Time to delisted status for listed firms in Chinese stock markets: An analysis using a mixture cure model with time-varying covariates
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".