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

• URL: http://arxiv.org/abs/2603.14313v1
• Date: Sun, 15 Mar 2026 10:07:28 GMT
• Title: Mind the Shift: Decoding Monetary Policy Stance from FOMC Statements with Large Language Models
• Abstract summary: 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.<n>Existing approaches typically treat stance detection as a standard classification problem, labeling each statement in isolation.<n>We introduce Delta-Consistent Scoring (DCS), an annotation-free framework that maps frozen large language model (LLM) representations to continuous stance scores.
• Score: 12.949322198287417
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Loom: Weaving Diagnostic Strands into Free-Text Consensus via Embedding-Space Reweighting [0.0]
  We present Loom, a generative consensus framework deployed for real-world Root Cause Analysis (RCA)<n>We show how Loom aggregates open-form hypotheses emitted by modular$s by projecting them into a continuous embedding space, and resolves conflicting signals with an iterative centroid-based reweighting algorithm.<n>We discuss our deployment experience, highlighting lessons learned regarding the trade-offs between agentic depth and inference latency, negative results in redundancy detection, and how deterministic consensus fosters trust among Subject Matter Experts.
  arXiv  Detail & Related papers  (2026-09-02T14:24:32Z)
• COSMO: Consensus-Driven Shift Modulation for Source-Free Domain Adaptation [79.43842208099619]
  Source-free domain adaptation (SFDA) adapts a source-trained model to an unlabeled target domain without source data.<n>We formulate VLM-guided SFDA as a sample-wise reliability-allocation problem.<n>We propose Consensus-Driven Shift Modulation (COSMO) to replace expert-to-expert guidance with co-adaptation.
  arXiv  Detail & Related papers  (2026-08-05T09:10:30Z)
• Not All Tokens Deserve Equal Credit: Counterfactual Sensitivity Credit Reallocation for Long-CoT Reasoning [3.9783774144289623]
  Critic-free methods such as GRPO convert response-level rewards into advantages and uniformly broadcast them across tokens.<n>We propose Counterfactual Sensitivity Credit Reallocation (CSCR), a simple extension of GRPO that reduces credit for highly sensitive tokens.<n>On long-CoT mathematical reasoning benchmarks, CSCR consistently outperforms GRPO baseline with the same number of policy updates.
  arXiv  Detail & Related papers  (2026-07-30T09:03:33Z)
• Inference-Time Steering for Cross-Lingual Factual Consistency in LLMs [51.56484100374058]
  Large Language Models (LLMs) demonstrate remarkable multilingual fluency, but their internal knowledge representations remain disproportionately biased toward high-resource languages.<n>We investigate whether these biases can be mitigated at inference time, forcing an English-prompted model to answer as if it were queried in target languages.<n>Experiments on Gemma 3 12B Instruct reveal persona prompting to be the strongest overall intervention, balancing efficacy, safety, and out-of-domain generalization.
  arXiv  Detail & Related papers  (2026-07-21T16:15:05Z)
• GRASP: Deterministic argument ranking in interaction graphs [65.66879897437157]
  We show that holistic judging suffers from substantial inter-model disagreement.<n>This instability arises from collapsing a debate's complex interaction structure into a single opaque score.<n>We propose GRASP, a deterministic framework that aggregates stable local interaction judgments into a global ranking.
  arXiv  Detail & Related papers  (2026-05-18T21:49:02Z)
• Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty [70.43119366710778]
  We introduce Agent-BRACE: Agent Belief state Representation via Abstraction and Confidence Estimation.<n>It is a method that decouples an LLM agent into a belief state model and a policy model, jointly optimized via reinforcement learning.<n>It achieves an average absolute improvement of +14.5% across long-horizon, partially observable language environments.
  arXiv  Detail & Related papers  (2026-05-12T02:37:04Z)
• Anchor-and-Resume Concession Under Dynamic Pricing for LLM-Augmented Freight Negotiation [5.225967186005105]
  Deriving $$ from the live spread enables adaptation but introduces a new problem: a pricing shift can cause the formula to retract a previous offer, violating monotonicity.<n>We propose a two-index anchor-and-resume framework that addresses both limitations.<n>A spread-derived $$ maps each load's margin structure to the correct concession posture, while the anchor-and-resume mechanism guarantees monotonically nondecreasing offers under arbitrary pricing shifts.
