Analyzing FOMC Minutes: Accuracy and Constraints of Language Models

TL;DR

This paper evaluates the effectiveness of advanced language models in analyzing FOMC statements, revealing their strengths in sentiment prediction and highlighting current limitations in NLP techniques for economic text analysis.

Contribution

It introduces an analysis of FOMC statements using models like VADER, FinBERT, and GPT-4, and discusses their performance and limitations in capturing economic sentiment.

Findings

FinBERT outperforms other models in negative sentiment prediction

FOMC statements are carefully neutral and template-based

Current NLP techniques face challenges in economic text analysis

Abstract

This research article analyzes the language used in the official statements released by the Federal Open Market Committee (FOMC) after its scheduled meetings to gain insights into the impact of FOMC official statements on financial markets and economic forecasting. The study reveals that the FOMC is careful to avoid expressing emotion in their sentences and follows a set of templates to cover economic situations. The analysis employs advanced language modeling techniques such as VADER and FinBERT, and a trial test with GPT-4. The results show that FinBERT outperforms other techniques in predicting negative sentiment accurately. However, the study also highlights the challenges and limitations of using current NLP techniques to analyze FOMC texts and suggests the potential for enhancing language models and exploring alternative approaches.