SOYBEAN PRICE FORECASTING VIA HYBRID LSTM-LLM ARCHITECTURE: STATISTICAL AND ECONOMIC EVALUATION OF BRAZILIAN AGRIBUSINESS NEWS SENTIMENT
DOI:
https://doi.org/10.56238/revgeov16n13-049Keywords:
Agricultural Commodities, Recurrent Neural Networks, Sentiment Analysis, Bayesian Optimization, Model Confidence SetAbstract
Soybean is Brazil's leading agricultural commodity, and its price volatility poses significant challenges to producers, traders, and policymakers, given the market's nonlinear dependence on exogenous factors such as climate conditions, trade policies, and the informational flow of news. This study investigates to what extent incorporating textual sentiment extracted by agribusiness-specialized LLMs improves the predictive accuracy and economic value of LSTM models for soybean futures price forecasting (SJCc1). To this end, six architectures were empirically compared — a naïve benchmark, pure LSTM, LSTM with frozen LLM in scalar and probabilistic outputs, and end-to-end versions of both — using 3,261 price records and a corpus of 27,024 Brazilian agribusiness news articles, with fine-tuning on 1,000 labeled news items and Bayesian hyperparameter optimization via TPE. Statistical comparison employed the Model Confidence Set (MCS) procedure at 90% confidence, complemented by a paired block bootstrap test for cumulative returns. It is observed that only the LSTM+LLM architecture with probabilistic output joined the MCS alongside the naïve benchmark — being the only model to generate statistically significant cumulative excess return over buy-and-hold (58.27%; p ≈ 0.003; Sharpe ratio: 1.74) —, with its advantage amplifying during high-volatility periods. It is concluded that the predictive gain stems from the specific combination of a specialized LLM and probabilistic sentiment encoding, rather than from textual integration per se.
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