An Empirical Test of Capital Structure Theories for the Vietnamese Listed Firms
Bibliographic record
Abstract
Raising capital efficiently for the operations is considered a fundamental decision for any firms. Since the 1960s, various theories on capital structure have been developed. Various empirical studies had also been conducted to examine the appropriateness of these theories in different markets. Unfortunately, evidence is mixed. In the context of Vietnam, a rising powerful economy in the Asia Pacific region, this important issue has been largely ignored. This paper is conducted to provide additional evidence on this important issue. In addition, different factors affecting the capital structure decisions from the Vietnamese listed firms are examined. The Generalized Method of Moment approach is employed on the sample of 227 listed firms in Ho Chi Minh City stock exchange over the period from 2008 to 2017. Findings from this study suggest that the Vietnamese listed firms follow the trade-off theory to determine their capital structure (i.e., to determine the optimal debt level). In contrast, no evidence has been found to confirm that the pecking order theory can explain the financing decisions of the Vietnamese listed firms, as previously expected. In addition, findings from this study also indicate that ‘Fund flow deficit’ and ‘Change in sales’ are the most two important factors that affect the amount of debt issued for the Vietnamese listed firms. Implications for academics, practitioners, and the Vietnamese government have also been emerged from the findings of this paper.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".