The Impact of Non-Interest Income on the Performance of Commercial Banks in the ASEAN Region
Bibliographic record
Abstract
This study investigates how non-interest income affects the performance of commercial banks in the ASEAN region. Using data from 36 commercial banks in ASEAN countries from 2008 to 2020 and Bayesian analysis techniques, the results of this study indicate that non-interest income negatively affects commercial banks’ performance in the ASEAN region. In addition, the quantile regression results demonstrated that non-interest income negatively affects commercial banks’ performance in the ASEAN region at all three percentiles (25th, 50th, and 75th). Additionally, we identified a non-interest income threshold of 59.3 percent of total income for commercial banks in the ASEAN region. In light of banking competition and the necessity for commercial banks to diversify their income streams, we offer a variety of policy implications to increase the performance of commercial banks.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".