Can ChatGPT Decipher Fedspeak?

Abstract

This paper investigates the ability of Generative Pre-training Transformer (GPT) models to decipher Fedspeak, a term used to describe the technical language used by the Federal Reserve to communicate on monetary policy decisions. We evaluate the ability of GPT models to classify the policy stance of Federal Open Market Committee announcements relative to human assessment. We show that GPT models deliver a considerable improvement in classification performance over other commonly used methods. Furthermore, we show that GPT-4 model demonstrates reasoning abilities on par with humans in this domain.

Keywords: Natural Language Processing (NLP), Generative Pre-training Transformer (GPT), Federal Reserve Communication, Applications, Artificial Intelligence (AI)

JEL Classification: E52, E58, C88