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Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements*


Abstract: We present a text-based metric for monetary policy stance using official and alternative Federal Open Market Committee statements. Our advanced natural language processing, with numeric property detection, jointly evaluates quantitative decisions like interest rates and qualitative explanations for these choices from texts. Monetary policy stance is decomposed into expected stance and surprise components by leveraging high-frequency bond futures data around FOMC announcements. We examine responses of stock returns to counterfactual (more dovish or hawkish) policy surprises through alternative language. This investigation yields valuable insights into monetary policy transmission.

JEL Classification: E30; E40; E50; G12;

https://doi.org/10.18651/RWP2020-14

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Provider: Federal Reserve Bank of Kansas City

Part of Series: Research Working Paper

Publication Date: 2023-11-14

Number: RWP 20-14

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