Using Interpretable NLP to Analyze Federal Reserve Communications

Summary

FedNLP is presented as a multi-component natural language processing system for examining Federal Reserve communications, which can be lengthy and difficult to interpret. It combines methods ranging from traditional machine learning to deep neural networks across several analysis tasks. The intended users can explore the outputs without writing code, while the underlying models perform the analysis.

The system presents sentiment analysis, document summaries, predictions of Federal Funds Rate movement, and visual explanations of its predictions. These components offer complementary ways to inspect policy language and its possible implications. The document describes a demonstration of the system, but does not provide enough detail to assess model accuracy, training data, validation procedures, or performance across different kinds of Fed communication. Rate predictions should therefore be treated as model outputs to investigate rather than established forecasts. The work is useful as an example of combining text analysis, rate-movement prediction, and interpretability in a monetary-policy research workflow.

Key ideas

• FedNLP applies multiple NLP models to complex Federal Reserve communications.
• Its analysis includes sentiment, document summaries, and predictions of Federal Funds Rate movement.
• Visualizations are used to help users interpret the model’s predictions.
• The described demonstration does not establish prediction accuracy or generalizability.

Tags

# FedNLP: An interpretable NLP System to Decode Federal Reserve Communications


# FedNLP: An interpretable NLP System to Decode Federal Reserve Communications









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.