Did the Introduction of Securities Margin Trading Decrease China’s A-Share Market Volatility?
Bibliographic record
Abstract
Securities margin trading is a form of credit trading that is used extensively in mature securities markets. With the rapid development of its securities market, China introduced securities margin trading to its A-share market on 31st March 2010 for the purpose of reducing A-share market volatility. Owing to the fact that the introduction of securities margin trading in 2010 only applied to part of the A-share transaction targets, it can be treated as a natural experiment. This paper uses difference-in-differences analysis to investigate whether the introduction of securities margin trading in 2010 decreased China’s A-share market volatility. By selecting 50 underlying stocks of securities margin trading as a ‘treatment group’ and 50 non-underlying stocks as a ‘control group’, this paper utilizes a panel dataset comprising 100 stocks for the period 31st March 2009 – 31st March 2011. Results indicate that the introduction of securities margin trading in 2010 significantly decreased China’s A-share market volatility. In conclusion, this paper recommends that China reduces the barriers and transaction costs of securities margin trading, extends the supply of underlying stocks for securities lending, and enhances the capital supply of margin trading.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".