IDEAS home Printed from https://ideas.repec.org/a/pep/journl/v1y1991i2p125-140.html
   My bibliography  Save this article

Predicting Small Bank Failure

Author

Listed:
  • Wilton E. Heyliger

    (Morris Brown College)

  • Don P. Holdren

    (Marshall University)

Abstract

There are many studies of bank performance and bank failure in the literature. Most of these studies used banking ratios as variables in their models without giving consideration to their appropriateness, nor was much consideration given to the stability of those ratios through time and across asset size. Many studies also failed to recognize that bank structure may differ by asset size. This study evaluates a large number of banking variables in order to identify stable ratios. These ratios are then used in disaggregated logistic models to predict bank failure. The study finds that the disaggregated models with stable variables were better predictors of bank failure than aggregated models used in earlier studies.

Suggested Citation

  • Wilton E. Heyliger & Don P. Holdren, 1991. "Predicting Small Bank Failure," Journal of Entrepreneurial Finance, Pepperdine University, Graziadio School of Business and Management, vol. 1(2), pages 125-140, Winter.
  • Handle: RePEc:pep:journl:v:1:y:1991:i:2:p:125-140
    as

    Download full text from publisher

    File URL: http://jefsite.org/RePEc/pep/journl/jef-1991-01-2-c-heyliger.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Larry D. Wall, 1987. "F.Y.I. commercial bank profitability: some disturbing trends," Economic Review, Federal Reserve Bank of Atlanta, issue Mar, pages 24-36.
    2. Benston, George J & Hanweck, Gerald A & Humphrey, David B, 1982. "Scale Economies in Banking: A Restructuring and Reassessment," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 14(4), pages 435-456, November.
    3. Dambolena, Ismael G & Khoury, Sarkis J, 1980. "Ratio Stability and Corporate Failure," Journal of Finance, American Finance Association, vol. 35(4), pages 1017-1026, September.
    4. Lynn A. Nejezchleb, 1986. "Declining profitability at small commercial banks: a temporary development or a secular trend?," Proceedings 134, Federal Reserve Bank of Chicago.
    5. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    6. West, Robert Craig, 1985. "A factor-analytic approach to bank condition," Journal of Banking & Finance, Elsevier, vol. 9(2), pages 253-266, June.
    7. Meyer, Paul A & Pifer, Howard W, 1970. "Prediction of Bank Failures," Journal of Finance, American Finance Association, vol. 25(4), pages 853-868, September.
    8. Libby, R, 1975. "Accounting Ratios And Prediction Of Failure - Some Behavioral Evidence," Journal of Accounting Research, Wiley Blackwell, vol. 13(1), pages 150-161.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stephen M. Miller & Athanasios Noulas, 1995. "Explaining Recent Connecticut Bank Failures," Working papers 1995-01, University of Connecticut, Department of Economics.

    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. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    2. Fen-May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(7), pages 650-662, July.
    3. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    4. Jan Hanousek & Gerard Roland, 2001. "Banking Passivity and Regulatory Failure in Emerging Markets: Theory and Evidence from the Czech Republic," CERGE-EI Working Papers wp192, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    5. Fiordelisi, Franco & Mare, Davide Salvatore, 2013. "Probability of default and efficiency in cooperative banking," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 30-45.
    6. Mare, Davide Salvatore, 2015. "Contribution of macroeconomic factors to the prediction of small bank failures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 25-39.
    7. Douglas, Ella & Lont, David & Scott, Tom, 2014. "Finance company failure in New Zealand during 2006–2009: Predictable failures?," Journal of Contemporary Accounting and Economics, Elsevier, vol. 10(3), pages 277-295.
    8. Daley, J. & Matthews, K. & Whitfield, K., 2008. "Too-big-to-fail: Bank failure and banking policy in Jamaica," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 290-303, July.
    9. Aykut Ekinci & Halil İbrahim Erdal, 2017. "Forecasting Bank Failure: Base Learners, Ensembles and Hybrid Ensembles," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 677-686, April.
    10. Thomas B. King & Daniel A. Nuxoll & Timothy J. Yeager, 2006. "Are the causes of bank distress changing? can researchers keep up?," Review, Federal Reserve Bank of St. Louis, vol. 88(Jan), pages 57-80.
    11. Halil Erdal & Aykut Ekinci, 2013. "A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 199-215, August.
    12. Kolari, James & Glennon, Dennis & Shin, Hwan & Caputo, Michele, 2002. "Predicting large US commercial bank failures," Journal of Economics and Business, Elsevier, vol. 54(4), pages 361-387.
    13. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    14. Andrew Logan, 2001. "The United Kingdom's small banks' crisis of the early 1990s: what were the leading indicators of failure?," Bank of England working papers 139, Bank of England.
    15. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    16. Cakir, Murat, 2005. "Firma Başarısızlığının Dinamiklerinin Belirlenmesinde Makina Öğrenmesi Teknikleri: Ampirik Uygulamalar ve Karşılaştırmalı Analiz [Machine Learning Techniques in Determining the Dynamics of Corporat," MPRA Paper 55975, University Library of Munich, Germany.
    17. Paola Bongini & Stijn Claessens & Giovanni Ferri, 2001. "The Political Economy of Distress in East Asian Financial Institutions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 19(1), pages 5-25, February.
    18. Allen N. Berger & Björn Imbierowicz & Christian Rauch, 2016. "The Roles of Corporate Governance in Bank Failures during the Recent Financial Crisis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 729-770, June.
    19. Carlos Serrano-Cinca & Yolanda Fuertes-Call鮠 & Bego uti鲲ez-Nieto & Beatriz Cuellar-Fernᮤez, 2014. "Path modelling to bankruptcy: causes and symptoms of the banking crisis," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3798-3811, November.
    20. repec:zbw:bofrdp:2009_035 is not listed on IDEAS
    21. Demyanyk, Yuliya & Hasan, Iftekhar, 2009. "Financial crises and bank failures: a review of prediction methods," Bank of Finland Research Discussion Papers 35/2009, Bank of Finland.

    More about this item

    Keywords

    Bank; Small Bank; Failure;
    All these keywords.

    JEL classification:

    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    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:pep:journl:v:1:y:1991:i:2:p:125-140. 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: Craig Everett (email available below). General contact details of provider: https://edirc.repec.org/data/bapepus.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.