PREDICCIÓN DE PRECIOS DE LA SOJA MEDIANTE ARQUITECTURA HÍBRIDA LSTM-LLM: EVALUACIÓN ESTADÍSTICA Y ECONÓMICA DEL SENTIMIENTO DE NOTICIAS DEL AGRONEGOCIO BRASILEÑO
DOI:
https://doi.org/10.56238/revgeov16n13-049Palabras clave:
Commodities Agrícolas, Redes Neuronales Recurrentes, Análisis de Sentimiento, Optimización Bayesiana, Model Confidence SetResumen
La soja es la principal commodity agrícola brasileña y la volatilidad de sus precios impone desafíos significativos a productores, traders y formuladores de políticas públicas, dada la dependencia no lineal del mercado a factores exógenos como las condiciones climáticas, las políticas comerciales y el flujo informacional de noticias. Se investiga en qué medida la incorporación de sentimiento textual extraído por LLMs especializados en el agronegocio mejora la precisión predictiva y el valor económico de modelos LSTM para la predicción del contrato de futuros de soja (SJCc1). Para ello, seis arquitecturas fueron comparadas empíricamente — benchmark naïve, LSTM pura, LSTM con LLM congelada en salidas escalar y probabilística, y versiones end-to-end de ambas — utilizando 3.261 registros de precios y un corpus de 27.024 noticias brasileñas del agronegocio, con fine-tuning sobre 1.000 noticias etiquetadas y optimización bayesiana de hiperparámetros mediante TPE. La comparación estadística empleó el procedimiento Model Confidence Set (MCS) con 90% de confianza, complementada por una prueba bootstrap emparejada en bloques para el retorno acumulado. Se observa que únicamente la arquitectura LSTM+LLM con salida probabilística integró el MCS junto al benchmark naïve — siendo el único modelo en generar retorno acumulado estadísticamente significativo sobre el buy-and-hold (58,27%; p ≈ 0,003; Sharpe ratio: 1,74) —, con ventaja ampliada en períodos de alta volatilidad. Se concluye que la ganancia predictiva proviene de la combinación específica entre un LLM especializado y la codificación probabilística del sentimiento, y no de la integración textual per se.
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