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Machine learning sentiment analysis, Covid-19 news and stock market reactions

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  • Costola, Michele
  • Nofer, Michael
  • Hinz, Oliver
  • Pelizzon, Loriana

Abstract

The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the "Natural Language Toolkit" that uses machine learning models to extract the news' sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms: MarketWatch.com, Reuters.com and NYtimes.com. Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.

Suggested Citation

  • Costola, Michele & Nofer, Michael & Hinz, Oliver & Pelizzon, Loriana, 2020. "Machine learning sentiment analysis, Covid-19 news and stock market reactions," SAFE Working Paper Series 288, Leibniz Institute for Financial Research SAFE.
    • Handle: RePEc:zbw:safewp:288
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    8. Thavavel Vaiyapuri & Sharath Kumar Jagannathan & Mohammed Altaf Ahmed & K. C. Ramya & Gyanendra Prasad Joshi & Soojeong Lee & Gangseong Lee, 2023. "Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    9. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2023. "Which COVID-19 information really impacts stock markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    10. Li, Yanshuang & Shi, Yujie & Shi, Yongdong & Xiong, Xiong & Yi, Shangkun, 2024. "Time-frequency extreme risk spillovers between COVID-19 news-based panic sentiment and stock market volatility in the multi-layer network: Evidence from the RCEP countries," International Review of Financial Analysis, Elsevier, vol. 94(C).
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    15. Ahelegbey, Daniel Felix & Cerchiello, Paola & Scaramozzino, Roberta, 2022. "Network based evidence of the financial impact of Covid-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 81(C).
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    17. Wasim ul Rehman & Omur Saltik & Faryal Jalil & Suleyman Degirmen, 2024. "Viral decisions: unmasking the impact of COVID-19 info and behavioral quirks on investment choices," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-20, December.

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    More about this item

    Keywords

    COVID-19 news; Sentiment Analysis; Stock Markets;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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