Earning Management and the Effect Characteristics of Audit Committee, Independent Commissioners: Evidence From Indonesia
Bibliographic record
Abstract
This study examines whether companies listed Indonesia Stock Exchange conduct efficient or opportunistic earning management and to investigate the effect the Independent Commissioner and the Characteristics of the Audit Committee (measured by Auditor Size, Independence, Expertise, and Activities) on it. The sample consists of 186 observations of manufacturing companies in Indonesia Stock Exchange during the 2013-2018. Using Panel Regression Fixed Effect method, we find evidence that Independent Commissioners has a significant effect on reducing Earnings Management. The Positive Accounting Theory-the efficient perspective occurs when the compensation contract and internal control system, including monitoring by the board of commissioners, will limit the common view opportunistic manager and motivate the manager to choose the right accounting policy. The effective monitoring conducted by an independent commissioner can reduce agency costs because management prioritizes the best interest of shareholders by maximizing company resources. Furthermore, it was found that Audit Committee Size can reduce earning management. It is due to the wide range of skills of audit committee members in exercising oversight on management. Audit Committees with accounting and financial expertise can reduce earnings management. This finding indicates that the Audit Committee may tend to uphold conservative as accounting mechanism. Accounting conservatism has an important role in restricting opportunistic management behavior.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".