Mind Your Language: Market Responses to Central Bank Speeches

Abstract

Researchers have carefully studied post-meeting central bank communication and have found that it often moves markets, but they have paid less attention to the more frequent central bankers' speeches. We create a novel dataset of US Federal Reserve speeches and use supervised multimodal natural language processing methods to identify how monetary policy news affect financial volatility and tail risk through implied changes in forecasts of GDP, inflation, and unemployment. We find that news in central bankers’ speeches can help explain volatility and tail risk in both equity and bond markets. We also find that markets attend to these signals more closely during abnormal GDP and inflation regimes. Our results challenge the conventional view that central bank communication primarily resolves uncertainty.

Keywords: Central Bank Communication, Multimodal Machine Learning, Natural Language Processing, Speech Analysis, High-Frequency Data, Volatility, Tail Risk

JEL Classification: E52, C45, C53, G12, G14