Not All News Is Equal: Topic- and Event-Conditional Sentiment from Finetuned LLMs for Aluminum Price Forecasting

TL;DR

This paper explores how finetuned large language models can extract sentiment signals from news headlines to improve aluminum price forecasting, especially during volatile periods.

Contribution

It demonstrates the effectiveness of sentiment analysis from finetuned LLMs in enhancing commodity price predictions under specific market conditions.

Findings

Sentiment signals from finetuned LLMs improve forecast accuracy during high volatility.

Models incorporating sentiment data achieve higher Sharpe ratios than baseline models.

Analysis reveals the importance of news source, topic, and event type in price prediction.

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.