Structural changes and statistical causal relationships in agricultural commodities markets: the impact of public news sentiment and institutional announcements

Ioannis Chalkiadakis, Gareth W. Peters, Guillaume Bagnarosa and Alexandre Gohin

Quantitative Finance, 2025, vol. 25, issue 8, 1233-1259

Abstract: Novel empirical evidence is studied for the way the agricultural commodities futures markets process information. The significant effect of institutional announcements, such as those of the United States Department of Agriculture (USDA), on the participants in such markets has been well documented in the literature. However, existing studies consider measures of market ‘surprise’ or analysts' ‘sentiment’ that do not stem directly from unstructured text in official reports or public news. In this work, we aim to verify the structural changes incurred in the corn and wheat markets by the release of the USDA reports while considering higher-order structural information of several market-related processes. Furthermore, we investigate whether there is evidence for statistical causality relationships between the market reaction, in terms of price, volume and volatility, and market participants' sentiment induced by public news. To address these goals we rely on a recently published efficient algorithm for statistical causality analysis in multivariate time-series based on Gaussian Processes [Zaremba, A.B. and Peters, G.W., Statistical causality for multivariate nonlinear time series via Gaussian process models. Methodol. Comput. Appl. Probab., 2022, 1–46. https://doi.org/10.1007/s11009-022-09928-3.]. Market and public news text signals are jointly modeled as a Gaussian Process, whose properties we leverage to study linear and non-linear causal effects between the different time-series signals. The participants' sentiment is extracted from public news data via methods developed in the area of statistical machine learning known as Natural Language Processing (NLP). A novel framework for text-to-time-series embedding is employed [Chalkiadakis, I., Zaremba, A., Peters, G.W. and Chantler, M.J., On-chain analytics for sentiment-driven statistical causality in cryptocurrencies. Blockchain: Res Appl., 2022, 3(2), 100063. Available online at: https://www.sciencedirect.com/science/article/pii/S2096720922000033.] to construct a sentiment index from publicly available news articles. The conducted studies offer a more comprehensive perspective of the information that is available to investors and how that is incorporated into the agricultural commodities market.

Date: 2025
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DOI: 10.1080/14697688.2025.2528689

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