Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/25372
Title: The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning
Authors: Ibrahim, BA
Elamer, AA
Abdou, HA
Keywords: cryptocurrencies;COVID-19;Bitcoin;machine learning;crude oil;neural networks
Issue Date: 28-Oct-2022
Publisher: Springer Nature
Citation: Ibrahim, 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.
Abstract: Copyright © 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.
Description: Data Availability Statement: Data available on request from the authors.
The original online version of this article was revised as typesetter overlooked author corrections during proofing. Original article has been corrected.
URI: https://bura.brunel.ac.uk/handle/2438/25372
DOI: https://doi.org/10.1007/s10479-022-05024-4
ISSN: 0254-5330
Other Identifiers: ORCiD ID: Ahmed A. Elamer https://orcid.org/0000-0002-9241-9081.
Appears in Collections:Brunel Business School Research Papers

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