用多维情感信号提升原油期货预测准确率
Beyond Polarity: Multi-Dimensional LLM Sentiment Signals for WTI Crude Oil Futures Return Prediction
- 从新闻中提取相关性、极性、强度等5维情感特征
- 结合GPT-4o与FinBERT模型，预测精度显著提升
- 强度与不确定性特征比单纯极性更关键，适合量化交易研究
原油价格预测仍具挑战性，因市场相关信息嵌入大量非结构化新闻中，传统极性类情感度量难以充分捕捉。本文探讨基于大语言模型的多维度情感信号能否提升周度WTI原油期货收益率预测能力。利用2020至2025年能源领域新闻文章，构建涵盖相关性、极性、强度、不确定性与前瞻性共五维的情感指标，基于GPT-4o、Llama 3.2-3b、FinBERT及AlphaVantage四类模型实现。将文章级信号聚合至周粒度，在分类框架下评估其预测性能。最优结果由GPT-4o与FinBERT联合实现，表明大模型与传统金融情感模型提供互补信息。SHAP分析显示，强度与不确定性相关特征为最重要预测因子，说明新闻情感的预测价值超越简单极性。总体表明，多维大模型情感度量可提升商品收益预测能力，支持能源市场风险监控。
原文摘要 · Abstract (English)
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.
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