Central Bank Communication and the Yield Curve: A Semi-Automatic Approach using Non-Negative Matrix Factorization

TL;DR

This paper introduces a semi-automatic machine learning approach to analyze FOMC statements, identifying key themes that influence the U.S. Treasury yield curve, especially during financial crises.

Contribution

It develops a novel semi-automatic topic modeling methodology to extract and assess the impact of communication themes on financial markets.

Findings

FOMC statements can be decomposed into three main topics.

Financial themes significantly influence the yield curve during crises.

The impact is primarily observed in the curvature of the yield curve.

Abstract

Communication is now a standard tool in the central bank's monetary policy toolkit. Theoretically, communication provides the central bank an opportunity to guide public expectations, and it has been shown empirically that central bank communication can lead to financial market fluctuations. However, there has been little research into which dimensions or topics of information are most important in causing these fluctuations. We develop a semi-automatic methodology that summarizes the FOMC statements into its main themes, automatically selects the best model based on coherency, and assesses whether there is a significant impact of these themes on the shape of the U.S Treasury yield curve using topic modeling methods from the machine learning literature. Our findings suggest that the FOMC statements can be decomposed into three topics: (i) information related to the economic conditions and the mandates, (ii) information related to monetary policy tools and intermediate targets, and (iii) information related to financial markets and the financial crisis. We find that statements are most influential during the financial crisis and the effects are mostly present in the curvature of the yield curve through information related to the financial theme.