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Sentiment analysis of the Spanish Financial Stability Report

Author

Listed:
  • Ángel Iván Moreno Bernal

    (Banco de España)

  • Carlos González Pedraz

    (Banco de España)

Abstract

This paper presents a text mining application, to extract information from financial texts and use this information to create sentiment indices. In particular, the analysis focuses on the Banco de España’s Financial Stability Reports from 2002 to 2019 in their Spanish version and on the press reaction to these reports. To calculate the indices, a Spanish dictionary of words with a positive, negative or neutral connotation has been created, to the best of our knowledge the first within the context of financial stability. The robustness of the indices is analysed by applying them to different sections of the Report, and using different variations of the dictionary and the definition of the index. Finally, sentiment is also measured for press reports in the days following the publication of the Report. The results show that the list of words collected in the reference dictionary represents a robust sample to estimate the sentiment of these texts. This tool constitutes a valuable methodology to analyse the repercussion of financial stability reports, while objectively quantifying the sentiment conveyed in them.

Suggested Citation

  • Ángel Iván Moreno Bernal & Carlos González Pedraz, 2020. "Sentiment analysis of the Spanish Financial Stability Report," Working Papers 2011, Banco de España.
    • Handle: RePEc:bde:wpaper:2011e
    as

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    References listed on IDEAS

    as
    1. Jegadeesh, Narasimhan & Wu, Di, 2013. "Word power: A new approach for content analysis," Journal of Financial Economics, Elsevier, vol. 110(3), pages 712-729.
    2. Paul Mielke & Kenneth Berry & Janis Johnston, 2011. "Robustness without rank order statistics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(1), pages 207-214.
    3. Kearney, Colm & Liu, Sha, 2014. "Textual sentiment in finance: A survey of methods and models," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 171-185.
    4. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Fernandez, Raul & Palma Guizar, Brenda & Rho, Caterina, 2021. "A sentiment-based risk indicator for the Mexican financial sector," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(3).

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    More about this item

    Keywords

    text mining; sentiment analysis; natural language processing; central bank communications; financial stability;
    All these keywords.

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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