GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves (2023)

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.

## the road to fomc transparency the federal open market committee (fomc) is responsible for controlling inflation in the united states, using instruments which dramatically affect the housing and financial markets, among others. for most of the 20 th century, conventional wisdom held that monetary policy is most effective when decision-making was shrouded in secrecy; the tightlipped alan greenspan, a past chairman of the fed, quipped about "learning to mumble with great incoherence." but times change. @xcite show how the emergence of greater transparency and strategic communication became an important feature of 21 st century central banking. fed communication is now an integral component of monetary policy, and "fed watchers" dote on every word. the fomc first started releasing public statements following their meetings in february 1994. this meager documentation grew and now consists of three types for each official meeting: carefully produced and highly stylized one page statements are released immediately after each fomc meeting, followed about three weeks later by lengthier minutes, and finally five years later by full, verbatim transcripts. subsequently this triplet is referred to as documents. we find minutes closely reflect the content of transcripts, so to avoid redundancy, we focus our analysis on transcripts and statements. increased fomc communication has prompted social science research spanning the disciplines of economics, sociology, finance and political science (section 2.1). financial market participants are also keenly interested. billions, if not trillions of dollars are traded on the fed's words. the interpretationsright or wrong-of what the fomc "really means" move markets and affect the economy. however, relying upon documents as data in the social sciences is a challenge due to the lack of structure and the cost of annotation @xcite @xcite . @xcite show that generative pre-training transformer (gpt) models outperform a suite of commonly used nlp methods on text quantification. motivated by these results, we set out to quantify the language of the fomc using gpt-4 (openai, 2023) by preparing a combined data set of fomc documents from 19942016 (section 2). 1 we conclude that transcripts contain more dissent than statements (section 3). ## fomc data: transcript to statement the fomc normally meets eight times per year in order to assess current economic conditions, ultimately deciding upon the path for monetary policy. we aggregate and release the official publicly available text documenting these deliberations by the fed as an aligned corpora of documents from 1994 to 2016. 2 these text documents are similar in content, but transcripts and statements are dramatically different in style and detail (figure 1 ). 3 for our purposes, the statements required no pre-processing. for the transcripts, we use regular expressions to partition and then re-aggregate the text by each unique speaker. see appendix d for an example of each document type.