GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves

Denis Peskoff, Adam Visokay, Sander V Schulhoff, Benjamin Wachspress, alan blinder, Brandon M. Stewart

Submission Type: Regular Short Paper

Submission Track: NLP Applications

Submission Track 2: Computational Social Science and Cultural Analytics

Keywords: FOMC, Fed, GPT, LLM, dissent

TL;DR: We study dissent in the statements and transcripts of the Federal Open Market Committee with GPT-4.

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.

Submission Number: 4850