The Impact of Audit Oversight Quality on the Financial Performance of U.S. Firms: A Subjective Assessment
Bibliographic record
Abstract
Audit committees are appointed by the board of directors of corporations to oversee the financial reporting process, monitor financial control processes, hire and assess independent auditors, and communicate findings with management and auditors. We propose two new measures of audit oversight quality. The first measure is purely subjective, in that it scores audit committees on a scale based on their ability to fulfill one or more of their responsibilities, as mentioned in annual reports, Form 10-K and DEF 13A. The second measure concerns audit committee activity, as it measures the number of times the term ‘audit committee’ is mentioned in these documents. Both measures were obtained for U.S. pharmaceutical companies and energy companies from 2010 to 2022. The audit oversight quality measures were regressed in regard to profitability (measured by return on assets and return on equity), debt capacity (measured by equity multiplier), and firm value (measured by Tobin’s q and economic value added). Audit oversight quality, using both measures, reduces the return on equity. Audit oversight quality, using both measures, had a disciplining effect on debt. Increases in the oversight of increasing debt discourage the propensity to increase borrowing using collateral (debt capacity), and reduce investor returns through investment in debt-financed projects (return on equity). Audit oversight quality, using both measures, exhibited a size effect on the firm’s value, in that an increase in the firm size with high audit oversight quality increases the firm’s value. However, it is possible that only the first measure of audit oversight quality significantly increased the firm’s value, as only the first measure exhibited robustness to the endogeneity effect of size.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".