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This paper investigates whether multi-dimensional sentiment analysis using LLMs can improve the prediction of weekly WTI crude oil futures returns. They extract five sentiment dimensions (relevance, polarity, intensity, uncertainty, and forwardness) from energy-sector news articles using GPT-4o, Llama 3.2-3b, FinBERT, and AlphaVantage. The study finds that combining GPT-4o and FinBERT yields the best predictive performance, with intensity and uncertainty being key predictive features.
LLMs uncover hidden signals in news sentiment, showing that intensity and uncertainty are more predictive of crude oil futures returns than simple positive/negative polarity.
Forecasting crude oil prices remains challenging because market-relevant information is embedded in large volumes of unstructured news and is not fully captured by traditional polarity-based sentiment measures. This paper examines whether multi-dimensional sentiment signals extracted by large language models improve the prediction of weekly WTI crude oil futures returns. Using energy-sector news articles from 2020 to 2025, we construct five sentiment dimensions covering relevance, polarity, intensity, uncertainty, and forwardness based on GPT-4o, Llama 3.2-3b, and two benchmark models, FinBERT and AlphaVantage. We aggregate article-level signals to the weekly level and evaluate their predictive performance in a classification framework. The best results are achieved by combining GPT-4o and FinBERT, suggesting that LLM-based and conventional financial sentiment models provide complementary predictive information. SHAP analysis further shows that intensity- and uncertainty-related features are among the most important predictors, indicating that the predictive value of news sentiment extends beyond simple polarity. Overall, the results suggest that multi-dimensional LLM-based sentiment measures can improve commodity return forecasting and support energy-market risk monitoring.
