Seasonality Effect on the Vietnamese Stock Exchange
Bibliographic record
Abstract
The Vietnamese Stock Market is a remarkable emerging market, including the two stock markets Ha Noi and Ho Chi Minh Stock Exchange and they have been playing a very important role inVietnamese economy. More and more attention is focused on the emerging Vietnamese market, and investors have been trying to find the opportunity to achieve abnormal returns through the Vietnamese Stock Market. We name this phenomenon market efficiency a nomaly, one pattern of which is seasonality effect. In this study, the topic about the seasonality effect is chosen. We try to test the seasonality in Vietnamese Stock Market by day of the week effect, January effect and turn of the month effect. Deductive approach and quantitative research method are used in this thesis. To analyze seasonality effect, the data has been collected from Ho Chi Minh Stock Exchange Composite Index – VN Index and has been tested from 2006 to 2014. Hypothesis and T-test with α=0.05 isused to test the seasonality effect. The results show that seasonal anomalies exist. The above indicates that the Vietnamese Stock Market is not fully efficient yet. Investors may have opportunities to make use of the seasonal anomalies to earn abnormal return. However, the study is based on the historical data, but the future stock price is affected by lots of factors; and like in other invested stock markets, as soon as the seasonal anomalies is certified by the public, the opportunity of making excessive return by profitable trading strategies will disappear at once.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".