Machine Learning Corn Price Predictions

Abstract

For a substantial period of time, a large number of market players have given price projections for the main agricultural commodities a high degree of significance. In this research, we look at the daily published price of corn in order to address the issue. The sample under analysis runs across a period of more than 10 years, from January 2, 2014, to April 10, 2024. The price series under investigation has significant effects on the business sector. In particular, Gaussian process regression models are created for this situation by using Bayesian optimization techniques and cross-validation (CV) methods. As a consequence, the circumstance leads to the development of methodologies that are used for price forecasting. Our empirical forecasting technique produces relatively accurate price projections for the out-of-sample assessment period, which runs from April 6, 2022, to April 10, 2024. For the price of corn, the relative root mean square error was found to be 1.8537%. Because price forecasting models are readily available, investors and governments may make informed decisions about the corn market as they have access to the necessary information.

Received: 13 May 2026 | Revised: 12 August 2026 | Accepted: 28 August 2026

Conflicts of Interest

Xiaojie Xu is the Editorial Board Member for FinTech and Sustainable Innovation and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Bingzi Jin: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Xiaojie Xu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization.