IDEAS home Printed from https://ideas.repec.org/p/een/camaaa/2009-16.html
   My bibliography  Save this paper

Statistical opacity in the US banking sector

Author

Listed:
  • Guo Li
  • Lee Sanning
  • Sherrill Shaffer

Abstract

Motivated by the observation that very few banks fail in normal years, we explore the impact of that pattern on the precision of a standard statistical failure model, and discuss implications for regulation and risk management. Out-of-sample forecasting is found to be worse for a model fitted to recent data with few failures than for a model fitted to much older data with more failures. This property may mask observable drift in risk linkages until aggregate risk levels have risen high enough to trigger new failures, thus suggesting an informational basis for the puzzling recurrence of bank crises.

Suggested Citation

  • Guo Li & Lee Sanning & Sherrill Shaffer, 2009. "Statistical opacity in the US banking sector," CAMA Working Papers 2009-16, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2009-16
    as

    Download full text from publisher

    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2017-02/16_li_sanning_shaffer_2009_revised_080909.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cole, Rebel A. & Gunther, Jeffery W., 1995. "Separating the likelihood and timing of bank failure," Journal of Banking & Finance, Elsevier, vol. 19(6), pages 1073-1089, September.
    2. Wheelock, David C & Wilson, Paul W, 1995. "Explaining Bank Failures: Deposit Insurance, Regulation, and Efficiency," The Review of Economics and Statistics, MIT Press, vol. 77(4), pages 689-700, November.
    3. Flannery, Mark J. & Kwan, Simon H. & Nimalendran, M., 2004. "Market evidence on the opaqueness of banking firms' assets," Journal of Financial Economics, Elsevier, vol. 71(3), pages 419-460, March.
    4. Hirtle, Beverly, 2006. "Stock Market Reaction to Financial Statement Certification by Bank Holding Company CEOs," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1263-1291, August.
    5. DeYoung, Robert, 2003. "The failure of new entrants in commercial banking markets: a split-population duration analysis," Review of Financial Economics, Elsevier, vol. 12(1), pages 7-33.
    6. 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.
    7. David C. Wheelock & Paul W. Wilson, 2000. "Why do Banks Disappear? The Determinants of U.S. Bank Failures and Acquisitions," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 127-138, February.
    8. Arena, Marco, 2008. "Bank failures and bank fundamentals: A comparative analysis of Latin America and East Asia during the nineties using bank-level data," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 299-310, February.
    9. DeYoung, Robert & Hasan, Iftekhar, 1998. "The performance of de novo commercial banks: A profit efficiency approach," Journal of Banking & Finance, Elsevier, vol. 22(5), pages 565-587, May.
    10. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    11. Rebel Cole & Jeffery Gunther, 1998. "Predicting Bank Failures: A Comparison of On- and Off-Site Monitoring Systems," Journal of Financial Services Research, Springer;Western Finance Association, vol. 13(2), pages 103-117, April.
    12. Rebel A. Cole & Jeffery W. Gunther, 1995. "FIMS: a new monitoring system for banking institutions," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), issue Jan, pages 1-15.
    13. Gary Whalen, 1991. "A proportional hazards model of bank failure: an examination of its usefulness as an early warning tool," Economic Review, Federal Reserve Bank of Cleveland, vol. 27(Q I), pages 21-31.
    14. Edwards, Franklin R, 1977. "Managerial Objectives in Regulated Industries: Expense-Preference Behavior in Banking," Journal of Political Economy, University of Chicago Press, vol. 85(1), pages 147-162, February.
    15. Meyer, Paul A & Pifer, Howard W, 1970. "Prediction of Bank Failures," Journal of Finance, American Finance Association, vol. 25(4), pages 853-868, September.
    16. Giuliano Iannotta, 2006. "Testing for Opaqueness in the European Banking Industry: Evidence from Bond Credit Ratings," Journal of Financial Services Research, Springer;Western Finance Association, vol. 30(3), pages 287-309, December.
    17. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    18. Slovin, Myron B. & Sushka, Marie E. & Polonchek, John A., 1992. "Informational externalities of seasoned equity issues : Differences between banks and industrial firms," Journal of Financial Economics, Elsevier, vol. 32(1), pages 87-101, August.
    19. Julapa Jagtiani & James Kolari & Catharine Lemieux & G. Hwan Shin, 2003. "Early warning models for bank supervision: Simpler could be better," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 27(Q III), pages 49-60.
