The Lead and Lag Relationship Between Spot Market and Futures Market: Empirical Evidence From Vietnam
Bibliographic record
Abstract
This study investigates the lead and lag relationship between Spot market and Futures market in Vietnam. In this study, we employ the data collected from stock-related database in Ho Chi Minh Stock Exchange and Ha Noi Stock Exchange. The data of daily closing prices of VN30 index (the spot price) and VN30F1M (the 1-month future price of VN30 index) are then collected. We apply various methods, namely: Granger causality test, Johansen co-integration test, Vector Error Correlation Model, Impulse Response Function and Variance Decomposition. The result of this paper is consistent with previous research. It finds strong evidence that Spot market leads Futures market in Vietnam stock market in both the short-run and long-run. Therefore, Spot market play a discovery role in which investors can obtain useful information from Spot market to improve their portfolio profit and minimize the risk. Besides, regulators can rely on this finding to come up with better policies and further develop Futures market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".