Hawkish or Dovish? That Is the Question: Agentic Retrieval of FED Monetary Policy Report

This paper develops a Natural Language Processing (NLP) pipeline to quantify the hawkish–dovish stance in the Federal Reserve’s semiannual Monetary Policy Reports (MPRs). The goal is to transform long-form central-bank text into reproducible stance scores and interpretable policy signals for research and monitoring. The corpus comprises 26 MPRs (26 February 2013 to 20 June 2025). PDFs are parsed and segmented and chunks are embedded, indexed with FAISS, retrieved via LangChain, and scored by GPT-4o on a continuous scale from −2 (dovish) to +2 (hawkish). Reliability is assessed with a four-dimension validation suite: (i) semantic consistency using cosine-similarity separation, (ii) numerical consistency against theory-implied correlation ranges (e.g., Taylor-rule logic), (iii) bootstrap stability of reported metrics, and (iv) content-quality diagnostics. Results show a predominant Neutral distribution (50.0%), with Dovish (26.9%) and Hawkish (23.1%). The average stance is near zero (≈0.019) with volatility σ ≈ 0.866, and the latest window exhibits a hawkish drift of ~+0.8 points. The Numerical Consistency Score is 0.800, and the integrated validation score is 0.796, indicating publication-grade robustness. We conclude that an embedding-based, agentic RAG approach with GPT-4o yields a scalable, auditable measure of FED communication; limitations include biannual frequency and prompt/model sensitivity, but the framework is suitable for policy tracking and empirical applications.