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

By Samuel Rönnqvist, Peter Sarlin. 2015

12 citations recorded by the source corpus.

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.

Subfields: Accounting, Finance, Management Science and Operations Research

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Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, methodology): “In [96], the news semantics were extracted by the word sequence learning, bank stress was determined and classified with the associated events.”
Bank distress in the news: Describing events through deep learning (2016, methodology): “Rönnqvist and Sarlin [31] explore this by similarly training a predictive model on document vectors and successfully applying it on word vectors, to weight the relevance individual words within the context.”