Abstract
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.
Abstract (translated)
通过捕捉当前的情绪和市场氛围，文本数据在预测商品价格方面变得越来越重要，特别是在金属市场上。然而，轻量级微调的大规模语言模型（LLMs）提取铝价的预测信号的有效性以及这些信号在特定市场条件下的信息价值仍然没有得到充分探索。这项研究生成了来自英文和中文新闻标题（路透社、道琼斯通讯社和中国新闻社）的月度情绪评分，并将它们与传统的表格数据集（包括基础金属指数、汇率、通胀率和能源价格）相结合。我们通过2007年至2024年在上海金属交易所进行的长短交易模拟来评估这些模型的预测性能和经济效用。我们的结果表明，在高波动性时期，结合来自微调后的Qwen3模型的情绪数据的长短时记忆（LSTM）模型表现显著优于仅使用表格数据作为基础的基准模型（夏普比率分别为1.04和0.23）。后续分析进一步阐明了新闻来源、主题和事件类型在铝价预测中的复杂作用。
URL
https://arxiv.org/abs/2603.09085
