用大模型解析财经推文事件，发现可交易的负面收益信号
Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading
- 用大模型自动给高情感强度推文打多标签事件类
- 部分事件标签7天内负超额收益，夏普比低至-0.38
- 代码开源透明，适合量化研究者复现与拓展
本研究展示大语言模型在金融语义标注和阿尔法信号发现中的独特价值。基于公司相关推文语料，我们利用大模型自动为高情感强度推文赋予多标签事件类别，并将其情感信号与1至7天后的未来收益进行对齐，评估其统计显著性与市场可交易性。实验表明，某些事件标签在统计上持续产生负阿尔法，夏普比率最低达-0.38，信息系数超过0.05，均在95%置信水平下显著。该研究证实了将非结构化社交媒体文本转化为结构化多标签事件变量的可行性。核心贡献在于对透明性与可复现性的承诺，所有代码与方法均公开。结果有力证明社交媒体情感是金融预测中一个有价值但噪声较大的信号，并凸显开源框架在推动算法交易研究民主化方面的潜力。
原文摘要 · Abstract (English)
In this study, we wish to showcase the unique utility of large language models (LLMs) in financial semantic annotation and alpha signal discovery. Leveraging a corpus of company-related tweets, we use an LLM to automatically assign multi-label event categories to high-sentiment-intensity tweets. We align these labeled sentiment signals with forward returns over 1-to-7-day horizons to evaluate their statistical efficacy and market tradability. Our experiments reveal that certain event labels consistently yield negative alpha, with Sharpe ratios as low as -0.38 and information coefficients exceeding 0.05, all statistically significant at the 95\% confidence level. This study establishes the feasibility of transforming unstructured social media text into structured, multi-label event variables. A key contribution of this work is its commitment to transparency and reproducibility; all code and methodologies are made publicly available. Our results provide compelling evidence that social media sentiment is a valuable, albeit noisy, signal in financial forecasting and underscore the potential of open-source frameworks to democratize algorithmic trading research.
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