The Analysis of Major Credit Risk Factors - The Case of the Vietnamese Commercial Banks
Bibliographic record
Abstract
In this study, we tried to identify determinants of the credit risks at Vietnamese commercial banks. By applying the quantitative model using the unbalanced panel data of 20 banks in the period from 2006 to 2014, coupled with surveying the dependent variable, that is non-performing loan (NPL), in order to express the credit risks in business activities of commercial banks, the study has made conclusions on two groups of determinants that may have influence on the credit risks: (i) Bank-specific determinants and (ii) macro determinants. Specifically, the quantitative results showed that most correlations affirmed the accuracy of theory and relevant previous research findings, of which some notable results obtained by the author included: (i) For bank-specific determinants, the credit risks were highly inertia, requiring the continuous management of credit risks. Besides, the bank size and market share negatively influenced the credit risks of commercial banks due to adverse impacts on the readiness of acceptance of risks in business activities. In addition, the rapid credit expansion, ineffective capital use and credit control and management also caused future credit risks. (ii) For macro determinants, the estimated results re-affirmed the relationship between impacts of economic cycles though GDP growth and credit risks of commercial banks. (iii) Additionally, the study has not found any correlation between the effectiveness of general management, real lending interest rate and credit risks in business activities 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".