用微调大模型分析新闻情绪，提升铝价预测精度。
Not All News Is Equal: Topic- and Event-Conditional Sentiment from Finetuned LLMs for Aluminum Price Forecasting
- 用微调的Qwen3模型提取中英文新闻情绪信号。
- 高波动期情绪数据使模型夏普比率提升至1.04。
- 适合关注金属市场情绪与量化交易的研究者。
通过捕捉市场情绪和舆论氛围，文本数据在大宗商品价格预测中愈发重要，尤其在金属市场。然而，轻量级微调大语言模型（LLM）在提取铝价预测信号方面的有效性，以及这些信号在何种市场条件下最具信息量，仍缺乏深入研究。本研究从英文和中文新闻标题（路透社、道琼斯新闻线、中国新闻网）生成月度情绪评分，并与传统表格数据（基本金属指数、汇率、通胀率、能源价格）结合。通过2007年至2024年上海金属交易所的多空模拟评估模型预测性能与经济价值。结果表明，在高波动时期，融合微调Qwen3情绪数据的LSTM模型（夏普比率1.04）显著优于仅使用表格数据的基准模型（夏普比率0.23）。后续分析揭示了新闻来源、话题和事件类型在铝价预测中的细微作用。
原文摘要 · Abstract (English)
By capturing the prevailing sentiment and market mood, textual data has become increasingly vital for forecasting commodity prices, particularly in metal markets. However, the effectiveness of lightweight, finetuned large language models (LLMs) in extracting predictive signals for aluminum prices, and the specific market conditions under which these signals are most informative, remains under-explored. This study generates monthly sentiment scores from English and Chinese news headlines (Reuters, Dow Jones Newswires, and China News Service) and integrates them with traditional tabular data, including base metal indices, exchange rates, inflation rates, and energy prices. We evaluate the predictive performance and economic utility of these models through long-short simulations on the Shanghai Metal Exchange from 2007 to 2024. Our results demonstrate that during periods of high volatility, Long Short-Term Memory (LSTM) models incorporating sentiment data from a finetuned Qwen3 model (Sharpe ratio 1.04) significantly outperform baseline models using tabular data alone (Sharpe ratio 0.23). Subsequent analysis elucidates the nuanced roles of news sources, topics, and event types in aluminum price forecasting.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。