Profitability of Applying Simple Moving Average Trading Rules for the Vietnamese Stock Market
Bibliographic record
Abstract
This paper considers whether the moving average rules can forecast stock price movements and outperform a simple buy-and-hold strategy over the period from July 2000 to March 2011 on Vietnamese data. We concluded that the technical trading rules examined have strongly predictive ability in term of Vietnamese data. The rules have greater forecasting power for Vietnamese than those for some other Asian markets. Using all the VMA rules and averaging the results yields annualized profit of 39.05% before trading cost, compared to 37.29% in Thailand and 29.2% in some other emerging Asian markets. The profitability of short-term technical trading rules is better than that of longer-term ones. We realize that the (1,10,0) rule, (1,20,0) rule, and (1,50,0) rule are determined to be very effective in Vietnamese stock market because they allow investors to make a large excess returns before trading cost. Specially, unlike almost prior studies for other emerging markets, we prove that the technical trading rules are profitable, even after adjusting for trading costs. However, they are not effective to create access returns for investors in the Vietnamese market after reducing trading costs.
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 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".