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

TL;DR

This paper introduces Delta-Consistent Scoring (DCS), a self-supervised framework leveraging large language models to decode the hawkish-dovish stance in FOMC statements by modeling absolute and relative shifts without manual labels.

Contribution

The paper presents DCS, a novel self-supervised method that captures temporal stance shifts in monetary policy statements using LLM representations, outperforming supervised approaches.

Findings

DCS achieves up to 71.1% accuracy in stance classification.

Meeting-level scores correlate with inflation indicators.

Representation-based scores relate to Treasury yield movements.

Abstract

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.