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arXiv Computation and Language By Yixuan Tang, Yi Yang

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

Read the original on arXiv Computation and Language →

The paper introduces Delta-Consistent Scoring (DCS), an annotation‑free method that uses large language model representations to produce continuous monetary‑policy stance scores. DCS jointly learns absolute stance for each FOMC statement and relative shifts between consecutive statements, enforcing consistency through a delta‑consistency objective. The approach outperforms supervised probes and LLM‑as‑judge baselines, achieving up to 71.1% accuracy on sentence‑level hawkish‑dovish classification and producing meeting‑level scores that correlate strongly with inflation indicators and Treasury yield movements.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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