We propose a method to quantify narratives from textual data in a structured manner, and identify what we label “narrative monetary policy surprises” as the difference in narrative focus in central bank communication accompanying interest rate meetings and economic media coverage prior to those meetings. Our proposed method is fast and simple, and relies on a Singular Value Decomposition of the different texts and articles coupled with a unit rotation identification scheme. Identifying monetary policy surprises using this type of data and identification gives surprise measures that are uncorrelated with conventional monetary policy surprises, and, in contrast to such surprises, have a significant effect on subsequent media coverage. In turn, narrative monetary policy surprises lead to macroeconomic responses similar to what the newer monetary policy literature associates with the information component of monetary policy communication. Our study highlights the importance of written central bank communication and the role of the media as information intermediaries.
News-driven inflation expectations and information rigidities R&R: Journal of Monetary Economics
with Leif Anders Thorsrud and Julia Zhulanova.
We investigate the role played by the media in the expectations formation process of households. Using a novel news-topic-based approach we show that the news types the media choose to report on are good predictors of households’ stated inflation expectations. In turn, in a noisy information model setting, augmented with a simple media channel, we show that the underlying time series properties of relevant news topics explain the time-varying information rigidity among households. As such, we not only provide a new estimate on the degree of time variation in households’ information rigidity, but also provide, using a large news corpus and machine learning algorithms, robust and new evidence highlighting the role of the media for understanding inflation expectations and information rigidities.
Media coverage: Dowjones.com
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 [slides] R&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