Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/25372
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dc.contributor.authorIbrahim, BA-
dc.contributor.authorElamer, AA-
dc.contributor.authorAbdou, HA-
dc.date.accessioned2022-10-26T09:28:30Z-
dc.date.available2022-10-26T09:28:30Z-
dc.date.issued2022-10-28-
dc.identifierORCiD ID: Ahmed A. Elamer https://orcid.org/0000-0002-9241-9081.-
dc.identifier.citationIbrahim, B.A., Elamer, A.A. and Abdou, H.A. (2022) 'The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning', Annals of Operations Research, 0 (ahead-of-print), pp. 1 - 44. doi: 10.1007/s10479-022-05024-4.en_US
dc.identifier.issn0254-5330-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/25372-
dc.descriptionData Availability Statement: Data available on request from the authors.-
dc.descriptionThe original online version of this article was revised as typesetter overlooked author corrections during proofing. Original article has been corrected.-
dc.description.abstractCopyright © The Author(s) 2022. This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.-
dc.format.extent1 - 44-
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.rightsCopyright © The Author(s) 2022. Rights and permissions: Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/.-
dc.subjectcryptocurrenciesen_US
dc.subjectCOVID-19en_US
dc.subjectBitcoinen_US
dc.subjectmachine learningen_US
dc.subjectcrude oilen_US
dc.subjectneural networksen_US
dc.titleThe role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learningen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1007/s10479-022-05024-4-
dc.relation.isPartOfAnnals of Operations Research-
pubs.publication-statusPublished-
pubs.volume0-
dc.identifier.eissn1572-9338-
dc.rights.holderThe Author(s)-
Appears in Collections:Brunel Business School Research Papers

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