Media influences on corn futures pricing
Document Type
Article
Source of Publication
European Review of Agricultural Economics
Publication Date
3-16-2024
Abstract
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.
DOI Link
ISSN
Publisher
Oxford University Press (OUP)
First Page
jbae002
Last Page
jbae002
Disciplines
Business
Keywords
Commodity futures, Agricultural economics, Textual machine learning, Market predictions, Policy decisions
Recommended Citation
Zhou, Xinquan; Bagnarosa, Guillaume; Dowling, Michael; and Dandu, Jagadish, "Media influences on corn futures pricing" (2024). All Works. 6498.
https://zuscholars.zu.ac.ae/works/6498
Indexed in Scopus
no
Open Access
no