A Review of the Establishment of the Stock Market in Vietnam–In Relation to other Transitional Economies
Bibliographic record
Abstract
This paper critically reviews the literature on the roles of privatisation and market regulations in establishing the stock markets in transitional economies, with the focus on the case of Vietnam. Privatisation, or equitisation in Vietnam, can provide promising candidates for the stock market but can by no means replace the well-established institutions which are crucial for its long-term development. The experience from Russia, Czech Republic, Poland and China had shown that the results of mass privatisation could be frustrated if there was little transparency and investor protection on the stock market. For Vietnam, ownership in equitised State-owned Enterprises was still concentrated with the Government and the insiders, posing various risks to minority shareholders. It then required the authority to strengthen the regulatory environment to improve investor confidence. However, enhancing regulations by imitating those of more advanced markets is not expected to bring desirable outcome for stock markets in less developed countries but instead they should focus on the ability to enforce those regulations. In Vietnam, disclosure requirements imposed on listed companies seemed to be comprehensive but not enforceable. This again reveals another drawback of the stock market in Vietnam that can disadvantage minority shareholders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".