A Comparison of China Main Board and Growth Enterprise Market Board - Market Microstructure Approach
Bibliographic record
Abstract
We compare the market quality of the newly established, second board of the China stock market, the Growth Enterprise Market (GEM) with the Main board, and examine its impact on the Main board from the market microstructure perspective. Using the newly available transaction level data, several findings emerge. First trading activities of the Main board stocks increases after the introduction of GEM Board, suggesting the establishment of GEM is not at the expense of the Main board but instead enhance the overall trading activities in China. Pricing error variances are not different in the two Boards while GEM stocks have larger adverse selection cost component of bid-ask spread and higher probability of information-based trading which indicate a larger information asymmetry among traders on average in GEM stocks than those in the Main board. Interestingly we find that the 15 minute returns of Main Board stocks strongly lead that of GEM stocks but the GEM board only weakly leads Main Board, evidencing information transmission from the Main board to the GEM. Overall our findings suggest that the market quality of the GEM is of sufficiently good to provide an important, alternative listing venue for high potential firms in China.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".