Empirical Comparison of Hazard Models in Predicting Bankruptcy

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 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 SMEs. In line 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 that employ failure definition of SMEs based on bankruptcy laws and firms’ financial health exhibits superior goodness of fit and classification measures, in comparison to hazard models that employ failure definitions that are based either on bankruptcy laws or firms’ financial health.