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Predicting U.S. Bank Failures with MIDAS Logit Models

Published online by Cambridge University Press:  08 October 2018

Francesco Audrino Open the ORCID record for Francesco Audrino [Opens in a new window] ,
Alexander Kostrov Open the ORCID record for Alexander Kostrov [Opens in a new window]  and
Juan-Pablo Ortega Open the ORCID record for Juan-Pablo Ortega [Opens in a new window]
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Abstract

We propose a new approach based on a generalization of the logit model to improve prediction accuracy in U.S. bank failures. Mixed-data sampling (MIDAS) is introduced in the context of a logistic regression. We also mitigate the class-imbalance problem in data and adjust the classification accuracy evaluation. In applying the suggested model to the period from 2004 to 2016, we show that it correctly classifies significantly more bank failure cases than the classic logit model, in particular for long-term forecasting horizons. Some of the largest recent bank failures in the United States that had been previously misclassified are now correctly predicted.

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Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 54 , Issue 6 , December 2019 , pp. 2575 - 2603
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2018 

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Footnotes

1

We are deeply grateful to Eric Ghysels (the referee), Lyudmila Grigoryeva, Jarrad Harford (the editor), and to the participants at the 2017 CFE-CMStatistics Conference in London, the Econometrics Colloquium (University of Konstanz), and the Economic Risk Seminar (Humboldt University of Berlin) for their helpful comments and suggestions.

