Mixture additive hazards cure model with latent variables: Application to corporate default data

Abstract A mixture additive hazards cure model with latent variables is proposed to investigate the risk factors of the corporate default issue with a sample of corporate bonds from the Chinese financial market. The proposed model combines confirmatory factor analysis, additive hazards, and cure models to characterize latent attributes, such as profitability, liquidity, and operating capacity, through multiple manifest variables and investigate the effects of observed covariates and latent factors on the hazards of corporate default and the probability of nonsusceptibility to default. An expectation-maximization algorithm is developed to conduct statistical inference. The satisfactory performance of the suggested method is demonstrated by simulation studies. Application to the corporate default data illustrates the utility of the proposed methodology and its superiority over conventional methods. The empirical results reveal that defaulted companies usually have low profitability, high debt level, and poor operating capacity. The findings also help differentiate between groups that are susceptible and nonsusceptible to default and provide new insights into the warning signs and effective strategies for preventing defaults.