Effects of Risk Committee on Agency Costs and Financial Performance
Bibliographic record
Abstract
This study aimed to explore the influence of risk committee characteristics on agency costs and financial performance as well as investigate whether the attributes of a risk committee moderate the association between the agency costs and financial performance of financial firms listed in the Saudi Stock Market (TASI). We primarily concentrate on six attributes of risk committees (risk committee existence, size, independence, meetings, financial expertise, and busyness) and their impact on agency costs and financial performance. This study employed the ordinary least squares (OLS) and generalized methods of moments (GMM) models to explore these relationships. Using a sample of 455 observations representing the financial corporations listed on the TASI for the period from 2010 to 2022, we found that risk committees’ existence, risk committee independence, and financial expertise have negative and significant associations with agency costs, but a positive influence on financial performance. However, risk committee size and busyness are positively related to agency costs and adversely associated with firms’ financial performance. Furthermore, we showed that agency costs influence banks’ financial performance negatively, yet risk committees oversee this risk and enhance banks’ financial performance. The findings of this study have implications for financial firms, policymakers, and regulators. Beyond making empirical contributions by investigating a relatively unexplored topic in a developing Middle Eastern economy, this analysis provides valuable insights into optimizing risk committee characteristics and structures to improve financial monitoring within the framework of Saudi Arabia. This area of research has been relatively limited compared to studies conducted in developed countries.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".