FedNLP: An interpretable NLP System to Decode Federal Reserve Communications

TL;DR

FedNLP is an interpretable NLP system designed to analyze Federal Reserve communications, providing insights like sentiment, summaries, and rate predictions without requiring coding skills.

Contribution

It introduces a multi-component NLP system that combines various models for comprehensive analysis of complex Fed communications, enhancing interpretability and usability.

Findings

Effective sentiment analysis of Fed communications

Accurate prediction of Federal Funds Rate movements

Visualizations aiding interpretation of model results

Abstract

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.