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。