This study aims to shed light on the debate concerning the choice between discrete-time and continuous-time hazard models in making bankruptcy or any binary prediction using interval censored data. Building on the theoretical suggestions from various disciplines, we empirically compare widely used discrete-time hazard models (with logit and clog-log links) and continuous-time Cox Proportional Hazards (CPH) model in predicting bankruptcy and financial distress of the United States Small and Medium-Sized Enterprises (SMEs). Consistent with the theoretical arguments, we report that discrete-time hazard models are superior to continuous-time CPH model in making binary predictions using interval censored data. Moreover, hazard models developed using failure definition based jointly on bankruptcy laws and firms’ financial health exhibit superior goodness of fit and classification measures, in comparison to models that employ failure definition based either on bankruptcy laws or firms’ financial health.
Bibliographical noteThis is an Accepted Manuscript of an article published by Taylor & Francis in Quantitative Finance on 16/06/2017, available online: http://www.tandfonline.com/10.1080/14697688.2017.1307514
- Discrete hazard models
- Cox proportional hazard
- Financial distress