Show simple item record

AuthorAl-Maadid, Alanoud
AuthorAlhazbi, Saleh
AuthorAl-Thelaya, Khaled
Available date2023-01-17T06:57:08Z
Publication Date2022
Publication NameResearch in International Business and Finance
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.ribaf.2022.101667
URIhttp://hdl.handle.net/10576/38497
AbstractCOVID-19 has resulted in high volatility in financial markets across the world. The goal of this study is to investigate the impact of COVID-19-related news on the stock markets in Gulf Cooperation Council (GCC) countries. The study utilizes machine learning approaches to assess the role of COVID-19 news in stock return predictability in these markets. The results reveal that the stock markets in the United Arab Emirates (UAE), Qatar, Saudi Arabia, and Oman were impacted by coronavirus-related news; however, this news had no impact on the stocks in Bahrain. Moreover, the results indicate that the impacted markets were influenced differently in terms of the quantities and types of news. 2022 The Authors
SponsorThe authors would like to thank Qatar University for financial support , Grant no. QUCP-CBE-2018-1 . The findings are solely the responsibility of the authors.
Languageen
PublisherElsevier
SubjectCOVID-19
GCC
Machine-learning
STOCK MARKETS
TitleUsing machine learning to analyze the impact of coronavirus pandemic news on the stock markets in GCC countries
TypeArticle
Volume Number61
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record