[ARTICLE] The authors 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.

by Andras Fulop (ESSEC Business School), Zalan Kocsis

They apply their technique on a 2007-2016 Reuters news corpus to create news indices on country fundamentals. Comparing the method with two popular alternatives in the literature, they find their 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 econometric application that investigates the fundamental content of asset prices in the sovereign credit risk arena, they show that including their news indices next to traditional macro variables significantly raises the explanatory power attributed to fundamentals. They also show that a large part of the covariance between the VIX index and sovereign spreads is related to global fundamentals captured by our indices.

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