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

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  • Ioannis Chalkiadakis

    (ISC-PIF - Institut des Systèmes Complexes - Paris Ile-de-France - ENS Cachan - École normale supérieure - Cachan - UP1 - Université Paris 1 Panthéon-Sorbonne - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - Institut Curie [Paris] - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)

  • Gareth W Peters

    (UC Santa Barbara - University of California [Santa Barbara] - UC - University of California)

  • Guillaume Bagnarosa

    (Rennes SB - Rennes School of Business)

  • Alexandre Gohin

    (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)

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

Suggested Citation

  • Ioannis Chalkiadakis & Gareth W Peters & Guillaume Bagnarosa & Alexandre Gohin, 2025. "Structural changes and statistical causal relationships in agricultural commodities markets: the impact of public news sentiment and institutional announcements," Post-Print hal-05280276, HAL.
    • Handle: RePEc:hal:journl:hal-05280276
    DOI: 10.1080/14697688.2025.2528689
    Note: View the original document on HAL open archive server: https://hal.science/hal-05280276v1
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    References listed on IDEAS

    as
    1. Kevin P. McNew & Juan Andres Espinosa, 1994. "The informational content of USDA crop reports: Impacts on uncertainty and expectations in grain futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 14(4), pages 475-492, June.
    2. Karali, Berna, 2012. "Do USDA Announcements Affect Comovements Across Commodity Futures Returns?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(01), pages 1-21, April.
    3. Ioannis Chalkiadakis & Hongxuan Yan & Gareth W Peters & Pavel V Shevchenko, 2021. "Infection rate models for COVID-19: Model risk and public health news sentiment exposure adjustments," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-39, June.
    4. Marilyne Huchet-Bourdon, 2011. "Agricultural Commodity Price Volatility: An Overview," OECD Food, Agriculture and Fisheries Papers 52, OECD Publishing.
    5. repec:ags:jrapmc:122315 is not listed on IDEAS
    6. Hamadi, Hassan & Bassil, Charbel & Nehme, Tamara, 2017. "News surprises and volatility spillover among agricultural commodities: The case of corn, wheat, soybean and soybean oil," Research in International Business and Finance, Elsevier, vol. 41(C), pages 148-157.
    7. Baur, Robert F & Orazem, Peter F, 1994. "The Rationality and Price Effects of U.S. Department of Agriculture Forecasts of Oranges," Journal of Finance, American Finance Association, vol. 49(2), pages 681-695, June.
    8. Michael K Adjemian & Scott H Irwin, 2018. "USDA Announcement Effects in Real-Time," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(4), pages 1151-1171.
    9. Phil L. Colling & Scott H. Irwin, 1990. "The Reaction of Live Hog Futures Prices to USDA Hogs and Pigs Reports," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 72(1), pages 84-94.
    10. Anna Zaremba & Tomaso Aste, 2014. "Measures of Causality in Complex Datasets with application to financial data," Papers 1401.1457, arXiv.org, revised Jun 2014.
    Full references (including those not matched with items on IDEAS)

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