Is hazard or probit more accurate in predicting financial distress? Evidence from U.S. bank failures

Cole, Rebel A. and Wu, Qiongbing (2009): Is hazard or probit more accurate in predicting financial distress? Evidence from U.S. bank failures.

Abstract

We compare the out-of-sample forecasting accuracy of the time-varying hazard model developed by Shumway (2001) and the one-period probit model used by Cole and Gunther (1998). Using data on U.S. bank failures from 1985 – 1992, we find that, from an econometric perspective, the hazard model is more accurate than the probit model in predicting bank failures, but this improvement in accuracy results from incorporating more recent information in the hazard, but not the probit, model. When we limit both models to the same information set, we find that the one-period probit model is slightly more accurate than the time-varying hazard model. We also find that a parsimonious specification of the one-period probit model fit to data from the 1980s performs surprisingly well in forecasting bank failures during 2009 – 2010.

• Item Type: MPRA Paper
• Language: English
• Keywords: bank; bank failure; failure prediction; financial crisis; forecasting; hazard model; probit model; static model; time-varying covariates
• Subjects: G - Financial Economics > G1 - General Financial Markets > G18 - Government Policy and Regulation; G - Financial Economics > G2 - Financial Institutions and Services > G28 - Government Policy and Regulation; G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks ; Depository Institutions ; Micro Finance Institutions ; Mortgages

References

Agarwal, V., and Taffler, R., 2008. Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance 32, 1541-1551.

Altman, E.I., 1968. Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. The Journal of Finance 23, 589-609.

Altman, E.I., 1993. Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting and Avoiding Distress and Profiting from Bankruptcy. New York: Wiley.

Arena, M., 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 and Finance 32, 299-310.

Beaver, W.H., McNichols, M.F., and Rhie, J., 2005. Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies 10, 93-122.

Bharath, S.T., and Shumway, T., 2008. Forecasting default with the Merton distance to default model. The Review of Financial Studies 21, 1339-1369.

Bonfim, D., 2009. Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics. Journal of Banking and Finance 33, 281-299.

Bovenzi, J., Marino, J., and McFadden, F., 1983. Commercial bank failure prediction models. Economic Review, Federal Reserve Bank of Atlanta (November), 27-34.

Brown, C., and Dinc, S., 2005. The politics of bank failures: Evidence from emerging markets. Quarterly Journal of Economic 120, 1413-1444.

Brown, C., and Dinc, S., 2010. Too many to fail? Evidence of regulatory forbearance when the banking sector is weak. Review of Financial Studies, forthcoming.

Campbell, J., Hilscher, J., and Szilagyi, J., 2008. In search of distress risk. The Journal of Finance 58(6), 2899-2939.

Carling, K., Jacobson, T., Linde, J., and Roszbach, K., 2007. Corporate credit risk modeling and the macroeconomy. Journal of Banking and Finance 31, 845-868.

Chava, S., and Jarrow, R., 2004. Bankruptcy prediction with industry effects. Review of Finance 8, 609-641.

Cole, R., Cornyn, B. and Gunther, J. 1995. FIMS: A new monitoring system for banking institutions. Federal Reserve Bulletin 81, 1-15.

Cole, R. A., and Gunther, J. W., 1995. Separating the likelihood and timing of bank failure. Journal of Banking and Finance 19, 1073-1089.

Cole, R., and Gunther, J., 1998. Predicting bank failures: A comparison of on- and off-site monitoring systems. Journal of Financial Services Research 13(2), 103-117.

Cole, R., Moshirian, F., and Wu, Q., 2008. Bank stock returns and economic growth. Journal of Banking and Finance 32, 995-1007.

Cox, D. R., 1972. Regression models and life-table (with discussion). Journal of Royal Statistical Society 34B, 187-220.

Cox, D. R., 1975. Partial likelihood. Biometrika 62, 269-276.

Cox, D.R., and Oakes, D., 1984. Analysis of Survival Data. New York: Chapman & Hall.

Curry, T., Elmer, P., and Fissel, G., 2007. Equity market data, bank failures and market efficiency. Journal of Economics and Business 59, 536-559.

Demirguc-Kunt, A., 1989. Modeling large commercial-bank failures: a simultaneous-equations approach. Working paper 8905, Federal Reserve Bank of Cleveland.

Efron, B., 1977. The efficiency of Cox’s likelihood function for censored data. Journal of American Statistical Association 72, 557-565.

Gajewski, G., 1989. Assessing the risk of bank failure.” Proceedings of a Conference on Bank Structure and Competition, Federal Reserve Bank of Chicago, 432-456.

Hoggarth, G., Reis, R., and Saporta, V., 2002. Costs of banking system instability: some empirical evidence. Journal of Banking and Finance 26, 825-855.

Jagtiani, J., and Lemieux, C., 2001. Market discipline prior to bank failure. Journal of Economics and Business 53, 313-324.

King, T., Nuxoll, D., and Yeager, T., 2006. Are the causes of bank distress changing? Can researchers keep up? Economic Review. Federal Reserve Bank of St. Louis 88, 57-870.

Lane, W., Looney, S. and Wansley, J., 1986. An application of the Cox proportional hazards model to bank failure. Journal of Banking and Finance 10, 511-531.

Levine, R., 2005. Finance and growth: Theory and evidence. In: Handbook of Economic Growth. Philippe Aghion and Steven Durlauf, eds. North-Holland, Amsterdam, 865-934.

Mannasoo, K., and Mayes, D., 2009. Explaining bank distress in Eastern European transition economies. Journal of Banking and Finance 33, 244-253.

Martin, D., 1977. Early warning of bank failure: a logit regression approach. Journal of Banking and Finance 1, 249-276.

Meyer, P. A., and Pifer, H. W., 1970. Prediction of banking failure. The Journal of Finance 25, 853-568.

Molina, C. A., 2002. Predicting bank failures using a hazard model: the Venezuelan banking crisis. Emerging Market Review 3, 31-50.

Nam, C., Kim, T., Park, N., and Lee, H., 2008. Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies. Journal of Forecasting 27, 493-506.

Pesaran, M. H., Schuermann, T., Treutler, B., and Weiner, S. T., 2006. Macroeconomic dynamics and credit risk: A global perspective. Journal of Money, Credit, and Banking 38, 1211-1261.

Pettway, R. H., 1980. Potential insolvency, market efficiency, and bank regulation of large commercial banks. Journal of Financial and Quantitative Analysis 15(1), 219-236.

Pettway, R., and Sinkey, J., 1980. Establishing on-site bank examination priorities: An early warning system using accounting and market information. The Journal of Finance 35, 137-150.

Shumway, T., 2001. Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business 74, 101-124.

Thomson, J., 1992. Modeling the regulator’s closure option: A two-step logit regression approach. Journal of Financial Services Research 6, 5-23.

Whalen, G., 1991. A proportional hazards model of bank failure: An examination of its usefulness as an early warning model tool. Economic Review, Federal Reserve Bank of Cleveland, 21-31.

Wheelock, D., and Wilson, P. 1995. Explaining bank failures: Deposit insurance, regulation and efficiency. Review of Economics and Statistics 77, 689-700.

Wheelock, D., and Wilson, P. 2000. Why do banks disappear? The determinants of U.S. bank failures and acquisitions. Review of Economics and Statistics 81, 127-138.