Abstract
We propose a novel method to extract textual information about macro fundamentals. The method has two pillars, a set of pre-defined regular expressions and a Bayesian feature selection model. We apply our technique to a 2007–2022 Reuters news corpus from Factiva to create news indices of country fundamentals. Compared to several literature alternatives, we find our method to better identify and discriminate among fundamentals based on both (i) observed economic surprises (macro announcements compared to Bloomberg survey expectations) and (ii) labels on a manually classified test sample. In an application that investigates the determinants of sovereign credit spreads, we show that including our news indices next to traditional macro variables significantly raises the explanatory power attributed to fundamentals. We also show that part of the covariance between sovereign spreads and the VIX and US high yield indices is related to global fundamentals captured by our indices.
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