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.

翻译：本研究旨在分析联邦公开市场委员会（FOMC）在定期会议后发布的官方声明中使用的语言，以深入了解FOMC官方声明对金融市场和经济预测的影响。研究揭示，FOMC在措辞中刻意避免表达情绪，并遵循一套模板来涵盖经济状况。分析采用了先进的建模语言技术，如VADER和FinBERT，并利用GPT-4进行了试点测试。结果显示，FinBERT在准确预测负面情绪方面优于其他技术。然而，本研究也指出了当前使用自然语言处理技术分析FOMC文本所面临的挑战和局限，并提出了增强语言模型及探索替代方法的潜在方向。