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The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning.
Ibrahim, Bassam A; Elamer, Ahmed A; Abdou, Hussein A.
  • Ibrahim BA; Department of Management, Faculty of Commerce, Mansoura University, Mansoura, Egypt.
  • Elamer AA; Brunel Business School, Brunel University London, Kingston Lane, Uxbridge, London, UB8 3PH UK.
  • Abdou HA; Departmentof Accounting, Faculty of Commerce, Mansoura University, Mansoura, Egypt.
Ann Oper Res ; : 1-44, 2022 Oct 28.
Article in English | MEDLINE | ID: covidwho-2148820
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Ann Oper Res Year: 2022 Document Type: Article Affiliation country: S10479-022-05024-4

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Ann Oper Res Year: 2022 Document Type: Article Affiliation country: S10479-022-05024-4