Impact of media hype and fake news on commodity futures prices: A deep learning approach over the COVID-19 period
buir.contributor.author | Şensoy, Ahmet | |
buir.contributor.orcid | Şensoy, Ahmet|0000-0001-7967-5171 | |
dc.citation.epage | 8 | en_US |
dc.citation.spage | 1 | |
dc.citation.volumeNumber | 59 | |
dc.contributor.author | Banerjee, Ameet Kumar | |
dc.contributor.author | Şensoy, Ahmet | |
dc.contributor.author | Goodell, John W. | |
dc.contributor.author | Mahapatra, Biplab | |
dc.date.accessioned | 2024-03-15T06:07:41Z | |
dc.date.available | 2024-03-15T06:07:41Z | |
dc.date.issued | 2023-10-30 | |
dc.department | Department of Management | |
dc.description.abstract | We 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.description.provenance | Made available in DSpace on 2024-03-15T06:07:41Z (GMT). No. of bitstreams: 1 Impact_of_media_hype_and_fake_news_on_commodity_futures_prices_A_deep_learning_approach_over_the_COVID-19_period.pdf: 1847829 bytes, checksum: 19ceb188c0b8356a4130a3ae1153c549 (MD5) Previous issue date: 2023-10-30 | en |
dc.embargo.release | 2025-10-30 | |
dc.identifier.doi | 10.1016/j.frl.2023.104658 | |
dc.identifier.eissn | 1544-6131 | |
dc.identifier.issn | 1544-6123 | |
dc.identifier.uri | https://hdl.handle.net/11693/114768 | |
dc.language.iso | en | |
dc.relation.isversionof | https://doi.org/10.1016/j.frl.2023.104658 | |
dc.rights | CC BY-NC-ND 4.0 DEED (Attribution-NonCommercial-NoDerivs 4.0 International) | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Commodity futures | |
dc.subject | Media hype | |
dc.subject | Fake news | |
dc.subject | Ravenpack database | |
dc.subject | COVID-19 | |
dc.title | Impact of media hype and fake news on commodity futures prices: A deep learning approach over the COVID-19 period | |
dc.type | Article |
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