Xinquan Zhou, Guillaume Bagnarosa, Michael Dowling, Jagadish Dandu
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引用次数: 0
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.
期刊介绍:
The European Review of Agricultural Economics serves as a forum for innovative theoretical and applied agricultural economics research.
The ERAE strives for balanced coverage of economic issues within the broad subject matter of agricultural and food production, consumption and trade, rural development, and resource use and conservation. Topics of specific interest include multiple roles of agriculture; trade and development; industrial organisation of the food sector; institutional dynamics; consumer behaviour; sustainable resource use; bioenergy; agricultural, agri-environmental and rural policy; specific European issues.
Methodological articles are welcome. All published papers are at least double peer reviewed and must show originality and innovation. The ERAE also publishes book reviews.