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Media influences on corn futures pricing

Author

Listed:
  • Xinquan Zhou
  • Guillaume Bagnarosa
  • Michael Dowling
  • Jagadish Dandu

Abstract

Understanding agricultural commodity futures is crucial for efficient business operations. This study employs textual machine learning on 290,271 articles (2009–2020) focusing on corn markets, aiming to model the impact of news on corn futures pricing. Our novel approach enables the identification of seven distinct topics within corn news, offering a comprehensive view of the news coverage spectrum. Soybean biofuel news notably influences corn prices, while exports, weather and wheat news significantly impact pricing uncertainty. These insights deepen our understanding of factors shaping corn futures and highlight machine learning’s potential in agricultural economic analysis, enabling more accurate market predictions and policy decisions.

Suggested Citation

  • Xinquan Zhou & Guillaume Bagnarosa & Michael Dowling & Jagadish Dandu, 2024. "Media influences on corn futures pricing," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 51(2), pages 399-435.
    • Handle: RePEc:oup:erevae:v:51:y:2024:i:2:p:399-435.
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    File URL: http://hdl.handle.net/10.1093/erae/jbae002
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