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

Ioannis Chalkiadakis (), Gareth Peters (), Guillaume Bagnarosa and Alexandre Gohin ()
Additional contact information
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 Peters: UC Santa Barbara - University of California [Santa Barbara] - UC - University of California
Guillaume Bagnarosa: ESC [Rennes] - ESC 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

Post-Print from HAL

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

Keywords: Text-as-data; Time-series; Agricultural commodities; Natural language processing; Multiple-Output Gaussian process; Statistical causality (search for similar items in EconPapers)
Date: 2025
Note: View the original document on HAL open archive server: https://hal.science/hal-05280276v1
References: Add references at CitEc
Citations:

Published in Quantitative Finance, 2025, 25 (8), pp.1233 - 1259. ⟨10.1080/14697688.2025.2528689⟩

Downloads: (external link)
https://hal.science/hal-05280276v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05280276

DOI: 10.1080/14697688.2025.2528689

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().