Segmentation of the Chinese stock market: A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The Chinese stock market has a threefold segmented structure. Firstly, there is a division between floatable and nonfloatable shares, known as the split‐share structure. Before 2005, more than two‐thirds of the shares outstanding were nonfloatable. This structure limited shareholders’ ability to exercise governance rights through stock trading. While largely dismantled by the 2005 reform, the split‐share structure has not been fully eliminated. Secondly, floatable shares can be issued in multiple currencies. However, shares denominated in the domestic currency often trade at a premium over those denominated in foreign currencies. This premium can be explained by the differences in target investors and the trading restrictions. Thirdly, stocks denominated in the domestic currency are sorted into different market tiers depending on the firms’ profitability and market capitalization. The limited mobility of stocks across different tiers engenders tier‐specific characteristics, such as high family ownership and venture‐capital backing, which have not been sufficiently studied. In this paper, we delineate the three aspects of market segmentation, offer critical comments on the caveats of the extant studies, and propose topics for future research.
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.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it