Forecasting Bankruptcy with Incomplete Information

Xin Xu

MPRA Paper from University Library of Munich, Germany

Abstract: We propose new specifications that explicitly account for information noise in the input data of bankruptcy hazard models. The specifications are motivated by a theory of modeling credit risk with incomplete information (Duffie and Lando [2001]). Based on over 2 million firm-months of data during 1979-2012, we demonstrate that our proposed specifications significantly improve both in-sample model fit and out-of-sample forecasting accuracy. The improvements in forecasting accuracy are persistent throughout the 10-year holdout periods. The improvements are also robust to empirical setup, and are more substantial in cases where information quality is a more serious problem. Our findings provide strong empirical support for using our proposed hazard specifications in credit risk research and industry applications. They also reconcile conflicting empirical results in the literature.

Keywords: Credit Risk Modeling; Incomplete Information; Hazard Models; Bankruptcy Forecast; Probability of Default (PD); Forecasting Accuracy; Intensity-based Models; Reduced-form Models; Duration Analysis; Survival Analysis (search for similar items in EconPapers)
JEL-codes: C41 G17 G33 (search for similar items in EconPapers)
Date: 2013-05-28, Revised 2014-03-31
New Economics Papers: this item is included in nep-cta, nep-for and nep-rmg
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