FedNLP: An interpretable NLP System to Decode Federal Reserve Communications
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Abstract (arXiv)
The Federal Reserve System (the Fed) plays a significant role in affecting monetary policy and financial conditions worldwide. Although it is important to analyse the Fed's communications to extract useful information, it is generally long-form and complex due to the ambiguous and esoteric nature of content. In this paper, we present FedNLP, an interpretable multi-component Natural Language Processing system to decode Federal Reserve communications. This system is designed for end-users to explore how NLP techniques can assist their holistic understanding of the Fed's communications with NO coding. Behind the scenes, FedNLP uses multiple NLP models from traditional machine learning algorithms to deep neural network architectures in each downstream task. The demonstration shows multiple results at once including sentiment analysis, summary of the document, prediction of the Federal Funds Rate movement and visualization for interpreting the prediction model's result.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 11 Jun 2021.
Builds on
11 of 28 references.
References · 11
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Built on by
Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2024A Survey of Large Language Models in Finance (FinLLMs)cited by 77
- 2023StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Seriescited by 9
- 2026From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discoverycited by 4
- 2024Logistic Regression makes small LLMs strong and explainable "tens-of-shot" classifierscited by 3
- 2026From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery14 May 2026
- 2024Logistic Regression makes small LLMs strong and explainable "tens-of-shot" classifiers6 Aug 2024
- 2024A Survey of Large Language Models in Finance (FinLLMs)4 Feb 2024
- 2023StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series23 Jan 2023