References

Agarwal, V., and Taffler, R.. “Comparing the Performance of Market-Based and Accounting-Based Bankruptcy Prediction Models.” Journal of Banking and Finance, 32 (2008), 1541–1551.Google Scholar
Almon, S.“The Distributed Lag between Capital Appropriations and Expenditures.” Econometrica, 33 (1965), 178–196.Google Scholar
Andreou, E.; Ghysels, E.; and Kourtellos, A.. “Regression Models with Mixed Sampling Frequencies.” Journal of Econometrics, 158 (2010), 246–261.Google Scholar
Aubuchon, C. P., and Wheelock, D. C.. “The Geographic Distribution and Characteristics of U.S. Bank Failures, 2007–2010: Do Bank Failures Still Reflect Local Economic Conditions?” Federal Reserve Bank of St. Louis Review, 92 (2010), 395–415.Google Scholar
Audrino, F.“Forecasting Correlations during the Late-2000s Financial Crisis: The Short-Run Component, the Long-Run Component, and Structural Breaks.” Computational Statistics and Data Analysis, 76 (2014), 43–60.Google Scholar
Berger, A. N., and Bouwman, C. H. S.. “How Does Capital Affect Bank Performance during Financial Crises?” Journal of Financial Economics, 109 (2013), 146–176.Google Scholar
Betz, F.; Oprică, S.; Peltonen, T. A.; and Sarlin, P.. “Predicting Distress in European Banks.” Journal of Banking and Finance, 45 (2014), 225–241.Google Scholar
Bologna, P.“Is There a Role for Funding in Explaining Recent U.S. Banks’ Failures?” Working Paper, International Monetary Fund (2011).Google Scholar
Breitung, J., and Roling, C.. “Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach.” Journal of Forecasting, 34 (2015), 588–603.Google Scholar
Chen, X., and Ghysels, E.. “News—Good or Bad—and Its Impact on Volatility Predictions over Multiple Horizons.” Review of Financial Studies, 24 (2011), 46–81.Google Scholar
Colacito, R.; Engle, R. F.; and Ghysels, E.. “A Component Model for Dynamic Correlations.” Journal of Econometrics, 164 (2011), 45–59.Google Scholar
Cole, R. A., and White, L. J.. “Déjà Vu All Over Again: The Causes of U.S. Commercial Bank Failures This Time Around.” Journal of Financial Services Research, 42 (2012), 5–29.Google Scholar
Cole, R. A., and Wu, Q.. “Hazard versus Probit in Predicting U.S. Bank Failures: A Regulatory Perspective over Two Crises.” Available at https://ssrn.com/abstract=1460526 (2014).Google Scholar
Demirgüç-Kunt, A., and Detragiache, E.. “Monitoring Banking Sector Fragility: A Multivariate Logit Approach.” World Bank Economic Review, 14 (2000), 287–307.Google Scholar
Demyanyk, Y., and Hasan, I.. “Financial Crises and Bank Failures: A Review of Prediction Methods.” Omega, 38 (2010), 315–324.Google Scholar
DeYoung, R., and Torna, G.. “Nontraditional Banking Activities and Bank Failures during the Financial Crisis.” Journal of Financial Intermediation, 22 (2013), 397–421.Google Scholar
Engle, R. F.; Ghysels, E.; and Sohn, B.. “Stock Market Volatility and Macroeconomic Fundamentals.” Review of Economics and Statistics, 95 (2013), 776–797.Google Scholar
Ferrara, L.; Marsilli, C.; and Ortega, J.-P.. “Forecasting Growth during the Great Recession: Is Financial Volatility the Missing Ingredient?” Economic Modelling, 36 (2014), 44–50.Google Scholar
Foroni, C., and Marcellino, M.. “A Comparison of Mixed Frequency Approaches for Nowcasting Euro Area Macroeconomic Aggregates.” International Journal of Forecasting, 30 (2014), 554–568.Google Scholar
Foroni, C.; Marcellino, M.; and Schumacher, C.. “Unrestricted Mixed Data Sampling (MIDAS): MIDAS Regressions with Unrestricted Lag Polynomials.” Journal of the Royal Statistical Society, 178 (2015), 57–82.Google Scholar
Freitag, L.“Default Probabilities, CDS Premiums and Downgrades: A Probit-MIDAS Analysis.” GSBE Research Memoranda, available at https://cris.maastrichtuniversity.nl/portal/files/523024/content (2014).Google Scholar
Freitag, L.“Credit Rating Agencies and the European Sovereign Debt Crisis,” Ph.D. Thesis, Maastricht University (2016).Google Scholar
García, V.; Sánchez, J. S.; and Mollineda, R. A.. “On the Effectiveness of Preprocessing Methods When Dealing with Different Levels of Class Imbalance.” Knowledge-Based Systems, 25 (2013), 13–21.Google Scholar
Ghysels, E.; Plazzi, A.; and Valkanov, R.. “Why Invest in Emerging Markets? The Role of Conditional Return Asymmetry.” Journal of Finance, 71 (2016), 2145–2192.Google Scholar
Ghysels, E., and Qian, H.. “Estimating MIDAS Regressions via OLS with Polynomial Parameter Profiling.” Econometrics and Statistics, 9 (2019), 1–16.Google Scholar
Ghysels, E.; Santa-Clara, P.; and Valkanov, R.. “The MIDAS Touch: Mixed Data Sampling Regression Models.” Working Paper, CIRANO, available at http://www.cirano.qc.ca/files/publications/2004s-20.pdf (2004).Google Scholar
Ghysels, E.; Santa-Clara, P.; and Valkanov, R.. “There Is a Risk-Return Trade-Off After All.” Journal of Financial Economics, 76 (2005), 509–548.Google Scholar
Ghysels, E.; Sinko, A.; and Valkanov, R.. “MIDAS Regressions: Further Results and New Directions.” Econometric Reviews, 26 (2007), 53–90.Google Scholar
