  In this paper we focus our attention on the exploitation of the information
contained in financial news to enhance the performance of a classifier of bank
distress. Such information should be analyzed and inserted into the predictive
model in the most efficient way and this task deals with all the issues related
to text analysis and specifically analysis of news media. Among the different
models proposed for such purpose, we investigate one of the possible deep
learning approaches, based on a doc2vec representation of the textual data, a
kind of neural network able to map the sequential and symbolic text input onto
a reduced latent semantic space. Afterwards, a second supervised neural network
is trained combining news data with standard financial figures to classify
banks whether in distressed or tranquil states, based on a small set of known
distress events. Then the final aim is not only the improvement of the
predictive performance of the classifier but also to assess the importance of
news data in the classification process. Does news data really bring more
useful information not contained in standard financial variables? Our results
seem to confirm such hypothesis.
