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Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic
Accurate oil market forecasting plays an important role in the theory and application of oil supply chain management for profit maximization and risk minimization. However, the coronavirus disease 2019 (COVID-19) has compelled governments worldwide to impose restrictions, consequently forcing the cl...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486164/ https://www.ncbi.nlm.nih.gov/pubmed/34629690 http://dx.doi.org/10.1016/j.energy.2021.120403 |
_version_ | 1784577688067899392 |
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author | Wu, Binrong Wang, Lin Wang, Sirui Zeng, Yu-Rong |
author_facet | Wu, Binrong Wang, Lin Wang, Sirui Zeng, Yu-Rong |
author_sort | Wu, Binrong |
collection | PubMed |
description | Accurate oil market forecasting plays an important role in the theory and application of oil supply chain management for profit maximization and risk minimization. However, the coronavirus disease 2019 (COVID-19) has compelled governments worldwide to impose restrictions, consequently forcing the closure of most social and economic activities. The latter leads to the volatility of the oil markets and poses a huge challenge to oil market forecasting. Fortunately, the social media information can finely reflect oil market factors and exogenous factors, such as conflicts and political instability. Accordingly, this study collected vast online oil news and used convolutional neural network to extract relevant information automatically. Oil markets are divided into four categories: oil price, oil production, oil consumption, and oil inventory. A total of 16,794; 9,139; 8,314; and 8,548 news headlines were collected in four respective cases. Experimental results indicate that social media information contributes to the forecasting of oil price, oil production and oil consumption. The mean absolute percentage errors are respectively 0.0717, 0.0144 and 0.0168 for the oil price, production, and consumption prediction during the COVID-19 pandemic. Marketers must consider the impact of social media information on the oil or similar markets, especially during the COVID-19 outbreak. |
format | Online Article Text |
id | pubmed-8486164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84861642021-10-04 Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic Wu, Binrong Wang, Lin Wang, Sirui Zeng, Yu-Rong Energy (Oxf) Article Accurate oil market forecasting plays an important role in the theory and application of oil supply chain management for profit maximization and risk minimization. However, the coronavirus disease 2019 (COVID-19) has compelled governments worldwide to impose restrictions, consequently forcing the closure of most social and economic activities. The latter leads to the volatility of the oil markets and poses a huge challenge to oil market forecasting. Fortunately, the social media information can finely reflect oil market factors and exogenous factors, such as conflicts and political instability. Accordingly, this study collected vast online oil news and used convolutional neural network to extract relevant information automatically. Oil markets are divided into four categories: oil price, oil production, oil consumption, and oil inventory. A total of 16,794; 9,139; 8,314; and 8,548 news headlines were collected in four respective cases. Experimental results indicate that social media information contributes to the forecasting of oil price, oil production and oil consumption. The mean absolute percentage errors are respectively 0.0717, 0.0144 and 0.0168 for the oil price, production, and consumption prediction during the COVID-19 pandemic. Marketers must consider the impact of social media information on the oil or similar markets, especially during the COVID-19 outbreak. Elsevier Ltd. 2021-07-01 2021-03-18 /pmc/articles/PMC8486164/ /pubmed/34629690 http://dx.doi.org/10.1016/j.energy.2021.120403 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Wu, Binrong Wang, Lin Wang, Sirui Zeng, Yu-Rong Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic |
title | Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic |
title_full | Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic |
title_fullStr | Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic |
title_full_unstemmed | Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic |
title_short | Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic |
title_sort | forecasting the u.s. oil markets based on social media information during the covid-19 pandemic |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486164/ https://www.ncbi.nlm.nih.gov/pubmed/34629690 http://dx.doi.org/10.1016/j.energy.2021.120403 |
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