  News is a pertinent source of information on financial risks and stress
factors, which nevertheless is challenging to harness due to the sparse and
unstructured nature of natural text. We propose an approach based on
distributional semantics and deep learning with neural networks to model and
link text to a scarce set of bank distress events. Through unsupervised
training, we learn semantic vector representations of news articles as
predictors of distress events. The predictive model that we learn can signal
coinciding stress with an aggregated index at bank or European level, while
crucially allowing for automatic extraction of text descriptions of the events,
based on passages with high stress levels. The method offers insight that
models based on other types of data cannot provide, while offering a general
means for interpreting this type of semantic-predictive model. We model bank
distress with data on 243 events and 6.6M news articles for 101 large European
banks.
