This article quantifies the epidemiology of media narratives relevant to business cycles in the US, Japan, and Europe (euro area). We do so by first constructing daily business cycle indexes computed on the basis of the news topics the media writes about. At a broad level, the most important news narratives are shown to be associated with general macroeconomic developments, finance, and (geo-)politics. However, a vast set of narratives contributes to our index estimates across time, especially in times of expansion. In times of trouble, narratives associated with economic fluctuations become more sparse. Likewise, we show that narratives do go viral, but mostly so when growth is low. While narratives interact in complicated ways, we document that some are clearly associated with economic fundamentals. Other narratives, on the other hand, show no such relationship, and are likely better explained by classical work capturing the market’s animal spirits.

Blog coverage: Part 1, Part 2, Part 3
Media coverage: Dowjones.com
 

Asset returns, news topics, and media effects
with Leif Anders Thorsrud.

We decompose the textual data in a daily Norwegian business newspaper into news topics and investigate their predictive and causal role for asset prices. Our three main findings are: (1) a one unit innovation in the news topics predict roughly a 1 percentage point increase in close-to-open returns and significant continuation patterns peaking at 4 percentage points after 15 business days, with little sign of reversal; (2) simple zero-cost news-based investment strategies yield significant annualized risk-adjusted returns of up to 20 percent; and (3) during a media shortage, due to an exogenous strike, returns for firms particularly exposed to our news measure experience a substantial fall. Our estimates suggest that between 20 to 40 percent of the news topics’ predictive power is due to the causal media effect. Together these findings lend strong support for a rational attention view where the media alleviate information frictions and disseminate fundamental information to a large population of investors.

Components of Uncertainty [slidesR&R: International Economic Review

Uncertainty is acknowledged to be a source of economic fluctuations. But, does the type of uncertainty matter for the economy’s response to an uncertainty shock? This paper offers a novel identification strategy to disentangle different types of uncertainty. It uses machine learning techniques to classify different types of news instead of specifying a set of keywords. It is found that, depending on its source, the effects of uncertainty on macroeconomic variable may differ. I find that both good (expansionary effect) and bad (contractionary effect) types of uncertainty exist.

Media coverage: CentralBanking.com

Work in progress: