Monetary policy pronouncements by Federal Open Market Committee (FOMC) are a
major driver of financial market returns. We construct the largest tokenized
and annotated dataset of FOMC speeches, meeting minutes, and press conference
transcripts in order to understand how monetary policy influences financial
markets. In this study, we develop a novel task of hawkish-dovish
classification and benchmark various pre-trained language models on the
proposed dataset. Using the best-performing model (RoBERTa-large), we construct
a measure of monetary policy stance for the FOMC document release days. To
evaluate the constructed measure, we study its impact on the treasury market,
stock market, and macroeconomic indicators. Our dataset, models, and code are
publicly available on Huggingface and GitHub under CC BY-NC 4.0 license.

本研究通过构建 FOMC 讲话、会议记录和新闻发布会文本的最大化标记数据集，研究了货币政策如何影响金融市场；发展了新的鹰派和鸽派分类任务，并在所提出的数据集上进行了基准测试；使用 RoBERTa-large 最佳模型构建了 FOMC 文件发布日的货币政策立场的度量，并以国债市场、股市和宏观经济指标为评估指标。

万亿美元的话语：一个新的金融数据集、任务和市场分析

Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis

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。