The Impact of Audit Committee Characteristics on Earnings Management: A Canadian Case Study
Bibliographic record
Abstract
The objective of this study was to analyze the impact of three Audit Committee (AC) characteristics, financial expertise, diversity and activism on aggressive earnings management. We hypothesized that these AC characteristics are negatively related to aggressive earnings management. To test or hypothesis, we conducted an empirical test with a sample of 10 Canadian corporations listed on the Toronto stock exchange: 5 companies that were accused of aggressive earnings management and 5 other corporations used as a control group. We analyzed the 5-year period prior to the accusation (1999-2003). We measured earnings management by the level of discretionary accruals (using the modified Jones model (1995). Our results show that activism and the financial expertise of AC members are negatively related to aggressive earnings management; however, we did not find a significant relationship between diversity and aggressive earnings management. These results contribute to help governance oversight organizations identify AC characteristics that have the most influence on the detection of aggressive earnings management, which could help agencies develop and enforce methods to detect and reduce aggressive earnings management practices.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".