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

Author

Listed:
  • Banerjee, Ameet Kumar
  • Sensoy, Ahmet
  • Goodell, John W.
  • Mahapatra, Biplab

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.

Suggested Citation

  • Banerjee, Ameet Kumar & Sensoy, Ahmet & Goodell, John W. & Mahapatra, Biplab, 2024. "Impact of media hype and fake news on commodity futures prices: A deep learning approach over the COVID-19 period," Finance Research Letters, Elsevier, vol. 59(C).
    • Handle: RePEc:eee:finlet:v:59:y:2024:i:c:s1544612323010309
    DOI: 10.1016/j.frl.2023.104658
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    Keywords

    Commodity futures; Media hype; Fake news; Ravenpack database; COVID-19;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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