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

28 Pages Posted: 26 May 2020

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

Multiple version iconThere are 2 versions of this paper

Date Written: December 2019

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 the 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.

Suggested Citation

Galil, Koresh and Gilat, Neta, Predicting Default More Accurately: To Proxy or Not to Proxy for Default? (December 2019). International Review of Finance, Vol. 19, Issue 4, pp. 731-758, 2019, Available at SSRN: https://ssrn.com/abstract=3603499 or http://dx.doi.org/10.1111/irfi.12197

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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