mlsanityData to 7 Oct 2026

Analyzing FOMC Minutes: Accuracy and Constraints of Language Models

arXiv 2304.10164 ↗First posted 20 Apr 2023cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 51 references that are in the map. First posted on arXiv 20 Apr 2023.

Builds on

18 of 51 references.

References · 18

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6 more
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Citing papers per year: 2024: 1.

1 paper.

  1. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 86