Survival prediction of distressed firms
Evidence from the Chinese special treatment firms
Bibliographic Data
| ID | 12549623 |
|---|---|
| Authors | Maria Heui-Yeong Kim (University of Wollongong, corresponding author), Shiguang Ma (0000-0003-4510-4236, University of Wollongong), Yanran Annie Zhou (University of Wollongong) |
| Year | 2016 |
| Volume | 21 |
| Issue | 3 |
| Pages | 418-443 |
| Publication date | 2016-05-24 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of the Asia Pacific Economy (JOURNAL) |
| Journal identifiers | ISSN: 1354-7860 • E-ISSN: 1469-9648 |
| Publisher | Routledge (PUBLISHER • GB) |
| DOI | 10.1080/13547860.2016.1176645 |
| OpenAlex | W2397552961 |
| Language | EN |
| Citations received | 1 |
| References cited | 40 |
In the Chinese stock market, firms experiencing financial distress have been imposed on a Special Treatment (ST) cap by the China Securities Regulatory Commission. Using a sample of 441 ST firms tracked from 1998 to 2011, this paper employs a Cox proportional hazards model to predict turnaround probability for a distressed firm to remove the ST cap. The predictor variables incorporate (1) accounting-driven ratios, (2) market-driven variables, and (3) information on ownership structure and restructuring status throughout the process. In contrast to previous distress studies, this paper finds that market variables do not add predictive power to the model when combined with accounting variables. Also, incorporating the time effect, the results show that the survivor function for an ST firm's survival is negatively related to the duration, and that the Cox hazards model outperforms the logit model in the out-of-sample forecast
Actuarial science · Business · China · Commission · Econometrics · Economics · Financial distress · Financial system · Logit · Predictive power · Proportional hazards model · Restructuring · Sample (material · Statistics · Stock market · Corporate Finance and Governance · Corporate Insolvency and Governance · Financial Distress and Bankruptcy Prediction · Finance
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |