Impact of media hype and fake news on commodity futures prices: A deep learning approach over the COVID-19 period

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buir.contributor.authorŞensoy, Ahmet
buir.contributor.orcidŞensoy, Ahmet|0000-0001-7967-5171
dc.citation.epage8en_US
dc.citation.spage1
dc.citation.volumeNumber59
dc.contributor.authorBanerjee, Ameet Kumar
dc.contributor.authorŞensoy, Ahmet
dc.contributor.authorGoodell, John W.
dc.contributor.authorMahapatra, Biplab
dc.date.accessioned2024-03-15T06:07:41Z
dc.date.available2024-03-15T06:07:41Z
dc.date.issued2023-10-30
dc.departmentDepartment of Management
dc.description.abstractWe investigate the reactions of eight commodity futures to media hype and fake news during COVID-19, utilising the Ravenpack news database, along with deep learning algorithms. Results identify a significant impact on commodity prices of media hype and fake news, with this reaction amplified during COVID-19. Compared to alternative deep learning algorithms, bi-directional long-short-term memory is adaptive to forecasting the returns of the commodity futures contracts with lower mean absolute error and root mean square error. Findings, confirmed by Diebold-Mariano testing, as well as alternative data partitioning, show commodity markets are susceptible to fake news and media hype.
dc.embargo.release2025-10-30
dc.identifier.doi10.1016/j.frl.2023.104658
dc.identifier.eissn1544-6131
dc.identifier.issn1544-6123
dc.identifier.urihttps://hdl.handle.net/11693/114768
dc.language.isoen
dc.relation.isversionofhttps://doi.org/10.1016/j.frl.2023.104658
dc.rightsCC BY-NC-ND 4.0 DEED (Attribution-NonCommercial-NoDerivs 4.0 International)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCommodity futures
dc.subjectMedia hype
dc.subjectFake news
dc.subjectRavenpack database
dc.subjectCOVID-19
dc.titleImpact of media hype and fake news on commodity futures prices: A deep learning approach over the COVID-19 period
dc.typeArticle

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