The Impact of the COVID-19 on Economic Sustainability—A Case Study of Fluctuation in Stock Prices for China and South Korea," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
    5. Ziliotto, Arianna & Serati, Massimiliano, 2015. "The semi-strong efficiency debate: In search of a new testing framework," Research in International Business and Finance, Elsevier, vol. 34(C), pages 412-438.
    6. Roland Rothenstein, 2018. "Quantification of market efficiency based on informational-entropy," Papers 1812.02371, arXiv.org.
    7. Yashraj Varma & Renuka Venkataramani & Parthajit Kayal & Moinak Maiti, 2021. "Short-Term Impact of COVID-19 on Indian Stock Market," JRFM, MDPI, vol. 14(11), pages 1-15, November.
    8. Naidu, Dharmendra & Ranjeeni, Kumari, 2021. "Effect of coronavirus fear on the performance of Australian stock returns: Evidence from an event study," Pacific-Basin Finance Journal, Elsevier, vol. 66(C).
    9. Bazán-Palomino, Walter & Winkelried, Diego, 2021. "FX markets’ reactions to COVID-19: Are they different?," International Economics, Elsevier, vol. 167(C), pages 50-58.
    10. Khoury Rim El, 2019. "The Cac 40 Index’S Reaction To Terrorist Attacks: The Case Of Charlie Hebdo," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 14(2), pages 55-72, August.
    11. Nunes, Mauro Fracarolli, 2018. "Supply chain contamination: An exploratory approach on the collateral effects of negative corporate events," European Management Journal, Elsevier, vol. 36(4), pages 573-587.
    12. Afees A. Salisu & Ahamuefula E. Ogbonna & Tirimisiyu F. Oloko & Idris A. Adediran, 2021. "A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    13. Mikio Ito & Akihiko Noda & Tatsuma Wada, 2016. "The evolution of stock market efficiency in the US: a non-Bayesian time-varying model approach," Applied Economics, Taylor & Francis Journals, vol. 48(7), pages 621-635, February.
    14. Daniel Martin Katz & Michael J Bommarito II & Tyler Soellinger & James Ming Chen, 2015. "Law on the Market? Abnormal Stock Returns and Supreme Court Decision-Making," Papers 1508.05751, arXiv.org, revised May 2017.
    15. Michael Heinrich Baumann, 2022. "Beating the market? A mathematical puzzle for market efficiency," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 45(1), pages 279-325, June.
    16. Tihana Škrinjarić, 2021. "Profiting on the Stock Market in Pandemic Times: Study of COVID-19 Effects on CESEE Stock Markets," Mathematics, MDPI, vol. 9(17), pages 1-20, August.
    17. Dionysia Dionysiou, 2015. "Choosing Among Alternative Long-Run Event-Study Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 158-198, February.
    18. Brada, Josef C. & Gajewski, Paweł & Kutan, Ali M., 2021. "Economic resiliency and recovery, lessons from the financial crisis for the COVID-19 pandemic: A regional perspective from Central and Eastern Europe," International Review of Financial Analysis, Elsevier, vol. 74(C).
    19. Taufiq Choudhry & Ranadeva Jayasekera, 2015. "Level of efficiency in the UK equity market: empirical study of the effects of the global financial crisis," Review of Quantitative Finance and Accounting, Springer, vol. 44(2), pages 213-242, February.
    20. Admin Starcevic & Timothy Rodgers, 2011. "Market Efficiency within the German Stock Market: A Comparative Study of the Relative Efficiencies of the DAX, MDAX, SDAX and ASE Indices," International Econometric Review (IER), Econometric Research Association, vol. 3(1), pages 25-37, April.

    More about this item

    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:arx:papers:2101.02587. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.