Banerjee, Ameet KumarŞensoy, AhmetGoodell, John W.Mahapatra, Biplab2024-03-152024-03-152023-10-301544-6123https://hdl.handle.net/11693/114768We 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.enCC BY-NC-ND 4.0 DEED (Attribution-NonCommercial-NoDerivs 4.0 International)Commodity futuresMedia hypeFake newsRavenpack databaseCOVID-19Article10.1016/j.frl.2023.1046581544-6131