    20. James Kolari & Michele Caputo & Drew Wagner, 1996. "Trait Recognition: An Alternative Approach to Early Warning Systems in Commercial Banking," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 23(9-10), pages 1415-1434, December.
    21. Donald P. Morgan, 2002. "Rating Banks: Risk and Uncertainty in an Opaque Industry," American Economic Review, American Economic Association, vol. 92(4), pages 874-888, September.
    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. Gerhard Hambusch & Sherrill Shaffer, 2012. "Forecasting Bank Leverage," Working Paper Series 176, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    2. Papanikolaou, Nikolaos I., 2018. "To be bailed out or to be left to fail? A dynamic competing risks hazard analysis," Journal of Financial Stability, Elsevier, vol. 34(C), pages 61-85.
    3. Gerhard Hambusch & Sherrill Shaffer, 2016. "Forecasting bank leverage: an alternative to regulatory early warning models," Journal of Regulatory Economics, Springer, vol. 50(1), pages 38-69, August.
    4. Pavlos Almanidis & Robin C. Sickles, 2016. "Banking Crises, Early Warning Models, and Efficiency," International Series in Operations Research & Management Science, in: Juan Aparicio & C. A. Knox Lovell & Jesus T. Pastor (ed.), Advances in Efficiency and Productivity, chapter 0, pages 331-364, Springer.
    5. Cole, Rebel A. & Wu, Qiongbing, 2009. "Is hazard or probit more accurate in predicting financial distress? Evidence from U.S. bank failures," MPRA Paper 24688, University Library of Munich, Germany, revised 01 Aug 2010.
    6. Fabrizio Ferriani & Wanda Cornacchia & Paolo Farroni & Eliana Ferrara & Francesco Guarino & Francesco Pisanti, 2019. "An early warning system for less significant Italian banks," Questioni di Economia e Finanza (Occasional Papers) 480, Bank of Italy, Economic Research and International Relations Area.
    7. 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.
    8. Suss, Joel & Treitel, Henry, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.
    9. Sahut, Jean-Michel & Mili, Mehdi, 2011. "Banking distress in MENA countries and the role of mergers as a strategic policy to resolve distress," Economic Modelling, Elsevier, vol. 28(1-2), pages 138-146, January.
    10. Koresh Galil & Margalit Samuel & Offer Moshe Shapir & Wolf Wagner, 2023. "Bailouts and the modeling of bank distress," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(1), pages 7-30, February.
    11. Qiongbing Wu & Rebel A. Cole, 2024. "Macroeconomic conditions and bank failure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1212-1234, August.
    12. Liu, Wei & Kolari, James W. & Kyle Tippens, T. & Fraser, Donald R., 2013. "Did capital infusions enhance bank recovery from the great recession?," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5048-5061.
    13. repec:erf:erfstu:78 is not listed on IDEAS
    14. Gogas, Periklis & Papadimitriou, Theophilos & Agrapetidou, Anna, 2018. "Forecasting bank failures and stress testing: A machine learning approach," International Journal of Forecasting, Elsevier, vol. 34(3), pages 440-455.
    15. Francis, William, 2014. "UK deposit-taker responses to the financial crisis: what are the lessons?," Bank of England working papers 501, Bank of England.
    16. Peresetsky, A. A., 2011. "What factors drive the Russian banks license withdrawal," MPRA Paper 41507, University Library of Munich, Germany.
    17. Maghyereh, Aktham I. & Awartani, Basel, 2014. "Bank distress prediction: Empirical evidence from the Gulf Cooperation Council countries," Research in International Business and Finance, Elsevier, vol. 30(C), pages 126-147.
    18. Cullen F. Goenner, 2020. "Uncertain times and early predictions of bank failure," The Financial Review, Eastern Finance Association, vol. 55(4), pages 583-601, November.
    19. Fungáčová, Zuzana & Turk-Ariss, Rima & Weill, Laurent, 2013. "Does excessive liquidity creation trigger bank failures?," BOFIT Discussion Papers 2/2013, Bank of Finland Institute for Emerging Economies (BOFIT).
    20. Kimmel, Randall K. & Thornton, John H. & Bennett, Sara E., 2016. "Can statistics-based early warning systems detect problem banks before markets?," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 190-216.
    21. Fungáčová, Zuzana & Turk-Ariss, Rima & Weill, Laurent, 2013. "Does excessive liquidity creation trigger bank failures?," BOFIT Discussion Papers 2/2013, Bank of Finland, Institute for Economies in Transition.

    More about this item

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:een:camaaa:2009-16. 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: Cama Admin (email available below). General contact details of provider: https://edirc.repec.org/data/asanuau.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.