Board Characteristics and Earnings Management: Evidence from the Vietnamese Market
Bibliographic record
Abstract
This study empirically analyzes the relationship between Vietnamese firms’ earnings management, board characteristics, and ownership structures. I use board size and the proportion of outside directors to reflect board characteristics, and the ownership percentages of the board of directors, outside directors, and the chief executive officer (CEO) to reflect the ownership structures. I use discretionary accruals, measured by the modified Jones model, to proxy for earnings management. From analyzing firms listed on the Ho Chi Minh and Hanoi Stock Exchanges from 2012 to 2017, I find that board size and the ownership percentages of outside directors and CEOs are negatively related to earnings management, whereas the board of directors’ ownership percentage is positively related. The proportion of outside directors is not significantly associated with earnings management. This study provides policy insights for improving Vietnamese firms’ financial transparency. Specifically, corporate laws regulating board composition should be enacted to ensure that all firms meet a minimum number of board members. Moreover, a policy mandating boards to include independent outside directors is necessary, as establishing an independent outside director system within Vietnam’s corporate law can strengthen the sustainability of the board of directors.
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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 | 0.003 |
| 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 | 0.001 |
| 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".