Time to delisted status for listed firms in Chinese stock markets: An analysis using a mixture cure model with time-varying covariates

Qingli Dong, Yingwei Peng and Peizhi Li

Journal of the Operational Research Society, 2022, vol. 73, issue 10, 2358-2369

Abstract: Analyzing time to delisted status for listed firms with risk warnings in a stock market is important in risk management of the stock market. This analysis is entangled by the fact that not all listed firms with risk warnings will eventually be delisted, making a standard time-to-event analysis not suitable. The presence of time-varying factors that are related to the listed firms and the macro-economic environment adds another layer of challenge to the analysis. We propose to use a mixture cure model with time-varying covariates to analyze time to delisting in two Chinses stock markets. We identify issues in an existing method and propose a new method to better handle time-varying covariates in the mixture cure model. The model allows an exploration of the association between the probability that a listed firm will never be delisted and time-fixed covariates. The performance of the proposed estimation method is examined using simulation and compared with existing methods. The results of the data analysis reveal a few important time-varying covariates that have significant impacts on the time to delisted status. However, none of the measured time-fixed covariates is found to have a significant impact on the probability of never being delisted.

Date: 2022
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DOI: 10.1080/01605682.2021.1992308

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