Financial Markets and News about the Coronavirus

Abstract

I examine how financial markets interact with news about the COVID-19 pandemic. A twelve topic model optimizes the trade-off between number of topics and topic coherence. Using this model, I show that before mid-March 2020 markets react more to the same quantum of news when volatility is higher – a phenomenon I call hypersensitivity. Formal tests identify a structural break in mid-March, post which markets are no longer hypersensitive. In the hypersensitive stage, markets are overly volatile and overreact to news. Despite hypersensitivity, lagged prices better forecast future COVID-19 case counts than do lagged news.

Keywords: asset pricing; natural disasters; natural language processing

JEL Classification: G10, G14