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Are Ratings the Worst Form of Credit Assessment Except for All the Others?

Published online by Cambridge University Press:  16 January 2018

Andreas Blöchlinger  and
Markus Leippold
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Abstract

We present a prediction model to forecast corporate defaults. In a theoretical model, under incomplete information in a market with publicly traded equity, we show that our approach must outperform ratings, Altman’s Z-score, and Merton’s distance to default. We reconcile the statistical and structural approaches under a common framework; that is, our approach nests Altman’s and Merton’s approaches as special cases. Empirically, the combined approach is indeed the most powerful predictor, and the numbers of observed defaults align well with the estimated probabilities. With a new transformation method, we obtain cycle-adjusted forecasts that still outperform ratings.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 53 , Issue 1 , February 2018 , pp. 299 - 334
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2018 

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Footnotes

1

We thank an anonymous referee, Karan Bhanot, Peter Christoffersen, Sergei Davydenko, Jin-Chuan Duan, Michael Gordy, Jarrad Harford (the editor), Harald Hau, Liuren Wu, and participants at the 2012 European Finance Association Conference, the 2013 Risk Management Conference, the 2014 Quant Europe Conference, and finance seminars at the University of Zurich, and the University of Toronto for useful comments. We thank Basile Maire for augmenting and arranging the data set used for our empirical analysis. The content of this article reflects the personal view of the authors. In particular, it does not necessarily represent the opinion of the Zurich Cantonal Bank. Leippold gratefully acknowledges financial support from the Swiss Finance Institute (SFI) and Bank Vontobel.

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