The Impact of Audit Committee Oversight on Investor Rationality, Price Expectations, Human Capital, and Research and Development Expense
Bibliographic record
Abstract
Audit committees monitor the actions of managers as they pursue the goal of shareholder wealth maximization. The purpose of this study is to measure the impact of audit committee oversight on novel aspects of firm performance, including investor rationality, price expectations, human capital, and research and development expenses. It extends the literature to non-financial outcomes of audit committee oversight. The literature thus far has focused on the financial effects of audit committee oversight, such as return on assets, return on equity, risk, debt capacity, and firm value. Data was collected from 588 publicly traded firms in the U.S. pharmaceutical industry and energy industry from 2010 to 2022. Audit oversight was measured by the novel measurement of the frequency of the term ‘audit committee’ in annual reports and Form 10Ks from the SeekEdgar database. COMPUSTAT provided the remainder of the data. Panel Data fixed-effects models were used to analyze the data. Audit committee oversight significantly increased investor rationality, significantly reduced price expectations, and significantly increased human capital investment. An inverted U-shaped relationship occurred for audit committee oversight and research and development expenses, with audit oversight first increasing research and development expenses, then decreasing them. The study makes several contributions. First, the study uses a novel measure of audit oversight. Second, the study predicts the effect of audit committee oversight on unexplored non-financial measures, such as human capital and research and development expense. Third, the study offers a current test of the Miller model, as the last tests were performed over 20 years ago. Fourth, the study examines the impact of auditing on market measures that have not been explored in the literature, such as investor rationality and short selling.
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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 | 0.017 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".