Hostname: page-component-7b7567c8c-tnzcf Total loading time: 0 Render date: 2026-11-12T08:58:16.531Z Has data issue: false hasContentIssue false
  • English
  • Français

Firm Default Prediction: A Bayesian Model-Averaging Approach

Published online by Cambridge University Press:  15 June 2017

Jeffrey Traczynski
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

I develop a new predictive approach using Bayesian model averaging to account for incomplete knowledge of the true model behind corporate default and bankruptcy filing. I find that uncertainty over the correct model is empirically large, with far fewer variables being significant predictors of default compared with conventional approaches. Only the ratio of total liabilities to total assets and the volatility of market returns are robust default predictors in the overall sample and individual industry groups. Model-averaged forecasts that aggregate information across models or allow for industry-specific effects substantially outperform individual models.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 52 , Issue 3 , June 2017, pp. 1211 - 1245
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2017 

Footnotes

1

I thank Hendrik Bessembinder (the editor), Steven Durlauf, Jane Cooley Fruehwirth, Bruce Hansen, S. Ghon Rhee, Karl Scholz, Tyler Shumway (the referee), Chris Taber, and participants in the University of Wisconsin–Madison Public Finance and Shidler College of Business seminars for their useful comments and suggestions. Special thanks go to Bill Taylor at the Center for High Throughput Computing at the University of Wisconsin–Madison for his invaluable assistance with computational tasks and to Ed Altman for providing data. All remaining errors are my own.

