Predicting Default More Accurately: To Proxy or Not to Proxy for Default?

35 Pages Posted: 14 Jun 2015 Last revised: 28 Feb 2018

See all articles by Koresh Galil

Koresh Galil

Ben-Gurion University of the Negev - Department of Economics

Neta Gilat

Ben-Gurion University of the Negev - Department of Economics

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Date Written: June 3, 2015

Abstract

Previous studies targeting accuracy improvement of default models mainly focused on the choice of the explanatory variables and the statistical approach. We alter the focus to the choice of the dependent variable. We particularly explore whether the common practice (in literature) of using proxies for default events (bankruptcy or delisting) to increase sample size indeed improves accuracy. We examine four definitions of financial distress and show that each definition carries considerably different characteristics. We discover that rating agencies effort to measure correctly the timing of default is valuable. Our main conclusion is that one cannot improve default prediction by making use of other distress events.

Keywords: Default, Bankruptcy, Financial Distress, Delisting, Bankruptcy Prediction, Default Prediction.

JEL Classification: G17, G33

Suggested Citation

Galil, Koresh and Gilat, Neta, Predicting Default More Accurately: To Proxy or Not to Proxy for Default? (June 3, 2015). Available at SSRN: https://ssrn.com/abstract=2618190 or http://dx.doi.org/10.2139/ssrn.2618190

Koresh Galil (Contact Author)

Ben-Gurion University of the Negev - Department of Economics ( email )

Beer-Sheva 84105
Israel
+972-8-6472310 (Phone)

Neta Gilat

Ben-Gurion University of the Negev - Department of Economics ( email )

Beer-Sheva 84105
Israel

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