Analyzing FOMC Minutes: Accuracy and Constraints of Language Models
This work addresses financial analysts and economists by providing insights into FOMC communication, but it is incremental as it applies existing NLP methods to a specific domain.
The study analyzed FOMC minutes to understand their impact on financial markets, finding that FinBERT outperforms other models like VADER and GPT-4 in predicting negative sentiment accurately.
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.