Regulation, Disclosure, and the Displacement of Internal Governance in Saudi Banks
Bibliographic record
Abstract
This study examines whether strengthened prudential supervision reduces the marginal influence of internal governance mechanisms on the performance of Saudi banks during the Vision 2030 reform period. Using a panel of ten listed Saudi banks from 2018 to 2024, governance measures are hand collected to align with Saudi Central Bank definitions, focusing on insider ownership and board independence. To address endogeneity arising from performance persistence and reverse causality, two-step system generalized method of moments with collapsed lagged internal instruments and Windmeijer-corrected standard errors are employed. The results reveal that insider ownership and board independence are statistically and economically insignificant for accounting performance and market valuation, whereas lagged performance remains the dominant predictor. Hansen J and Arellano–Bond AR(2) diagnostics support instrument validity, and robustness checks using alternative estimators and variable specifications produce consistent findings. The results suggest that in contexts where prudential oversight is comprehensive and consistently enforced, internal governance mechanisms may provide limited incremental monitoring value. However, they do not imply that boards or insiders are irrelevant during crises or when enforcement is uneven. Therefore, refining supervisory tools and disclosure practices should be prioritized over imposing additional structural mandates on boards or ownership configurations.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".