The Impact of Noninterest Income on the Profitability of Commercial Banks in VietNam: Evidence of Non-Linear Relationship
Bibliographic record
Abstract
This paper uses the Generalized Method Of Moments (GMM) to analyze the impact of noninterest income on the profitability of 28 Vietnamese commercial banks in the period from 2010 to 2018. At the same time, the Threshold Regression Model is applied on a panel data to evaluate whether or not there is a non-linear relationship between the noninterest income ratio and bank’s profitability. The results have shown that the optimal diversification benefit can be attained by reaching a certain level of non-interest income proportion. The findings of the study are: (1) The existence of two thresholds shows that there is non-linear relationship, confirming the non-linear relationship between the noninterest income ratio (NII) and profitability (ROA); (2) The noninterest income ratio impacts negatively on profitability (ROA) when NII (≤44.16% and ≥ 46.62%), when the noninterest income ratio is between 44.16% and 46.12% the relationship is positive. The noninterest income ratio ranging from 44.16% to 46.62% is called optimal when this ratio is in a positive correlation with profitability, which means that Vietnamese commercial banks can try to increase their profits by increasing NII and maintaining that level to get exploiting their maximum level of diversification from noncredit income.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".