FedNLP: An interpretable NLP System to Decode Federal Reserve Communications
This system addresses the challenge for financial analysts and policymakers in decoding ambiguous Fed communications, but it is incremental as it applies existing NLP methods to a specific domain.
The authors tackled the problem of analyzing complex Federal Reserve communications by developing FedNLP, an interpretable NLP system that assists end-users in understanding these documents without coding, demonstrating results such as sentiment analysis and Federal Funds Rate predictions.
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.