Beyond Polarity: Multi-Dimensional LLM Sentiment Signals for WTI Crude Oil Futures Return Prediction

TL;DR

This paper explores how multi-dimensional sentiment signals derived from large language models can enhance the prediction of WTI crude oil futures returns, moving beyond traditional polarity-based sentiment analysis.

Contribution

It introduces a multi-dimensional sentiment framework using LLMs like GPT-4o and Llama 3.2-3b, demonstrating improved predictive performance over conventional models.

Findings

Combining GPT-4o and FinBERT yields the best prediction results.

Intensity and uncertainty features are key predictors.

Multi-dimensional sentiment signals outperform polarity-only measures.

Abstract

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.