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

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  • Agam Shah
  • Suvan Paturi
  • Sudheer Chava

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.

Suggested Citation

  • Agam Shah & Suvan Paturi & Sudheer Chava, 2023. "Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis," Papers 2305.07972, arXiv.org.
    • Handle: RePEc:arx:papers:2305.07972
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    References listed on IDEAS

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    Cited by:

    1. Kwok Ping Tsang & Zichao Yang, 2023. "Agree to Disagree: Measuring Hidden Dissent in FOMC Meetings," Papers 2308.10131, arXiv.org, revised Nov 2024.
    2. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.

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