IDEAS home Printed from https://ideas.repec.org/p/bde/wpaper/2124.html
   My bibliography  Save this paper

The impact of COVID-19 on analysts’ sentiment about the banking sector

Author

Listed:
  • Alicia Aguilar

    (Banco de España)

  • Diego Torres

    (Banco de España)

Abstract

The use of quantitative tools to analyse the huge amount of qualitative information has been acquiring increasing importance. Market participants and, of course, Central Banks have been involved in this trend. The vast majority of qualitative data can be qualified as non-structured and refers mainly to news, reports or another kind of texts. Its transformation into structured data can improve the availability of information and hence, decision making. This article applies sentiment analysis tools to text data in order to quantify the impact of Covid-19 on the analysts’ opinions. Using this methodology, it is possible to transform qualitative non-structured data into a quantitative index that can be used to compare reports from different periods and countries. The results show the pandemic worsens banking sentiment in Europe, which coincides with higher uncertainty in the stock market. There are also regional differences in the decline in sentiment as well as higher divergence is observed across opinions.

Suggested Citation

  • Alicia Aguilar & Diego Torres, 2021. "The impact of COVID-19 on analysts’ sentiment about the banking sector," Working Papers 2124, Banco de España, revised Jun 2021.
    • Handle: RePEc:bde:wpaper:2124
    as

    Download full text from publisher

    File URL: https://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/21/Files/dt2124e.pdf
    File Function: Updated version, July 2021
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Feng, 2008. "Annual report readability, current earnings, and earnings persistence," Journal of Accounting and Economics, Elsevier, vol. 45(2-3), pages 221-247, August.
    2. Price, S. McKay & Doran, James S. & Peterson, David R. & Bliss, Barbara A., 2012. "Earnings conference calls and stock returns: The incremental informativeness of textual tone," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 992-1011.
    3. 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.
    4. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Beattie, Vivien, 2014. "Accounting narratives and the narrative turn in accounting research: Issues, theory, methodology, methods and a research framework," The British Accounting Review, Elsevier, vol. 46(2), pages 111-134.
    2. An, Suwei, 2023. "Essays on incentive contracts, M&As, and firm risk," Other publications TiSEM dd97d2f5-1c9d-47c5-ba62-f, Tilburg University, School of Economics and Management.
    3. Renato Camodeca & Alex Almici & Umberto Sagliaschi, 2018. "Sustainability Disclosure in Integrated Reporting: Does It Matter to Investors? A Cheap Talk Approach," Sustainability, MDPI, vol. 10(12), pages 1-34, November.
    4. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    5. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    6. Bian, Shibo & Jia, Dekui & Li, Ruihai & Sun, Wujun & Yan, Zhipeng & Zheng, Yingfei, 2021. "Can management tone predict IPO performance? – Evidence from mandatory online roadshows in China," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    7. Wanli Li & Tiantian Yan & Yue Li & Ziqiao Yan, 2023. "Earnings management and CSR report tone: Evidence from China," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 30(4), pages 1883-1902, July.
    8. Kristian D. Allee & Matthew D. Deangelis, 2015. "The Structure of Voluntary Disclosure Narratives: Evidence from Tone Dispersion," Journal of Accounting Research, Wiley Blackwell, vol. 53(2), pages 241-274, May.
    9. Frankel, Richard & Jennings, Jared & Lee, Joshua, 2016. "Using unstructured and qualitative disclosures to explain accruals," Journal of Accounting and Economics, Elsevier, vol. 62(2), pages 209-227.
    10. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    11. Ardia, David & Bluteau, Keven & Boudt, Kris, 2022. "Media abnormal tone, earnings announcements, and the stock market," Journal of Financial Markets, Elsevier, vol. 61(C).
    12. Rahman, Sheehan, 2023. "Narrative tone and earnings persistence," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 52(C).
    13. Elshandidy, Tamer & Kamel, Hany, 2024. "Tone of narrative disclosures and earnings management: UK evidence," Advances in accounting, Elsevier, vol. 64(C).
    14. Richard Frankel & Jared Jennings & Joshua Lee, 2022. "Disclosure Sentiment: Machine Learning vs. Dictionary Methods," Management Science, INFORMS, vol. 68(7), pages 5514-5532, July.
    15. Blau, Benjamin M. & DeLisle, Jared R. & Price, S. McKay, 2015. "Do sophisticated investors interpret earnings conference call tone differently than investors at large? Evidence from short sales," Journal of Corporate Finance, Elsevier, vol. 31(C), pages 203-219.
    16. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    17. Kim, Jongkyum & Lim, Jee-Hae & Yoon, Kyunghee, 2022. "How do the content, format, and tone of Twitter-based corporate disclosure vary depending on earnings performance?," International Journal of Accounting Information Systems, Elsevier, vol. 47(C).
    18. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    19. Doshi, Hitesh & Patel, Saurin & Ramani, Srikanth & Sooy, Matthew, 2023. "Uncertain tone, asset volatility and credit default swap spreads," Journal of Contemporary Accounting and Economics, Elsevier, vol. 19(3).
    20. James P. Ryans, 2021. "Textual classification of SEC comment letters," Review of Accounting Studies, Springer, vol. 26(1), pages 37-80, March.

    More about this item

    Keywords

    Sentiment analysis; COVID-19 impact; European banking; analysts’ estimates;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bde:wpaper:2124. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ángel Rodríguez. Electronic Dissemination of Information Unit. Research Department. Banco de España (email available below). General contact details of provider: https://edirc.repec.org/data/bdegves.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.