  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.
