CAP Forum on Forensic Accounting in the Post‐Enron World: Audit Committees and Misappropriation of Assets: Publicly Held Companies in the United States*/LES COMITÉS DE VÉRIFICATION ET LE DÉTOURNEMENT DE BIENS: LES SOCIÉTÉS OUVERTES AUX ÉTATS‐UNIS
Bibliographic record
Abstract
ABSTRACT The majority of previous studies investigating the different risk factors associated with financial fraud have focused on investigating misreporting. A few studies have provided only a limited descriptive analysis of cases involving misappropriation of assets without investigating the corporate governance structure and its role in reducing the incidence of misappropriation. Only Beasley (1996) has examined financial fraud and corporate governance structure by combining cases of misreporting and misappropriation of assets by top management. This study investigates the relationship between the incidence of misappropriation of assets by employees, including management, and the effectiveness of the audit committee. Using LEXIS‐NEXIS Research Software 7.1, Business/Finance News to find relevant articles, we identified publicly held companies suffering misappropriation of assets by employees during the period from 1987 to 2000. The study investigated 81 companies experiencing misappropriation and two control samples: 81 random‐control companies and 81 matched‐control companies. The results extend the previous literature related to financial fraud and corporate governance. The percentage of independent members in audit committees and the average tenure of audit committee members were significantly and negatively related to the incidence of misappropriation of assets in publicly held companies in both the random and the matched models, while the number of audit committee meetings was not significant.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".