Deep learning bank distress from news and numerical financial data

TL;DR

This paper explores how deep learning models using news and financial data can improve bank distress prediction, demonstrating that news adds valuable information beyond traditional financial metrics.

Contribution

It introduces a deep learning approach combining doc2vec and neural networks to incorporate news data into bank distress classification, showing improved predictive performance.

Findings

News data enhances classification accuracy.

Deep learning effectively captures semantic information from news.

News provides additional useful signals beyond financial variables.

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 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.