Should Vietnamese Banks Need More Equity? Evidence on Risk-Return Trade-Off in Dynamic Models of Banking
Bibliographic record
Abstract
This study employs generalized method of moments (GMM) for dynamic panel data models to deal with the nature of banking behaviour, aiming at investigating the impact of bank equity on the risk and return of Vietnamese commercial banks during the period of 2006–2017. The study finds that increasing bank equity is not always the best strategy to be accompanied by absolute benefits, increasing returns and reducing risks for banks but is a trade-off instead. More precisely, banks with larger capital buffers tend to take less risk but are less profitable. In addition, the study also finds a non-linear relationship revealing that bank risk mitigates the effect of bank equity on profitability. Most estimations show strong robustness checked by some alternative techniques. Based on the findings, the study provides some important policy implications to improve the performance of the banking system in Vietnam as well as in other emerging countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".