D1 Article in a trade journal
Deep Learning for Assessing Banks’ Distress from News and Numerical Financial Data




List of Authors: Paola Cerchiello, Giancarlo Nicola, Samuel Rönnqvist, Peter Sarlin
Publisher: Elsevier
Publication year: 2018
Number in series: 18-15

Abstract

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 the issues related to text analysis and specifically to
the 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 sequence of words contained
within a text 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. Indeed, 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.


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Last updated on 2019-22-02 at 11:23