Does Audit Oversight Quality Reduce Insolvency Risk, Systematic Risk, and ROA Volatility? The Role of Institutional Ownership
Bibliographic record
Abstract
The board of directors appoints the audit committee to assess the financial performance of the firm. The audit committee uses reports provided by audit firms, such as Form 10Ks, and annual reports to assess firm financial performance. The degree of audit oversight quality is a governance measure, which, if effective, may reduce firm risk. This study measures the effect of three measures of audit oversight quality on insolvency risk, systematic risk, and volatility of return on assets for a sample of U.S. pharmaceutical firms and energy firms from 2010 to 2022. All measures of audit oversight quality reduced firm risk, with the first measure reducing both systematic risk and volatility of return on assets, the second measure reducing systematic risk, and the third measure reducing volatility of return on assets. As institutional ownership is also a governance measure, we tested whether its joint effect with audit oversight quality reduced firm risk. This hypothesis was supported for all three measures of audit oversight quality for systematic risk and for the third audit oversight quality measure for volatility of assets. Robustness was established by replicating the regressions with an alternate governance measure, which yielded similar results. Endogeneity of all audit oversight quality measures was absent due to lack of significance of leverage, firm size, equity multiplier, and firm value in reducing risk through their effect on audit oversight 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".