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Deep Learning Bank Distress from News and Numerical Financial Data

Author

Listed:
  • Paola Cerchiello

    (Department of Economics and Management, University of Pavia)

  • 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)

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.

Suggested Citation

  • Paola Cerchiello & Giancarlo Nicola & Samuel Rönnqvist & Peter Sarlin, 2017. "Deep Learning Bank Distress from News and Numerical Financial Data," DEM Working Papers Series 140, University of Pavia, Department of Economics and Management.
    • Handle: RePEc:pav:demwpp:demwp0140
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    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0140.pdf
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    References listed on IDEAS

    as
    1. 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.
    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. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    4. Pekka Malo & Ankur Sinha & Pekka Korhonen & Jyrki Wallenius & Pyry Takala, 2014. "Good debt or bad debt: Detecting semantic orientations in economic texts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 782-796, April.
    Full references (including those not matched with items on IDEAS)

    Citations

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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. Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021. "Comparing minds and machines: implications for financial stability," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 479-508.
    3. Yi Cao & Xiaoquan Liu & Jia Zhai & Shan Hua, 2022. "A two‐stage Bayesian network model for corporate bankruptcy prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 455-472, January.
    4. Mary Chen & Matthew DeHaven & Isabel Kitschelt & Seung Jung Lee & Martin J. Sicilian, 2023. "Identifying Financial Crises Using Machine Learning on Textual Data," JRFM, MDPI, vol. 16(3), pages 1-28, March.
    5. Mary Chen & Matthew DeHaven & Isabel Kitschelt & Seung Jung Lee & Martin Sicilian, 2023. "Identifying Financial Crises Using Machine Learning on Textual Data," International Finance Discussion Papers 1374, Board of Governors of the Federal Reserve System (U.S.).
    6. Rastin Matin & Casper Hansen & Christian Hansen & Pia M{o}lgaard, 2018. "Predicting Distresses using Deep Learning of Text Segments in Annual Reports," Papers 1811.05270, arXiv.org.
    7. Paola Cerchiello & Giancarlo Nicola, 2018. "Assessing News Contagion in Finance," Econometrics, MDPI, vol. 6(1), pages 1-19, February.

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    More about this item

    Keywords

    behavioural finance; financial news; deep learning; bank distress; Word2vec.;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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