Do Family-Owned Banks Perform Better? A Study of Malaysian Banking Industry
Bibliographic record
Abstract
It has been discussed that whether family ownership perform better or less perform than non-family ownership that might create or destroy agency costs among the managers and shareholders. This paper is to investigate the financial performance of family and non-family owned banks in Malaysia from year 2001 to 2010. This study compares the financial performance of family and non-family owned banks that operate under central bank of Malaysia, (BNM) and are listed on Bursa Malaysia. Multiple regression technique was performed to investigate the relationship between independent variable (ownership structure) and dependent variables (Tobin’s Q, ROA and ROE). Findings indicate that Tobin’s Q is the best fit as the dependent variable for the regression model. It shows the highest F statistics value, which is 6.247 as compared to ROA and ROE for full sample. Meanwhile, the adjusted R squared of Tobin’s Q indicates similar higher value as well that is 0.150 between the dependent variables. Board composition and board size indicate strong influence on the performance of family-owned banks. Smaller board size on the board can help the bank to achieve better performance in term of Tobin’s Q and ROE. In contrast, board composition attains better performance in term of ROA rather than Tobin’s Q and ROE. This study can provide useful insights of the governance mechanism that could influence the firm performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".