References

Altman, E. I.“Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” Journal of Finance, 23 (1968), 589–609.CrossRefGoogle Scholar
Avramov, D.“Stock Return Predictability and Model Uncertainty.” Journal of Financial Economics, 64 (2002), 423–458.Google Scholar
Azevedo-Filho, A., and Shachter, R. D.. “Laplace’s Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables.” In Uncertainty in Artificial Intelligence, de Mantaras, R. L. and Poole, D., eds. San Francisco, CA: Morgan Kaufmann (1994), 24–36.Google Scholar
Bharath, S. T., and Shumway, T.. “Forecasting Default with the Merton Distance to Default Model.” Review of Financial Studies, 21 (2008), 1339–1369.CrossRefGoogle Scholar
Brock, W., and Durlauf, S. N.. “Growth Empirics and Reality.” World Bank Economic Review, 15 (2001), 229–272.Google Scholar
Brock, W. A.; Durlauf, S. N.; and West, K. D.. “Policy Evaluation in Uncertain Economic Environments.” Brookings Papers on Economic Activity, 1 (2003), 235–322.CrossRefGoogle Scholar
Campbell, J. Y.; Hilscher, J.; and Szilagyi, J.. “In Search of Distress Risk.” Journal of Finance, 63 (2008), 2899–2939.CrossRefGoogle Scholar
Chava, S., and Jarrow, R. A.. “Bankruptcy Prediction with Industry Effects.” Review of Finance, 8 (2004), 537–569.CrossRefGoogle Scholar
Cohen, R. B.; Polk, C.; and Vuolteenaho, T.. “The Value Spread.” Journal of Finance, 58 (2003), 609–641.Google Scholar
Crain, B. R., and Morgan, R. L.. “Asymptotic Normality of the Posterior Distribution for Exponential Models.” Annals of Statistics, 3 (1975), 223–227.CrossRefGoogle Scholar
Crawford, S. L.“An Application of the Laplace Method to Finite Mixture Distributions.” Journal of the American Statistical Association, 89 (1994), 259–267.CrossRefGoogle Scholar
Cremers, K. J. M.“Stock Return Predictability: A Bayesian Model Selection Perspective.” Review of Financial Studies, 15 (2002), 1223–1249.Google Scholar
Crosbie, P., and Bohn, J.. “Modeling Default Risk.” Working Paper, Moody’s KMV (2003).Google Scholar
Dichev, I. D.“Is the Risk of Bankruptcy a Systematic Risk?” Journal of Finance, 53 (1998), 1131–1147.Google Scholar
Duffie, D.; Eckner, A.; Horel, G.; and Saita, L.. “Frailty Correlated Default.” Journal of Finance, 64 (2009), 2089–2123.Google Scholar
Duffie, D.; Saita, L.; and Wang, K.. “Multi-Period Corporate Default Prediction with Stochastic Covariates.” Journal of Financial Economics, 83 (2007), 635–665.CrossRefGoogle Scholar
Durlauf, S. N.; Johnson, P.; and Temple, J.. “Growth Econometrics.” In Handbook of Economic Growth, Aghion, P. and Durlauf, S., eds. Amsterdam, Netherlands: North-Holland (2005).Google Scholar
Durlauf, S. N.; Kourtellos, A.; and Tan, C. M.. “Are Any Growth Theories Robust?” Economic Journal, 118 (2008), 329–346.CrossRefGoogle Scholar
Fernandez, C.; Ley, E.; and Steel, M. F. J.. “Benchmark Priors for Bayesian Model Averaging.” Journal of Econometrics, 100 (2001a), 381–427.CrossRefGoogle Scholar
Fernandez, C.; Ley, E.; and Steel, M. F. J.. “Model Uncertainty in Cross-Country Growth Regressions.” Journal of Applied Econometrics, 16 (2001b), 563–576.Google Scholar
Freedman, D. A.“Diagnostics Cannot Have Much Power Against General Alternatives.” International Journal of Forecasting, 25 (2009), 833–839.Google Scholar
George, E. I.“Discussion of ‘Bayesian Model Averaging and Model Search’ by Merlise Clyde.” Bayesian Statistics, 6 (1999), 175–177.Google Scholar
Giordani, P.; Jacobson, T.; von Schedvin, E.; and Villani, M.. “Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios.” Journal of Financial and Quantitative Analysis, 49 (2014), 1071–1099.Google Scholar
Harada, K.; Ito, T.; and Takahashi, S.. “Is the Distance to Default a Good Measure in Predicting Bank Failures? Case Studies.” NBER Working Paper No. 16182 (2010).Google Scholar
Hillegeist, S. A.; Keating, E. K.; Cram, D. P.; and Lundstedt, K. G.. “Assessing the Probability of Bankruptcy.” Review of Accounting Studies, 9 (2004), 5–34.Google Scholar
Jeffreys, H. Theory of Probability. Oxford, UK: Oxford University Press (1961).Google Scholar
Kandel, S., and Stambaugh, R. F.. “On the Predictability of Stock Returns: An Asset Allocation Perspective.” Journal of Finance, 51 (1996), 385–424.Google Scholar
Kass, R. E., and Raftery, A. E.. “A Reference Bayesian Test for Nested Hypotheses and Its Relationship to the Schwarz Criterion.” Journal of the American Statistical Association, 90 (1995), 928–934.Google Scholar
