Bank Profitability Determinants: Firm-Level Observations in the ASEAN-5 Markets
Bibliographic record
Abstract
This paper aims to investigate the bank-specific characteristics and macroeconomic factors affecting the profitability performance of the Southeast Asian banking sector. The sample markets cover the five original members of ASEAN, i.e. Indonesia, Malaysia, Philippines, Singapore, and Thailand, whereas the sample period encompasses the years between 2010 and 2017. While a healthy financial system is important for the economic sustainability and growth, there are still limited studies to understand how banks generally perform in this region. Our findings largely support the existing hypotheses about the importance of certain micro- and macro variables while contributing new empirical evidence to the current literature. The bank size, loan to assets, loan loss provision, non-interest incomes and expenses, and capital adequacy remain relevant in influencing bank profitability in the ASEAN-5 region. Macroeconomic variables of inflation, interest rate, market concentration and GDP per capita play considerable roles in profitability when they are assessed separately from the bank-specific factors. It is worth noting that the bank-level factors remain important and outplay the macroeconomic factors when they are considered at the same time. The result robustness is of a certain level of satisfaction because comparisons have been performed across individual countries and across different regression models of pooled ordinary least squares model, random effect model, and fixed effect model for all the tentative tests. Both the return on assets and return on equity are examined. Combining both micro- and macroeconomic variables in the regressions also indicates an overall improvement in the r-squared under the same models.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".