警惕轉向：運用大型語言模型解讀聯準會政策聲明中的貨幣政策立場

Mind the Shift: Decoding Monetary Policy Stance from FOMC Statements with Large Language Models

March 15, 2026

作者: Yixuan Tang, Yi Yang

cs.AI

摘要

联邦公开市场委员会（FOMC）声明是货币政策信息的主要来源，其措辞的细微变化甚至能牵动全球金融市场。因此，核心任务在于量化这些文本传递的鹰派-鸽派立场。现有方法通常将立场检测视为标准分类问题，对每份声明进行独立标注。然而，货币政策沟通的解读本质上是相对的：市场反应不仅取决于声明的基调，更关键的是其相较于历次会议的立场变化。我们提出Delta一致性评分（DCS）框架，该无标注方法通过联合建模绝对立场与会议间相对变化，将冻结的大语言模型（LLM）表征映射为连续立场分数。DCS不依赖人工标注的鹰派-鸽派标签，而是以连续会议作为自监督信号，同步学习每份声明的绝对立场分数及连续声明间的相对变化分数。通过delta一致性目标函数，确保绝对分数的变化与相对变化保持一致，从而在没有人工标注的情况下还原时间连贯的立场轨迹。在四种LLM骨干网络的测试中，DCS始终优于监督式探测器和LLM作为评判员的基线模型，在句子级鹰派-鸽派分类任务中准确率最高达71.1%。所得会议级分数亦具经济意义：与通胀指标显著相关，且与国债收益率波动存在显著关联。总体而言，研究结果表明LLM表征中编码的货币政策信号可通过相对时间结构进行有效提取。

English

Federal Open Market Committee (FOMC) statements are a major source of monetary-policy information, and even subtle changes in their wording can move global financial markets. A central task is therefore to measure the hawkish--dovish stance conveyed in these texts. Existing approaches typically treat stance detection as a standard classification problem, labeling each statement in isolation. However, the interpretation of monetary-policy communication is inherently relative: market reactions depend not only on the tone of a statement, but also on how that tone shifts across meetings. We introduce Delta-Consistent Scoring (DCS), an annotation-free framework that maps frozen large language model (LLM) representations to continuous stance scores by jointly modeling absolute stance and relative inter-meeting shifts. Rather than relying on manual hawkish--dovish labels, DCS uses consecutive meetings as a source of self-supervision. It learns an absolute stance score for each statement and a relative shift score between consecutive statements. A delta-consistency objective encourages changes in absolute scores to align with the relative shifts. This allows DCS to recover a temporally coherent stance trajectory without manual labels. Across four LLM backbones, DCS consistently outperforms supervised probes and LLM-as-judge baselines, achieving up to 71.1% accuracy on sentence-level hawkish--dovish classification. The resulting meeting-level scores are also economically meaningful: they correlate strongly with inflation indicators and are significantly associated with Treasury yield movements. Overall, the results suggest that LLM representations encode monetary-policy signals that can be recovered through relative temporal structure.