CEO Involvement in Selecting Board Members, Audit Committee Effectiveness, and Restatements*
Bibliographic record
Abstract
Research finds independent audit committees and audit committee financial experts are generally effective in monitoring the financial reporting and auditing processes. However, not all audit committees that appear in form to be independent are in fact actually independent, and not all financial experts with similar backgrounds and credentials are equally effective. CEO involvement in the board selection process can affect whether an audit committee substantively functions as an independent one. We use financial statement restatements to examine whether the benefits of having an independent and expert audit committee are diminished, or even eliminated, when the CEO is involved in the selection of board members. Our results provide some evidence that the monitoring benefits of having an independent and expert audit committee are only maintained when the CEO is not formally involved in selecting board members, although our proxy for CEO involvement in the director selection process is likely subject to some measurement error. Further, we find that these results appear to be driven by the more severe restatements, including misstatements in conjunction with fraudulent financial reporting. In addition, we find that our results continue to apply in the post-SOX period, a period where we have a more exact measure of CEO involvement in the director selection process. Finally, we find that the stock market’s negative reaction to a restatement is mitigated only when the audit committee is independent and the CEO was not involved in selecting board members.
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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 | 0.020 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".