Enhancing Agricultural Futures Return Prediction: Insights from Rolling VMD, Economic Factors, and Mixed Ensembles

Yiling Ye, Xiaowen Zhuang, Cai Yi, Dinggao Liu and Zhenpeng Tang ()
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Yiling Ye: College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Xiaowen Zhuang: College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Cai Yi: College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Dinggao Liu: College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Zhenpeng Tang: College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou 350002, China

Agriculture, 2025, vol. 15, issue 11, 1-35

Abstract: The prediction of agricultural commodity futures returns is crucial for understanding global economic trends, alleviating inflationary pressures, and optimizing investment portfolios. However, current research that uses full-sample decomposition to predict agricultural futures returns suffers from data leakage, and the resulting forecast bias leads to overly optimistic outcomes. Additionally, previous studies have lacked a comprehensive consideration of key economic variables that influence agricultural prices. To address these issues, this study proposes the “Rolling VMD-LASSO-Mixed Ensemble” forecasting framework and compares its performance with “Rolling VMD” against univariate models, “Rolling VMD-LASSO” against “Rolling VMD”, and “Rolling VMD-LASSO-Mixed Ensemble” against “Rolling VMD-LASSO”. Empirical results show that, on average, “Rolling VMD” improved MSE, MAE, Theil U, ARV, and DA by 3.05%, 1.09%, 1.52%, 2.96%, and 11.11%, respectively, compared to univariate models. “Rolling VMD-LASSO” improved these five indicators by 2.11%, 1.15%, 1.09%, 2.13%, and 1.00% over “Rolling VMD”. The decision tree-based “Rolling VMD-LASSO-Mixed Ensemble” outperformed “Rolling VMD-LASSO” by 1.98%, 0.96%, 1.28%, 2.55%, and 4.18% in the five metrics. Furthermore, the daily average return, maximum drawdown, Sharpe ratio, Sortino ratio, and Calmar ratio based on prediction results also show that “Rolling VMD” outperforms univariate forecasting, “Rolling VMD-LASSO” outperforms “Rolling VMD”, and “Rolling VMD-LASSO-Mixed Ensemble” outperforms “Rolling VMD-LASSO”. This study provides a more accurate and robust forecasting framework for the global agricultural futures market, offering significant practical value for investor risk management and policymakers in stabilizing prices.

Keywords: agricultural futures return prediction; rolling VMD algorithm; dynamic factors screen; mixed ensemble; investment performance (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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