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Firm Default Prediction: A Bayesian Model-Averaging Approach

Published online by Cambridge University Press:  15 June 2017

Jeffrey Traczynski
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Abstract

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.

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 

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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.

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