Gogas, P.; Papadimitriou, T.; and Agrapetidou, A.. “Forecasting Bank Failures and Stress Testing: A Machine Learning Approach.” International Journal of Forecasting, 34 (2018), 440–455.Google Scholar
Gorgi, P.; Koopman, S. J.; and Li, M.. “Forecasting Economic Time Series Using Score-Driven Dynamic Models with Mixed-Data Sampling.” International Journal of Forecasting, forthcoming (2019).Google Scholar
Guérin, P., and Marcellino, M.. “Markov-Switching MIDAS Models.” Journal of Business and Economic Statistics, 31 (2013), 45–56.Google Scholar
Hu, B. G., and Dong, W. M.. “A Study on Cost Behaviors of Binary Classification Measures in Class-Imbalanced Problems.” Available at https://arxiv.org/abs/1403.7100 (2014).Google Scholar
Iturriaga, F. J. L., and Sanz, I. P.. “Bankruptcy Visualization and Prediction Using Neural Networks: A Study of U.S. Commercial Banks.” Expert Systems with Applications, 42 (2015), 2857–2869.Google Scholar
Japkowicz, N.“Learning from Imbalanced Data Sets: A Comparison of Various Strategies.” Technical Report WS-00-05, AAAI Press (2000), 10–15.Google Scholar
Japkowicz, N., and Shah, M.. Evaluating Learning Algorithms: A Classification Perspective. New York, NY: Cambridge University Press (2011).Google Scholar
Jin, J. Y.; Kanagaretnam, K.; and Lobo, G. J.. “Ability of Accounting and Audit Quality Variables to Predict Bank Failure during the Financial Crisis.” Journal of Banking and Finance, 35 (2011), 2811–2819.Google Scholar
Jin, J. Y.; Kanagaretnam, K.; Lobo, G. J.; and Mathieu, R.. “Impact of FDICIA Internal Controls on Bank Risk Taking.” Journal of Banking and Finance, 37 (2013), 614–624.Google Scholar
Jordan, D. J.; Rice, D.; Sanchez, J.; Walker, C.; and Wort, D. H.. “Predicting Bank Failures: Evidence from 2007 to 2010.” Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1652924 (2010).Google Scholar
Karminsky, A. M., and Kostrov, A.. “The Back Side of Banking in Russia: Forecasting Bank Failures with Negative Capital.” International Journal of Computational Economics and Econometrics, 7 (2017), 170–209.Google Scholar
Kerstein, J., and Kozberg, A.. “Using Accounting Proxies of Proprietary FDIC Ratings to Predict Bank Failures and Enforcement Actions during the Recent Financial Crisis.” Journal of Accounting, Auditing, and Finance, 28 (2013), 128–151.Google Scholar
Kolari, J.; Glennon, D.; Shin, H.; and Caputo, M.. “Predicting Large U.S. Commercial Bank Failures.” Journal of Economics and Business, 54 (2002), 361–387.Google Scholar
Li, D. C.; Liu, C. W.; and Hu, S. C.. “A Learning Method for the Class Imbalance Problem with Medical Data Sets.” Computers in Biology and Medicine, 40 (2010), 509–518.Google Scholar
Lo Duca, M., and Peltonen, T. A.. “Assessing Systemic Risks and Predicting Systemic Events.” Journal of Banking and Finance, 37 (2013), 2183–2195.Google Scholar
Longadge, R.; Dongre, S. S.; and Malik, L.. “Class Imbalance Problem in Data Mining: Review.” International Journal of Computer Science and Network, 2 (2013), 83–87.Google Scholar
Lu, W., and Whidbee, D. A.. “Bank Structure and Failure during the Financial Crisis.” Journal of Financial Economic Policy, 5 (2013), 281–299.Google Scholar
Malof, M. M.; Mazurowski, M. A.; and Tourassi, G. D.. “The Effect of Class Imbalance on Case Selection for Case-Based Classifiers: An Empirical Study in the Context of Medical Decision Support.” Neural Networks, 25 (2012), 141–145.Google Scholar
Marcellino, M., and Schumacher, C.. “Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP.” Oxford Bulletin of Economics and Statistics, 72 (2010), 518–550.Google Scholar
Mayes, D. G., and Stremmel, H.. “The Effectiveness of Capital Adequacy Measures in Predicting Bank Distress.” Available at https://ssrn.com/abstract=2191861 (2013).Google Scholar
Mazurowski, M. A.; Habas, P. A.; Zurada, J. M.; Lo, J. Y.; Baker, J. A.; and Tourassi, G. D.. “Training Neural Network Classifiers for Medical Decision Making: The Effects of Imbalanced Datasets on Classification Performance.” Neural Networks, 21 (2008), 427–436.Google Scholar
Menon, A. K.; Narasimhan, H.; Agarwal, S.; and Chawla, S.. “On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance.” Proceedings of Machine Learning Research, 28 (2013), 603–611.Google Scholar
Miller, A. B.; Wall, C.; Baines, C. J.; Sun, P.; To, T.; and Narod, S. A.. “Twenty Five Year Follow-Up for Breast Cancer Incidence and Mortality of the Canadian National Breast Screening Study: Randomised Screening Trial.” BMJ, (2014), 348–g366.Google Scholar
Sarlin, P.“On Policymakers’ Loss Functions and the Evaluation of Early Warning Systems.” Economics Letters, 119 (2013), 1–7.Google Scholar
Sun, Y.; Kamel, M. S.; Wong, A. K.; and Wang, Y.. “Cost-Sensitive Boosting for Classification of Imbalanced Data.” Pattern Recognition, 40 (2007), 3358–3378.Google Scholar
Wheelock, D., and Wilson, P.. “Why Do Banks Disappear? The Determinants of U.S. Bank Failures and Acquisitions.” Review of Economics and Statistics, 8 (2000), 127–138.Google Scholar