Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/25447
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dc.contributor.authorElamer, A-
dc.contributor.authorElbialy, BA-
dc.contributor.authorAlsaab, KA-
dc.contributor.authorKhashan, MA-
dc.date.accessioned2022-11-04T14:45:46Z-
dc.date.available2022-11-04-
dc.date.available2022-11-04T14:45:46Z-
dc.date.issued2022-11-04-
dc.identifier.citationElamer AA, et al. (2022) 'The Impact of COVID-19 on the Relationship between Non-Renewable Energy and Saudi Stock Market Sectors Using Wavelet Coherence Approach and Neural Networks' in Sustainability, Vol. 14(21), pp.1-24. https://doi.org/10.3390/su142114496.en_US
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/25447-
dc.description.abstractIn this study, we examine the impact of COVID-19 on the relationship between non-renewable energy and Saudi stock market sectors for the period 11 January 2017–22 January 2022. We apply wavelet coherence and Radial Basis Function Neural Network (RBFNN) models. Our results provide evidence that COVID-19 led to an increase in the strength of the relationship between oil as a main non-renewable energy source and Saudi stock market sectors and affected the nature and direction of this relationship. The relationships between oil and commercial and professional services, materials, banks, energy, and transportation sectors are the most affected. Our results will help hedge funds, mutual funds, and individual investors, forecast the direction of Saudi stock market sectors and the use of oil for hedging or diversification during periods of uncertainty and crisis. It will also help decision and policymakers in Saudi Arabia to make the necessary decisions and actions to maintain the stability of the stock market sectors during these periods.en_US
dc.description.sponsorshipDeanship of Scientific Research at the Imam Abdulrahman bin Faisal University, Saudi Arabia, grant number 2019-155-ASCS.en_US
dc.publisherMDPIen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0-
dc.subjectWavelet coherenceen_US
dc.subjectneural networken_US
dc.subjectSaudi stock marketen_US
dc.subjectnon-renewable energyen_US
dc.subjectoilen_US
dc.titleThe Impact of COVID-19 on the Relationship between Non-Renewable Energy and Saudi Stock Market Sectors Using Wavelet Coherence Approach and Neural Networksen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.3390/su142114496-
dc.relation.isPartOfSustainability-
pubs.publication-statusPublished-
Appears in Collections:Brunel Business School Research Papers

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