Impact of Public Interest on Agricultural CommodityPrices

As financial markets grow more complex and sophisticated, the use of diverse data sources has become essential for accurate market analysis and prediction. This study leverages Google Trends data, representing public search interest, and combines it with a long short-term memory (LSTM) model to explore its impact on futures prices for major agricultural commodities. By applying the model to commodities such as corn, wheat, cocoa, and coffee, we analyze how fluctuations in public interest influence price volatility. The results confirm that periods of heightened search frequency for specific words often correspond to changes in futures price behavior, indicating that public sentiment plays a role in market dynamics. This study demonstrates that integrating social data, like Google Trends, into financial forecasting frameworks can enhance predictive accuracy and market insights, analyzing that the relationship between investors’ sentiment and commodity prices in the financial market quantitatively.