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

Koresh Galil () and Neta Gilat

International Review of Finance, 2019, vol. 19, issue 4, 731-758

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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/irfi.12197

Related works:
Working Paper: PREDICTING DEFAULT MORE ACCURATELY: TO PROXY OR NOT TO PROXY FOR DEFAULT (2018)
Working Paper: Predicting default more accurately: to proxy or not to proxy for default? (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:irvfin:v:19:y:2019:i:4:p:731-758

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1369-412X

Access Statistics for this article

International Review of Finance is currently edited by Bruce D. Grundy, Naifu Chen, Ming Huang, Takao Kobayashi and Sheridan Titman

More articles in International Review of Finance from International Review of Finance Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().