Determinants influencing capital adequacy ratio of Vietnamese commercial banks
Bibliographic record
Abstract
This study employs a panel data analysis to identify the factors that significantly affect the capital adequacy ratio (CAR) of Vietnamese commercial banks for the period from 2011 to 2018. During this period, the number of banks had decreased from 41 to 31 due to mergers and acquisitions. The variables that are hypothesized to affect the capital adequacy ratio of commercial banks in Vietnam include bank size (SIZE), deposit (DEP), loan (LOA), loan loss reserves (LLR), liquidity (LIQ), return on assets (ROA), return on capital (ROE), net interest margin (NIM), non-performing loans (NPL) and leverage (LEV). The results indicate that LEV, LLR, ROE had a negative impact, ROA had a positive impact, and SIZE, DEP, LOA, LIQ, NIM, NPL did not significantly influence the CAR of Vietnamese commercial banks.
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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 | 0.004 |
| 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) | 0.002 | 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".