Measuring economic uncertainty using news-media textual data

We develop a news-media textual measure of aggregate economic uncertainty – the fraction of Financial Times articles containing uncertainty-related keyphrases – for 1982–2014 at daily to annual frequencies. We contribute to the literature in three areas. First, we provide a measurement framework that links observed expressions of uncertainty in newspaper articles to a latent propensity to express uncertainty, which we argue is an ordinal proxy for the uncertainty that matters for economic decision-making, namely the intensity of the cognitive state of uncertainty. We use this framework to estimate how the noise-to-signal ratios varies with sample size (or frequency) and show that noise variance is modest at monthly and lower frequencies, and approaching signal variance at daily frequency. Second, we study key choices in the empirical implementation of such measures more deeply than has been done previously, focusing on uncertainty keyphrase selection, isolating economic uncertainty, de-duplication of articles, and appropriate scaling of the uncertainty measure, with a critique of scaling methods commonly used in the literature. Our findings provide empirical foundations for the extant literature, and evidence-based recommendations for methodological improvements. Third, we conduct the first detailed comparative analysis of a news-media uncertainty measure with another uncertainty proxy, stock returns volatility. Our narrative analysis establishes the plausibility of our news-media measure. Our quantitative analysis reveals a strong relationship to stock volatility on average. But this relationship breaks down periodically, with timing that suggests that the semantics of the word “uncertainty” may be biased towards downside uncertainty or risk. Finally, we establish the absence of Granger causation between the measures down to daily frequency, except for a one-day lead of stock volatility over news-media uncertainty, which is to be expected given that the FT is published before the market opens.