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Impact of key risk, green and behavioral factors on agricultural commodity price forecasting amid volatile markets: a deep learning approach

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This study demonstrates the impact of risk and green factors on agricultural prices, indicating that deep learning can enhance forecasting accuracy.

Deep learning models significantly outperform traditional forecasting methods in agricultural price prediction, enhancing accuracy.
Using SHAP analysis, key predictive factors like the S&P 500 and Bloomberg Agriculture Index highlight their relevance in forecasting.
The study utilizes GRUs and RNNs optimized with a genetic algorithm, providing sophisticated tools for predicting agricultural prices.
Findings emphasize the importance of addressing volatility in agricultural markets, guiding financial policy restructuring.

Drira et al. (2025) studied this question.

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This study demonstrates the impact of risk and green factors on agricultural prices, indicating that deep learning can enhance forecasting accuracy.