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：本研究分析了联邦公开市场委员会（FOMC）在例行会议后发布的官方声明所使用的语言，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在表述中谨慎避免流露情绪，并遵循一套覆盖经济状况的模板。分析采用了VADER和FinBERT等先进语言建模技术，并结合GPT-4进行试验测试。结果表明，FinBERT在准确预测负面情绪方面优于其他技术。然而，研究也凸显了当前NLP技术分析FOMC文本的挑战与局限性，并提出了增强语言模型及探索替代方法的可能性。