Board Attributes and Bank Performance in Light of Saudi Corporate Governance Regulations
Bibliographic record
Abstract
This study investigates the relationship between various attributes of boards of directors on bank performance in light of Saudi corporate governance regulations. The data set of this study is extracted from the annual reports of all 12 banks listed on the Saudi Stock Exchange (Tadawul) over a period of 10 years from 2009 to 2018. To test the study hypotheses, check the robustness of the results, and address potential endogeneity issues, this study applies different statistical methods, including FGLS, OLS, RE, PLCSE, and 2SLS, using STATA version 17. The results of multivariate analysis show that board size has a significant positive influence only on operational bank performance (ROA). For board composition, the results show that while board independence has a significant negative impact on accounting-based performance (ROA and ROE), it affects positively and significantly the market-based performance (Tobin’s Q). Regarding board education, the results indicate that board members with at least a Bachelor’s degree have a significant negative impact on ROA and ROE. In contrast, PhD holders on the board have a significant positive impact on ROA and ROE, while Master’s holders affect positively and significantly all measures of bank performance. With respect to board diversity, only the CEO nationality has a significant positive effect on ROA and ROE. Board IT experience is found to be significantly and positively associated with ROA and ROE, while board meeting attendance has a significant positive influence only on ROE. These findings have important implications, especially for Saudi regulatory authorities to assess the current practice and compliance with the Saudi corporate governance regulations (SCGRs) and the principles of corporate governance for banks operating in Saudi Arabia (PCGB) regarding board characteristics and provide insights to improve board effectiveness and corporate governance practice in general.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".