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

TL;DR

This paper introduces a large annotated dataset of FOMC communications, develops a hawkish-dovish classification task, benchmarks language models, and analyzes the impact of monetary policy stance on financial markets.

Contribution

It provides the largest dataset of FOMC texts, a novel classification task, and a market impact analysis, advancing research in financial NLP and policy analysis.

Findings

RoBERTa-large achieves best classification performance.

Constructed a monetary policy stance measure from FOMC documents.

Policy stance impacts treasury, stock markets, and macroeconomic indicators.

Abstract

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.