  arXiv  Detail & Related papers  (2026-04-22T16:17:07Z)
• Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs [84.3271821505699]
  Chain of Modality (CoM) is an agentic framework that transitions multimodal fusion from passive concatenation to dynamic orchestration.<n> operating in either a training-free or a data-efficient SFT setting, CoM achieves robust and consistent generalization across diverse benchmarks.
  arXiv  Detail & Related papers  (2026-04-16T01:21:14Z)
• From Global to Granular: Revealing IQA Model Performance via Correlation Surface [83.65597122328133]
  We present textbfGranularity-Modulated Correlation (GMC), which provides a structured, fine-grained analysis of IQA performance.<n>GMC includes a textbfDistribution Regulator that regularizes correlations to mitigate biases from non-uniform quality distributions.<n>Experiments on standard benchmarks show that GMC reveals performance characteristics invisible to scalar metrics, offering a more informative and reliable paradigm for analyzing, comparing, and deploying IQA models.
  arXiv  Detail & Related papers  (2026-01-29T13:55:26Z)
• All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection [67.89888669159899]
  RFC Bench is a benchmark for evaluating large language models on financial misinformation under realistic news.<n>The benchmark defines two complementary tasks: reference free misinformation detection and comparison based diagnosis.
  arXiv  Detail & Related papers  (2026-01-07T18:18:28Z)
• History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting [1.8911049192403768]
  We introduce macro-contextual retrieval, a retrieval-augmented forecasting framework that grounds each prediction in historically analogous macroeconomic regimes.<n>We trained on seventeen years of S&P 500 data (2007-2023) and evaluated OOD on AAPL (2024) and XOM (2024), and the framework consistently narrows the CV to OOD performance gap.<n>By operationalizing the principle that "financial history may not repeat, but it often rhymes," this work demonstrates that macro-aware retrieval yields robust, explainable forecasts under distributional change.
  arXiv  Detail & Related papers  (2025-11-12T21:34:23Z)
• COMPASS: Context-Modulated PID Attention Steering System for Hallucination Mitigation [2.1521364454860525]
  We introduce a lightweight, interpretable control framework that embeds a model-based feedback loop directly within decoding.<n>We show that a PID controller dynamically modulates attention heads to maintain factual consistency without retraining or multi-pass decoding.
  arXiv  Detail & Related papers  (2025-11-05T05:30:28Z)
• HAVE: Head-Adaptive Gating and ValuE Calibration for Hallucination Mitigation in Large Language Models [29.677280135028436]
  Large Language Models (LLMs) often produce hallucinations in retrieval-augmented or long-context generation.<n> HAVE (Head-Adaptive Gating and ValuE) is a parameter-free decoding framework that addresses head importance and raw attention weights.<n> HAVE consistently reduces hallucinations and outperforms strong baselines, including DAGCD, with modest overhead.
  arXiv  Detail & Related papers  (2025-09-08T12:06:09Z)
• Can We Reliably Predict the Fed's Next Move? A Multi-Modal Approach to U.S. Monetary Policy Forecasting [2.6396287656676733]
  This study examines whether predictive accuracy can be enhanced by integrating structured data with unstructured textual signals from Federal Reserve communications.<n>Our results show that hybrid models consistently outperform unimodal baselines.<n>For monetary policy forecasting, simpler hybrid models can offer both accuracy and interpretability, delivering actionable insights for researchers and decision-makers.
  arXiv  Detail & Related papers  (2025-06-28T05:54:58Z)
• Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations [50.31129784616845]
  We learn a Distributed Marker Representation (DMR) by utilizing the unlimited discourse marker data with a latent discourse sense. Our method also offers a valuable tool to understand complex ambiguity and entanglement among discourse markers and manually defined discourse relations.
  arXiv  Detail & Related papers  (2023-06-19T00:49:51Z)
• AmbiFC: Fact-Checking Ambiguous Claims with Evidence [57.7091560922174]
  We present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. We analyze disagreements arising from ambiguity when comparing claims against evidence in AmbiFC. We develop models for predicting veracity handling this ambiguity via soft labels.
  arXiv  Detail & Related papers  (2021-04-01T17:40:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.