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

Michele Costola, Michael Nofer, Oliver Hinz and Loriana Pelizzon ()

No 288, SAFE Working Paper Series from Leibniz Institute for Financial Research SAFE

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.

Keywords: COVID-19 news; Sentiment Analysis; Stock Markets (search for similar items in EconPapers)
JEL-codes: G10 G14 G15 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cfn, nep-cmp, nep-fmk and nep-mst
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Machine learning sentiment analysis, COVID-19 news and stock market reactions (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:safewp:288

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