1. Predicting Default More Accurately: To Proxy or Not to Proxy for Default?
- Author
-
Neta Sher and Koresh Galil
- Subjects
Economics and Econometrics ,Variables ,Actuarial science ,media_common.quotation_subject ,Accuracy improvement ,jel:G33 ,jel:G17 ,Default ,Bankruptcy ,Financial Distress ,Delisting ,Bankruptcy Prediction ,Default Prediction ,Sample size determination ,Bankruptcy prediction ,Econometrics ,Economics ,Financial distress ,Business ,Proxy (statistics) ,Finance ,media_common - 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. In predicting default and in explaining CDS spreads, a default model significantly outperforms any other type of financial-distress model, despite being estimated on a substantially smaller sample (72 defaults compared to 409 bankruptcies and 923 delistings).
- Published
- 2018
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