Machine Learning Sentiment Analysis, Covid-19 News and Stock Market Reactions

Posted: 15 Sep 2020

See all articles by Michele Costola

Michele Costola

Ca' Foscari University of Venice

Michael Nofer

Goethe University Frankfurt

Oliver Hinz

Goethe University Frankfurt - Faculty of Economics and Business Administration

Loriana Pelizzon

Goethe University Frankfurt - Faculty of Economics and Business Administration; Leibniz Institute for Financial Research SAFE; Ca Foscari University of Venice

Date Written: September 11, 2020

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

JEL Classification: G10, G14, G15

Suggested Citation

Costola, Michele and Nofer, Michael and Hinz, Oliver and Pelizzon, Loriana, Machine Learning Sentiment Analysis, Covid-19 News and Stock Market Reactions (September 11, 2020). SAFE Working Paper No. 288, Available at SSRN: https://ssrn.com/abstract=3690922 or http://dx.doi.org/10.2139/ssrn.3690922

Michele Costola

Ca' Foscari University of Venice ( email )

Cannaregio 873
Venice, 30121
Italy

Michael Nofer

Goethe University Frankfurt ( email )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

Oliver Hinz

Goethe University Frankfurt - Faculty of Economics and Business Administration ( email )

Mertonstrasse 17-25
Frankfurt am Main, D-60325
Germany

Loriana Pelizzon (Contact Author)

Goethe University Frankfurt - Faculty of Economics and Business Administration ( email )

Theodor-W.-Adorno-Platz 3
Frankfurt am Main, D-60323
Germany

Leibniz Institute for Financial Research SAFE ( email )

Theodor-W.-Adorno-Platz 3
Frankfurt am Main, 60323
Germany

HOME PAGE: http://www.safe-frankfurt.de

Ca Foscari University of Venice ( email )

Dorsoduro 3246
Venice, Veneto 30123
Italy

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