Evidence map›Paper›PMID 36320866›Full record

ArticleAnnals of operations research2022

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning.

Bassam A Ibrahim, Ahmed A Elamer, Hussein A Abdou

Erratum issuedAbstract read
In one paragraph

Article in Annals of operations research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Bassam A IbrahimDepartment of Management, Faculty of Commerce, Mansoura University, Mansoura, Egypt.
Ahmed A ElamerBrunel Business School, Brunel University London, Kingston Lane, Uxbridge, London, UB8 3PH UK.ORCID 0000-0002-9241-9081
Hussein A AbdouDepartment of Management, Faculty of Commerce, Mansoura University, Mansoura, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

BitcoinCOVID-19Crude oilCryptocurrenciesMachine learningNeural networks

Identifiers

PMID36320866
PMCPMC9613455

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.