Analyzing FOMC Minutes: Accuracy and Constraints of Language Models
- URL: http://arxiv.org/abs/2304.10164v2
- Date: Mon, 19 Feb 2024 15:24:39 GMT
- Title: Analyzing FOMC Minutes: Accuracy and Constraints of Language Models
- Authors: Wonseong Kim, Jan Frederic Sp\"orer, Siegfried Handschuh
- Abstract summary: The study reveals that the Federal Open Market Committee (FOMC) is careful to avoid expressing emotion in their sentences.
The analysis employs advanced language modeling techniques such as VADER and FinBERT, and a trial test with GPT-4.
- Score: 6.647569337929869
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
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