GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves
This work addresses the problem for financial markets and policymakers who rely on accurate FOMC sentiment analysis, but it is incremental as it applies an existing AI method to a specific domain.
The researchers used GPT-4 to analyze FOMC meeting transcripts and minutes, finding that these documents reveal significant dissent among members on inflation, which is largely omitted from public statements, indicating that forecasting sentiment based solely on statements is insufficient.
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.