Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis
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Abstract (arXiv)
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.
Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). Builds on 7 of its 47 references that are in the map. First posted on arXiv 13 May 2023.
Builds on
7 of 47 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 378
- 2020FinBERT: A Pretrained Language Model for Financial Communicationscited by 161
- 2022WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domaincited by 117
- 2022Benchmarking Machine Learning Models to Predict Corporate Bankruptcycited by 4
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Citing papers per year: 2023: 8; 2024: 24; 2025: 20; 2026: 21.
73 papers.
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- 2024A Survey of Large Language Models in Finance (FinLLMs)cited by 77
- 2024Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuningcited by 59
- 2025Demystifying Domain-adaptive Post-training for Financial LLMscited by 24
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- 2026From Tone to Trajectory: Continuous Sentiment and the Shape of Monetary Policy Communication24 Aug 2026
- 2026Damage Predicts Recovery: When Calibration Data Matters in Compressing Financial LLMs12 Aug 2026
- 2026TELLME: Test-Enhanced Learning for Language Model Enrichment12 Aug 2026
- 2026LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification12 Aug 2026
- 2026From Financial Sentiment Classification to Return Predictability: A QLoRA Benchmark of Large Language Models4 Aug 2026
- 2026A Persona-based Rate Action Index29 Jul 2026
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