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

• URL: http://arxiv.org/abs/2305.07972v1
• Date: Sat, 13 May 2023 17:32:39 GMT
• Title: Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis
• Authors: Agam Shah and Suvan Paturi and Sudheer Chava
• Abstract summary: We construct the largest tokenized and annotated dataset of Federal Open Market Committee (FOMC) speeches, meeting minutes, and press conference transcripts. Using the best-performing model (RoBERTa-large), we construct a measure of monetary policy stance for the document release days. Our dataset, models, and code are publicly available on Huggingface and GitHub under CC BY-NC 4.0 license.
• Score: 1.933681537640272
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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