The Impact of Restrictive Measures on the Price Discovery Function of Stock Index Futures – Evidence From CSI 500 Stock Index Futures
Bibliographic record
Abstract
Restrictive measures implemented by governments have a great impact on the price discovery function of stock index futures. This study compares the price discovery function of CSI 500 stock index futures and CSI 500 stock index before and after the implementation of restrictive measures based on the reaction speed to new information, the price ratio of new information and the price contribution of both future market and spot market. It also analyzes the difference between the price discovery function of the future market and that of the spot market and thus proposes policy implications accordingly.Utilizing data of CSI 500 stock index futures in the period of the stock market crash, this study compares the price discovery function before and after the implementation of restrictive measures. By means of the VECM model and common factor analysis, it further investigates the difference in the price contribution of the two markets. Contributing to existing literature on the relationship between the future market and the spot market, this study explores the change in the price contribution of the two markets and therein studies the impact of restrictive measures on the price discovery function. Empirical evidence finds that before the implementation of restrictive measures, the price discovery function worked more efficiently, while, however, after the implementation of restrictive measures, the price discovery function did not work. Hence, stock index futures do assist in the price discovery of the spot market. In some special time periods, however, due to the impact of restrictive policies, the price contribution of the spot market exceeded that of the future market, implying that the price discovery function of the CSI 500 stock index future market is unstable.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".