Abstract

The nonlinear nexus between financial conditions indicators and the conditional distribution of GDP growth has recently been challenged (on the grounds that financial indicators do not contain more information than real indicators). We show how one can use textual economic news combined with a shallow Neural Network to construct an alternative financial indicator based on word embeddings. By design the index associates growth-at-risk to news about credit, leverage and funding, and we document that the proposed indicator is particularly informative about the lower left tail of the GDP distribution and delivers significantly better out-of-sample density forecasts than commonly used alternatives.
Linking the derived index to credit market valuation indicators and balance-sheet aggregates suggests the news-based index carries information about both sentiment and
fundamentals but also potential independent media effects.