AI-Driven News-Enhanced Machine Learning for Short-Term Corn Futures Price Forecasting

Asterios Theofilou, Stefanos A. Nastis, Konstadinos Mattas, Konstantinos Theofilou

Accurately forecasting agricultural commodity prices is a complex and persistent problem for producers, traders, and policymakers. In this study we examine how artificial intelligence can be combined with large-scale global news data to refine daily corn price forecasts. A Long Short-Term Memory (LSTM) neural network was trained on Chicago corn futures between 2021 and 2024 to capture price dynamics, while agriculture-related news features were derived from the Global Database of Events, Language, and Tone (GDELT). Rather than sentiment polarity, the analysis shows that attention-based indicators, such as article volume, rolling intensity measures, and persistence of elevated coverage, carry stronger predictive information. These features are incorporated through a Ridge regression residual correction applied to the LSTM predictions, forming a lightweight two-stage hybrid model. While absolute forecast accuracy remains comparable to the price-only baseline (RMSE ≈ 9 ¢/bu; MAE ≈ 5.8 ¢/bu; R2 ≈ 0.99), the hybrid framework improves directional accuracy by approximately 2.4 percentage points, with gains concentrated during periods of moderate news intensity. Feature attribution results indicate that media attention intensity and persistence dominate sentiment-tone variables, which receive zero weight under regularization. Overall, the proposed framework offers a transparent, computationally efficient, and reproducible approach for integrating open global news data into short-term agricultural price forecasting.