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

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.

This paper investigates the effects of credit rating downgrades, equity mispricing, and CEO overconfidence on zero-leverage policy, using data for listed U.S. firms during the period 1980-2012. The results show that (a) the likelihood of zero leverage increases significantly following a downgrade in credit rating; (b) zero leverage is the outcome of the past attempts by firms to issue more overvalued equity capital; and (c) firms with overconfident CEOs are more likely to choose zero leverage. The results clearly suggest that the conditions prevailing in both credit and equity markets exert significant influence on zero-leverage policy. The analysis also advocates the inclusion of managerial biases in conjunction with the market-wide conditions in the analysis of zeroleverage policy. Overall, the findings reveal that zero-leverage firms find that the benefits of issuing overvalued equity outweigh the benefits associated with debt financing. These results are robust to a battery of checks.

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