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Sentiment analysis of the Spanish financial stability Report

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  • Moreno Bernal, Ángel Iván
  • Pedraz, Carlos González

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

  • Moreno Bernal, Ángel Iván & Pedraz, Carlos González, 2024. "Sentiment analysis of the Spanish financial stability Report," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 913-939.
    • Handle: RePEc:eee:reveco:v:89:y:2024:i:pb:p:913-939
    DOI: 10.1016/j.iref.2023.10.037
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    References listed on IDEAS

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    1. Nicholas Apergis & Ioannis Pragidis, 2019. "Stock Price Reactions to Wire News from the European Central Bank: Evidence from Changes in the Sentiment Tone and International Market Indexes," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 25(1), pages 91-112, February.
    2. 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.
    3. Benjamin Born & Michael Ehrmann & Marcel Fratzscher, 2014. "Central Bank Communication on Financial Stability," Economic Journal, Royal Economic Society, vol. 124(577), pages 701-734, June.
    4. 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.
    5. 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.
    6. Ricardo Correa & Keshav Garud & Juan M. Londono & Nathan Mislang, 2017. "Constructing a Dictionary for Financial Stability," IFDP Notes 2017-06-28, Board of Governors of the Federal Reserve System (U.S.).
    Full references (including those not matched with items on IDEAS)

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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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