<scp>CEO</scp> power and the strategic selection of accounting financial experts to the audit committee
Bibliographic record
Abstract
Abstract We examine the role of CEO power in the appointment of accounting financial experts (AFEs) to firm audit committees. Our results show that firms with powerful CEOs have a lower likelihood of appointing AFEs to their audit committees. In addition, effective AFEs—those characterized by experience, high status, and social independence from the CEO—are less likely to be appointed in firms with powerful CEOs. In the presence of powerful CEOs, effective AFEs are also less likely to be designated audit committee chair. The absence of effective AFEs is associated with the use of accounting discretion by powerful CEOs to meet or just beat analyst earnings forecasts. We find no evidence that AFEs choose to avoid serving on the boards of firms with powerful CEOs. Our findings are consistent with powerful CEOs influencing board appointments post‐Sarbanes‐Oxley Act through informal channels, including through their social ties with nominating committees. Our results suggest that current regulations prohibiting CEO involvement in the director nomination process and specifying who qualifies as a financial expert may be insufficient to ensure audit committee effectiveness and financial reporting quality.
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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 | 0.004 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".