Bank distress in the news: Describing events through deep learning

Samuel Rönnqvist*, Peter Sarlin

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

42 Citations (Scopus)


While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting relevant discussion in text and extracting natural language descriptions of events. Supervised by only a small set of event information, comprising entity names and dates, the model is leveraged by unsupervised learning of semantic vector representations on extensive text data. We demonstrate applicability to the study of financial risk based on news (6.6M articles), particularly bank distress and government interventions (243 events), where indices can signal the level of bank-stress-related reporting at the entity level, or aggregated at national or European level, while being coupled with explanations. Thus, we exemplify how text, as timely, widely available and descriptive data, can serve as a useful complementary source of information for financial and systemic risk analytics.

Original languageEnglish
Peer-reviewed scientific journalNeurocomputing
Pages (from-to)57-70
Number of pages14
Publication statusPublished - 15.11.2017
MoE publication typeA1 Journal article - refereed


  • 512 Business and Management
  • Bank distress
  • Distributional semantics
  • Event detection
  • Financial risk
  • Neural networks
  • Text mining


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