Media influences on corn futures pricing

Xinquan Zhou (), Guillaume Bagnarosa (), Michael Dowling and Jagadish Dandu
Additional contact information
Xinquan Zhou: THU - Tsinghua University [Beijing]
Guillaume Bagnarosa: SMART - Structures et Marché Agricoles, Ressources et Territoires - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement
Michael Dowling: DCU - Dublin City University [Dublin]
Jagadish Dandu: Zayed University

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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.

Keywords: Corn markets; Topic modelling; Machine learning; Media analysis; Commodity markets (search for similar items in EconPapers)
Date: 2024-04-06
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Published in European Review of Agricultural Economics, 2024, 51 (2), pp.399-435. ⟨10.1093/erae/jbae002⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04643077

DOI: 10.1093/erae/jbae002

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