Deep Learning Bank Distress from News and Numerical Financial Data

Paola Cerchiello (), Giancarlo Nicola (), Samuel Rönnqvist () and Peter Sarlin ()
Additional contact information
Giancarlo Nicola: Department of Economics and Management, University of Pavia
Samuel Rönnqvist: Turku Centre for Computer Science - TUCS, Åbo Akademi University
Peter Sarlin: Hanken School of Economics, RiskLab Finland

No 140, DEM Working Papers Series from University of Pavia, Department of Economics and Management

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.

Keywords: behavioural finance; financial news; deep learning; bank distress; Word2vec. (search for similar items in EconPapers)
JEL-codes: C12 C83 E58 E61 G02 G14 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2017-05
New Economics Papers: this item is included in nep-ban, nep-cmp, nep-fmk and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0140.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pav:demwpp:demwp0140

Access Statistics for this paper

More papers in DEM Working Papers Series from University of Pavia, Department of Economics and Management Contact information at EDIRC.
Bibliographic data for series maintained by Alice Albonico ( this e-mail address is bad, please contact ).