Revenue Diversification, Risk and Bank Performance of Vietnamese Commercial Banks
Bibliographic record
Abstract
In the future, when the process of economic integration in the banking sector is more powerful, and competitive, diversifying revenue is an inevitable and objective trend to help the banks increase profits, minimize risks and improve their competitive position in the system. The research is on the relationship between revenue diversification, risk and bank performance using data from audited financial statements and annual reports of 26 commercial banks listed and unlisted in Vietnam during the period 2010–2018. The research method uses Generalized Method of Moment (GMM) modeling techniques to solve endogenous problems, variance and autocorrelation in the research model. Research results show that diversification negatively impacts profitability and the higher the diversification, the higher the risk of commercial banks. However, the more diversified listed banks, the more increased the bank’s stability. The banks show the weakness and lack of experience of the banking system in developing a reasonable profit transformation model. The revenue diversification of banks is currently passive and moves slowly. Interest income is still the motivation of bank development, boosting profit growth. Growth, as well as the contribution from service activities, is not commensurate with potentials; although there are many positive points, they are not enough to cover risks from net interest income activities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".