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Detect & Describe: Deep Learning of Bank Stress in the News

Samuel Rönnqvist, Peter Sarlin, Detect & Describe: Deep Learning of Bank Stress in the News. In: 2015 IEEE Symposium Series on Computational Intelligence, 890–897, IEEE, 2015.

http://dx.doi.org/10.1109/SSCI.2015.131

Abstract:

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.

BibTeX entry:

@INPROCEEDINGS{inpRxSa15b,
  title = {Detect & Describe: Deep Learning of Bank Stress in the News},
  booktitle = {2015 IEEE Symposium Series on Computational Intelligence},
  author = {Rönnqvist, Samuel and Sarlin, Peter},
  publisher = {IEEE},
  pages = {890–897},
  year = {2015},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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