Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis
This work addresses the need for better tools to analyze monetary policy impacts on financial markets, though it is incremental as it applies existing NLP methods to a new financial dataset.
The authors tackled the problem of quantifying monetary policy's influence on financial markets by constructing a large annotated dataset of FOMC documents and developing a hawkish-dovish classification task, benchmarking models like RoBERTa-large to create a measure that was evaluated on treasury and stock markets.
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.