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.

本文分析 FOMC 官方声明中的语言以获取对金融市场和经济预测的影响，结果表明，FOMC 小心避免表达情绪；使用 VADER，FinBERT 和 GPT-4 等先进的语言建模技术。FinBERT 能够更准确地预测负面情绪，但是分析强调了使用 NLPTechniques 分析 FOMC 文本的挑战和限制，并建议增强语言模型和探索替代方法。