TY  - UNPD
A1  - Costola, Michele
A1  - Nofer, Michael
A1  - Hinz, Oliver
A1  - Pelizzon, Loriana
T1  - Machine learning sentiment analysis, COVID-19 news and stock market reactions
T2  - SAFE working paper series ; No. 288
N2  - 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.
T3  - SAFE working paper - 288 
KW  - COVID-19 news
KW  - Sentiment Analysis
KW  - Stock Markets
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/55247
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-552475
UR  - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690922
PB  - SAFE
CY  - Frankfurt am Main
ER  -