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Detect & Describe: Deep learning of bank stress in the news

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  • Samuel Ronnqvist
  • Peter Sarlin

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.

Suggested Citation

  • Samuel Ronnqvist & Peter Sarlin, 2015. "Detect & Describe: Deep learning of bank stress in the news," Papers 1507.07870, arXiv.org.
    • Handle: RePEc:arx:papers:1507.07870
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    References listed on IDEAS

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    1. Gropp, Reint & Vesala, Jukka & Vulpes, Giuseppe, 2006. "Equity and Bond Market Signals as Leading Indicators of Bank Fragility," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(2), pages 399-428, March.
    2. Sarlin, Peter, 2013. "On policymakers’ loss functions and the evaluation of early warning systems," Economics Letters, Elsevier, vol. 119(1), pages 1-7.
    3. Samuel Ronnqvist & Peter Sarlin, 2014. "Bank Networks from Text: Interrelations, Centrality and Determinants," Papers 1406.7752, arXiv.org, revised Jul 2015.
    4. Peltonen, Tuomas A. & Sarlin, Peter & Piloiu, Andreea, 2015. "Network linkages to predict bank distress," Working Paper Series 1828, European Central Bank.
    5. Milne, Alistair, 2014. "Distance to default and the financial crisis," Journal of Financial Stability, Elsevier, vol. 12(C), pages 26-36.
    6. Betz, Frank & Oprică, Silviu & Peltonen, Tuomas A. & Sarlin, Peter, 2014. "Predicting distress in European banks," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 225-241.
    7. Männasoo, Kadri & Mayes, David G., 2009. "Explaining bank distress in Eastern European transition economies," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 244-253, February.
    8. Rebel Cole & Jeffery Gunther, 1998. "Predicting Bank Failures: A Comparison of On- and Off-Site Monitoring Systems," Journal of Financial Services Research, Springer;Western Finance Association, vol. 13(2), pages 103-117, April.
    9. Samuel R�nnqvist & Peter Sarlin, 2015. "Bank networks from text: interrelations, centrality and determinants," Quantitative Finance, Taylor & Francis Journals, vol. 15(10), pages 1619-1635, October.
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    Cited by:

    1. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    2. Oet, Mikhail V. & Gramlich, Dieter & Sarlin, Peter, 2016. "Evaluating measures of adverse financial conditions," Journal of Financial Stability, Elsevier, vol. 27(C), pages 234-249.
    3. Agarwal, Arvind & Gupta, Aparna & Kumar, Arun & Tamilselvam, Srikanth G., 2019. "Learning risk culture of banks using news analytics," European Journal of Operational Research, Elsevier, vol. 277(2), pages 770-783.
    4. Samuel Ronnqvist & Peter Sarlin, 2016. "Bank distress in the news: Describing events through deep learning," Papers 1603.05670, arXiv.org, revised Dec 2016.

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