Finding 5925Emerging EvidenceValidation V0
This paper presents a novel multi-agent LLM framework for extracting conditional expectations from Federal Reserve communications, enabling construction of narrative monetary policy surprises. Its originality lies in combining LLMs, narrative identification, and real-time analysis, yielding theoretically consistent, less noisy policy shock measures with significant implications for macroeconomics, finance, and AI-driven trading strategies.
78%Confidence
1Evidence objects
v1Version
DraftStatus
Evidence trail
Supporting78% linkage confidence
This paper presents a novel multi-agent LLM framework for extracting conditional expectations from Federal Reserve communications, enabling construction of narrative monetary policy surprises. Its originality lies in combining LLMs, narrative identification, and real-time analysis, yielding theoretically consistent, less noisy policy shock measures with significant implications for macroeconomics, finance, and AI-driven trading strategies.
key_findings bullet 4 · key_findings
Inspect source: Monetary Policy Schocks: A New Hope Large Language Models and Central Bank Communication →This Finding was extracted from the configured corpus. It is versioned, traceable, and may evolve through editorial review or new corpus evidence.