IDEAS home Printed from https://ideas.repec.org/p/fip/fedgfe/2022-77.html
   My bibliography  Save this paper

Sentiment in Bank Examination Reports and Bank Outcomes

Author

Listed:

Abstract

We investigate whether the bank examination process provides useful insight into bank future outcomes. We do this by conducting textual analysis on about 5,500 small to medium-sized commercial bank examination reports from 2004 to 2016. These confidential examination reports provide textual context to the components of supervisory ratings: capital adequacy, asset quality, management, earnings, and liquidity. Each component is given a categorical rating, and each bank is assigned an overall composite rating, which are used to determine the safety and soundness of banks. We find that, controlling for a variety of factors, including the ratings themselves, the sentiment supervisors express in describing most of the components predict relevant future bank outcomes. The sentiment conveyed in the asset quality, management, and earnings sections provides significant information in predicting future outcomes for problem loans, supervisory actions, and profitability, respectively, for all banks. Sentiment conveyed in the capital adequacy section appears to be predictive of future capital ratios for weak banks. These relationships suggest that bank supervisors play a meaningful role in the surveillance of the banking system.

Suggested Citation

  • Maureen Cowhey & Seung Jung Lee & Thomas Popeck Spiller & Cindy M. Vojtech, 2022. "Sentiment in Bank Examination Reports and Bank Outcomes," Finance and Economics Discussion Series 2022-077, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2022-77
    DOI: 10.17016/FEDS.2022.077
    as

    Download full text from publisher

    File URL: https://www.federalreserve.gov/econres/feds/files/2022077pap.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.17016/FEDS.2022.077?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Rickard Nyman & Sujit Kapadia & David Tuckett & David Gregory & Paul Ormerod & Robert Smith, 2018. "News and narratives in financial systems: exploiting big data for systemic risk assessment," Bank of England working papers 704, Bank of England.
    2. Joe Peek & Eric S. Rosengren & Geoffrey M. B. Tootell, 1999. "Is Bank Supervision Central to Central Banking?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(2), pages 629-653.
    3. 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.
    4. DeYoung, Robert, et al, 2001. "The Information Content of Bank Exam Ratings and Subordinated Debt Prices," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 33(4), pages 900-925, November.
    5. Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2020. "Making text count: economic forecasting using newspaper text," Bank of England working papers 865, Bank of England.
    6. Jordan, John S. & Peek, Joe & Rosengren, Eric S., 2000. "The Market Reaction to the Disclosure of Supervisory Actions: Implications for Bank Transparency," Journal of Financial Intermediation, Elsevier, vol. 9(3), pages 298-319, July.
    7. Berger, Allen N & Davies, Sally M & Flannery, Mark J, 2000. "Comparing Market and Supervisory Assessments of Bank Performance: Who Knows What When?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(3), pages 641-667, August.
    8. Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2022. "Making text count: Economic forecasting using newspaper text," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 896-919, August.
    9. Nyman, Rickard & Kapadia, Sujit & Tuckett, David, 2021. "News and narratives in financial systems: Exploiting big data for systemic risk assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    10. 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. Rickard Nyman & Sujit Kapadia & David Tuckett & David Gregory & Paul Ormerod & Robert Smith, 2018. "News and narratives in financial systems: exploiting big data for systemic risk assessment," Bank of England working papers 704, Bank of England.
    2. Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021. "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series 2616, European Central Bank.
    3. Nyman, Rickard & Kapadia, Sujit & Tuckett, David, 2021. "News and narratives in financial systems: Exploiting big data for systemic risk assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    4. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
    5. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    6. Pablo Pastory y Camarasa & Martien Lamers, 2023. "Do Actions Follow Words? How bank sentiment predicts credit growth," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 23/1073, Ghent University, Faculty of Economics and Business Administration.
    7. 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).
    8. Lange, Kai-Robin & Reccius, Matthias & Schmidt, Tobias & Müller, Henrik & Roos, Michael W. M. & Jentsch, Carsten, 2022. "Towards extracting collective economic narratives from texts," Ruhr Economic Papers 963, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Erik Andres-Escayola & Corinna Ghirelli & Luis Molina & Javier J. Pérez & Elena Vidal, 2022. "Using newspapers for textual indicators: which and how many?," Working Papers 2235, Banco de España.
    10. Dooruj Rambaccussing & Craig Menzies & Andrzej Kwiatkowski, 2022. "Look who’s Talking: Individual Committee members’ impact on inflation expectations," Dundee Discussion Papers in Economics 305, Economic Studies, University of Dundee.
    11. Nikoleta Anesti & Eleni Kalamara & George Kapetanios, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    12. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    13. Łukasz Baszczak, 2023. "Ekonomia narracji – początki nowego nurtu," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 1, pages 66-81.
    14. Erik Andres-Escayola & Corinna Ghirelli & Luis Molina & Javier J. Perez & Elena Vidal, 2024. "Using Newspapers for Textual Indicators: Guidance Based on Spanish- and Portuguese-Speaking Countries," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 643-692, August.
    15. Luca Barbaglia & Sergio Consoli & Sebastiano Manzan, 2024. "Forecasting GDP in Europe with textual data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 338-355, March.
    16. Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    17. Yuting Chen & Don Bredin & Valerio Potì & Roman Matkovskyy, 2022. "COVID risk narratives: a computational linguistic approach to the econometric identification of narrative risk during a pandemic," Digital Finance, Springer, vol. 4(1), pages 17-61, March.
    18. Youngjoon Lee & Soohyon Kim & Ki Young Park, 2018. "Deciphering Monetary Policy Committee Minutes with Text Mining Approach: A Case of South Korea," Working papers 2018rwp-132, Yonsei University, Yonsei Economics Research Institute.
    19. Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero, 2023. "The power of text-based indicators in forecasting Italian economic activity," International Journal of Forecasting, Elsevier, vol. 39(2), pages 791-808.
    20. Young Joon Lee & Soohyon Kim & Ki Young Park, 2019. "Deciphering Monetary Policy Board Minutes with Text Mining: The Case of South Korea," Korean Economic Review, Korean Economic Association, vol. 35, pages 471-511.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

    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:fip:fedgfe:2022-77. 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: Ryan Wolfslayer ; Keisha Fournillier (email available below). General contact details of provider: https://edirc.repec.org/data/frbgvus.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.