Empirical comparison of hazard models in predicting SMEs failure

Jairaj Gupta, Andros Gregoriou, Tahera Ebrahimi

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

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 the 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 the continuous-time CPH model in making binary predictions using interval censored data. Moreover, hazard models developed using a 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 a failure definition based either on bankruptcy laws or firms’ financial health alone.

LanguageEnglish
Pages437-466
Number of pages30
JournalQuantitative Finance
Volume18
Issue number3
Early online date16 Jun 2017
DOIs
Publication statusPublished - 4 Mar 2018
Externally publishedYes

Fingerprint

Small and medium-sized enterprises
Hazard models
Discrete-time
Continuous time
Cox proportional hazards model
Censored data
Bankruptcy law
Financial health
Bankruptcy
Prediction interval
Logit
Financial distress
Goodness of fit

Cite this

Gupta, Jairaj ; Gregoriou, Andros ; Ebrahimi, Tahera. / Empirical comparison of hazard models in predicting SMEs failure. In: Quantitative Finance. 2018 ; Vol. 18, No. 3. pp. 437-466.
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Empirical comparison of hazard models in predicting SMEs failure. / Gupta, Jairaj; Gregoriou, Andros; Ebrahimi, Tahera.

In: Quantitative Finance, Vol. 18, No. 3, 04.03.2018, p. 437-466.

Research output: Contribution to journalArticle

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