Bankruptcy Prediction With Industry Effects
Georgia Institute of Technology - College of Management
Robert A. Jarrow
Cornell University - Samuel Curtis Johnson Graduate School of Management
This paper investigates the forecasting accuracy of bankruptcy hazard rate models for U.S. companies over the time period 1962 - 1999 using both yearly and monthly observation intervals. The contribution of this paper is multiple-fold. One, using an expanded bankruptcy database we validate the superior forecasting performance of Shumway's (2001) model as opposed to Altman (1968) and Zmijewski (1984). Two, we demonstrate the importance of including industry effects in hazard rate estimation. Industry groupings are shown to significantly affect both the intercept and slope coefficients in the forecasting equations. Three, we extend the hazard rate model to apply to financial firms and monthly observation intervals. Due to data limitations, most of the existing literature employs only yearly observations. We show that bankruptcy prediction is markedly improved using monthly observation intervals. Fourth, consistent with the notion of market efficiency with respect to publicly available information, we demonstrate that accounting variables add little predictive power when market variables are already included in the bankruptcy model.
Number of Pages in PDF File: 49
Keywords: Bankruptcy Prediction, Hazard Models, Industry Effects, Reduced Form Credit Risk Models
JEL Classification: G33, C41, G13working papers series
Date posted: October 20, 2001
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