Kass, R. E.; Tierney, L.; and Kadane, J. B.. “The Validity of Posterior Expansions Based on Laplace’s Method.” In Bayesian and Likelihood Methods in Statistics and Econometrics, Geissner, S., Hodges, J. S., Press, S. J., and Zellner, A., eds. Amsterdam, Netherlands: North-Holland (1990), 473–488.Google Scholar
Koop, G., and Potter, S.. “Forecasting in Large Macroeconomic Panels Using Bayesian Model Averaging.” Federal Reserve Bank of New York Staff Report 163 (2003).Google Scholar
Leamer, E. E. Specification Searches. New York, NY: John Wiley & Sons (1978).Google Scholar
Ley, E., and Steel, M. F. J.. “On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression.” Journal of Applied Econometrics, 24 (2009), 651–674.Google Scholar
Merton, R. C.“On the Pricing of Corporate Debt: The Risk Structure of Interest Rates.” Journal of Finance, 29 (1974), 449–470.Google Scholar
Ohlson, J. A.“Financial Ratios and the Probabilistic Prediction of Bankruptcy.” Journal of Accounting Research, 18 (1980), 109–131.Google Scholar
Pastor, L.“Portfolio Selection and Asset Pricing Models.” Journal of Finance, 55 (2000), 179–223.Google Scholar
Pastor, L., and Stambaugh, R. F.. “Costs of Equity Capital and Model Mispricing.” Journal of Finance, 54 (1999), 67–121.Google Scholar
Pastor, L., and Stambaugh, R. F.. “Comparing Asset Pricing Models: An Investment Perspective.” Journal of Financial Economics, 56 (2000), 335–381.Google Scholar
Raftery, A. E.“Bayesian Model Selection in Social Research.” Sociological Methodology, 25 (1995), 111–163.CrossRefGoogle Scholar
Raftery, A. E.“Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models.” Biometrika, 83 (1996), 251–266.Google Scholar
Rosenkranz, S. L.; Raftery, A. E.; and Diehr, P.. “Covariate Selection in Hierarchical Models of Hospital Admission Counts: A Bayes Factor Approach.” Technical Report No. 268, University of Washington (1994).Google Scholar
Sala-i-Martin, X.; Doppelhofer, G.; and Miller, R. I.. “Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach.” American Economic Review, 94 (2004), 813–835.CrossRefGoogle Scholar
Schervish, M. J.“A General Method for Comparing Probability Assessors.” Annals of Statistics, 17 (1989), 1856–1879.Google Scholar
Shumway, T.“Forecasting Bankruptcy More Accurately: A Simple Hazard Model.” Journal of Business, 74 (2001), 101–124.CrossRefGoogle Scholar
Stock, J. H., and Watson, M. W.. “An Empirical Comparison of Methods for Forecasting Using Many Predictors.” Working Paper, Harvard University (2005).Google Scholar
Tierney, L., and Kadane, J. B.. “Approximations for Posterior Moments and Marginal Densities.” Journal of the American Statistical Association, 81 (1986), 82–86.Google Scholar
Tierney, L.; Kass, R. E.; and Kadane, J. B.. “Fully Exponential Laplace Approximations to Expectations and Variances of Nonpositive Functions.” Journal of the American Statistical Association, 84 (1989), 710–716.Google Scholar
Topaloglu, Z., and Yildirim, Y.. “Bankruptcy Prediction.” Working Paper, Georgetown University (2009).Google Scholar
Vassalou, M., and Xing, Y.. “Default Risk in Equity Returns.” Journal of Finance, 59 (2004), 831–868.Google Scholar
Volinsky, C. T.; Madigan, D.; Raftery, A. E.; and Kronmal, R. A.. “Bayesian Model Averaging in Proportional Hazard Models: Assessing Stroke Risk.” Technical Report No. 302, University of Washington (1996).Google Scholar
Weakliem, D. L.“A Critique of the Bayesian Information Criterion for Model Selection.” Sociological Methods and Research, 27 (1999), 359–397.Google Scholar
Winkler, R. L.“Scoring Rules and the Evaluation of Probability Assessors.” Journal of the American Statistical Association, 64 (1969), 1073–1078.Google Scholar
Wright, J. H.“Bayesian Model Averaging and Exchange Rate Forecasts.” Journal of Econometrics, 146 (2008), 329–341.Google Scholar
Wright, J. H.“Forecasting U.S. Inflation by Bayesian Model Averaging.” Journal of Forecasting, 28 (2009), 131–144.Google Scholar
Zellner, A.“On Assessing Prior Distributions and Bayesian Regression Analysis with g-Prior Distributions.” In Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti, Goel, P. K. and Zellner, A., eds. Amsterdam, Netherlands: Elsevier Science (1986), 233–243.Google Scholar
Zimjewski, M. E.“Methodological Issues Related to the Estimation of Financial Distress Prediction Models.” Journal of Accounting Research, 22 (1984), 59–82.Google Scholar