The Impact of Securities Margin Trading on Chinese Stock Market
Bibliographic record
Abstract
In this paper, we take Shanghai Stock Market as the research object, conducts a multi-dimensional analysis of the volatility of the Shanghai Stock Exchange 50 Index before and after the introduction of margin trading. After the implementation of the securities margin trading policy, the historical volatility of the securities market has obviously been weakened. From the perspective of dynamic volatility, we establish a GARCH (1, 1) model by introducing the dummy variables according to the AIC and SC optimal rules, and establish TGARCH (1, 1), EGARCH (1, 1) and PGARCH (1, 1) to analyze the asymmetry. All of the model results show that the introduction of margin trading reduces the risk of the stock market and weakens the asymmetry. In order to test the effect of the residual distribution of returns, we assume that the residuals follow the t distribution and the GED distribution respectively and establish the optimal GARCH (1, 1) model. The final result is the same as those under the Gaussian distribution assumption.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".