GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves

TL;DR

This paper uses GPT-4 to analyze FOMC meeting transcripts, revealing that dissenting opinions among members are largely omitted from official statements, thus providing a more accurate picture of internal attitudes on inflation.

Contribution

It introduces a novel GPT-4 based method to quantify dissent in FOMC meetings, highlighting discrepancies between internal views and public statements.

Findings

Transcripts show diverse views on macroeconomic outlook.

Official statements omit most diverging opinions.

Forecasting sentiment from statements alone is insufficient.

Abstract

Markets and policymakers around the world hang on the consequential monetary policy decisions made by the Federal Open Market Committee (FOMC). Publicly available textual documentation of their meetings provides insight into members' attitudes about the economy. We use GPT-4 to quantify dissent among members on the topic of inflation. We find that transcripts and minutes reflect the diversity of member views about the macroeconomic outlook in a way that is lost or omitted from the public statements. In fact, diverging opinions that shed light upon the committee's "true" attitudes are almost entirely omitted from the final statements. Hence, we argue that forecasting FOMC sentiment based solely on statements will not sufficiently reflect dissent among the hawks and doves.