发表机构
School of ASEE, Beihang University; School of BME, Beihang University; CAIR and CECS, VinUniversity（北京航空航天大学ASEE学院; 北京航空航天大学生物医学工程学院; VinUniversity CAIR与CECS机构）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对金融情感分析仅压缩新闻为单一极性评分的局限，提出用LLaMA-3.1-70B提取金融新闻的结构化语义特征，结合情感与结构化特征可提升股票预测性能，为多维金融NLP开辟新方向。
AI 中文摘要
金融情感分析已成为新闻驱动型股票预测的标准组成部分，但它将丰富的多维度新闻文章简化为单一的极性评分。我们假设金融新闻编码了多个正交信息维度——事件类型、影响范围、时间范围和语义置信度，这些是情感无法单独捕捉的，且这些维度具有独立的预测价值。为验证这一假设，我们提出了一个结构化信息提取框架，利用LLaMA-3.1-70B从金融新闻中提取六个语义维度。通过对FNSPID数据集中的41618个新闻-股票对进行大规模实验，我们发现：（i）FinBERT情感特征在非线性模型下表现出较强的预测能力（F1=0.576），但在线性模型下性能显著较弱（F1=0.230），揭示了情感与收益之间存在高度非线性关系；（ii）大语言模型提取的结构化特征虽然单独较弱，但能捕捉到与情感正交的信息，两种方法之间存在53.5%的系统分歧率可证明这一点；（iii）结合两种信号源可得到F1=0.600，显著优于单独使用任一信号源（p<0.0001），且在全部七个事件类型中均有一致提升。消融实验证实，非情感结构维度（事件类型、影响主体、时间范围、置信度）在FinBERT之外独立贡献ΔF1=+0.019。特征重要性分析显示六个提取维度的贡献均衡（14%-21%），表明将新闻压缩为单一情感评分会造成大量信息损失。我们的结果表明，金融文本中的情感-语义解耦是系统性且可利用的，为多维金融自然语言处理开辟了新方向。
英文摘要
Financial sentiment analysis has become a standard component in news-driven stock prediction, yet it reduces rich, multi-dimensional news articles to a single polarity score. We hypothesize that financial news encodes multiple orthogonal information dimensions---event type, impact scope, temporal horizon, and semantic confidence---that sentiment alone cannot capture, and that these dimensions carry independent predictive value. To test this hypothesis, we propose a structured information extraction framework that leverages LLaMA-3.1-70B to extract six semantic dimensions from financial news. Through large-scale experiments on 41,618 news--stock pairs from the FNSPID dataset, we find that (i) FinBERT sentiment features exhibit strong predictive power under nonlinear models (F1=0.576) but substantially weaker performance under linear models (F1=0.230), revealing a highly nonlinear sentiment--return relationship; (ii) LLM-extracted structured features, while individually weaker, capture information orthogonal to sentiment, as evidenced by a 53.5% systematic disagreement rate between the two approaches; and (iii) combining both signal sources yields F1=0.600, significantly outperforming either alone ($p < 0.0001$), with consistent improvements across all seven event types. Ablation experiments confirm that non-sentiment structural dimensions (event type, impact subject, time horizon, confidence) independently contribute $Δ\text{F1} = +0.019$ beyond FinBERT alone. Feature importance analysis reveals balanced contributions from all six extracted dimensions (14--21%), demonstrating that compressing news into a single sentiment score incurs substantial information loss. Our results suggest that the sentiment--semantics decoupling in financial text is systematic and exploitable, opening a new direction for multi-dimensional financial NLP.
