The Testing Method of the Co-Movement of A+H Stock Prices
Bibliographic record
Abstract
In this paper, the technical methods utilized for testing the co-movement of A+H stock prices of dual listed companies were analyzed through co-integration test, the Granger causality test, variance decomposition and Geweke indicators. Currently, empirical research on A+H stock markets are mainly based on two perspectives: empirical analysis of international asset price equalization theory and empirical analysis of measurement technology. Commonly there are four base unit test methods: Dickey-Fuller test, Augmented Dickey-Fuller test, Phillips and Perron test and KPSS test. These indicators and methods are then analyzed and evaluated. Research has not yet found an ideal approach to link A+H stock markets, although the above mentioned methods complement each other. In short, these test theories for development are only used in practice. With expanding research, in the future, there will be more powerful theoretical tools for empirical analysis.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".