ArticleResearch in international business and finance2023
Machine learning sentiment analysis, COVID-19 news and stock market reactions.
Article in Research in international business and finance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Media's role in agenda-setting: Exploring media coverage of the German Baltic fishery discourse.Ambio · 2026Article
- A novel framework using particle swarm optimization and long short-term memory networks for stock market forcasting.Scientific reports · 2025Article
- Article
- Enhanced stock market forecasting using dandelion optimization-driven 3D-CNN-GRU classification.Scientific reports · 2024Article
- COVID-19's influence on Karachi stock exchange: A comparative machine learning algorithms study for forecasting.Heliyon · 2024Article
- Machine learning and game theory for cyber governance: Enhancing public opinion and regional sustainable development.PloS one · 2024Article
- Emerging Technologies for 6G Communication Networks: Machine Learning Approaches.Sensors (Basel, Switzerland) · 2023Review
- Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic.PeerJ. Computer science · 2023Article
- Network based evidence of the financial impact of Covid-19 pandemic.International review of financial analysis · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.