Full length article
Machine learning sentiment analysis, COVID-19 news and stock market reactions

https://doi.org/10.1016/j.ribaf.2023.101881Get rights and content
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Highlights

  • The recent COVID-19 pandemic represents an unprecedented worldwide event.

  • We study the influence of COVID-19-related news on the financial markets.

  • We analyze 203,886 online articles published on MarketWatch, NYTimes, and Reuters.

  • We extract the news sentiment through a financial market-adapted BERT model.

  • The flow of news on COVID-19 has had an impact on forming market expectations.

Abstract

The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.

JEL classification

G10
G14
G15

Keywords

COVID-19 news
Sentiment analysis
Stock markets

Data availability

The authors do not have permission to share data.

Cited by (0)

The authors thank two anonymous Reviewers and the Guest Editor, Sabri Boubaker, for their insightful comments and suggestions. This research has received funding from the Volkswagen Foundation, Germany as part of the initiative “Artificial Intelligence and its Impact on Tomorrow’s World”. Loriana Pelizzon gratefully acknowledges research and financial support from the Leibniz Institute for Financial Research SAFE, Frankfurt . The usual disclaimer applies.