Earning Quality in Public Listed Companies: A Study on Malaysia Exchange for Securities Dealing and Automated Quotation
Bibliographic record
Abstract
This study attempted to find out whether the size of audit firm, internal audit establishment and former senior auditor as company director have any significant effect on earning management. All data were extracted from the annual reports. The sample firms used in this study were all the companies listed on Bursa Malaysia under Malaysia Exchange for Securities Dealing and Automated Quotation (MESDAQ) technological sector in 2006. There were 128 MESDAQ Companies listed in 2006. However due to unavailability of reports, 113 were used for analysis. Earning Management can be viewed from financial reporting perspective. From a financial reporting perspective, managers may use earning management to meet analysts’ earning forecast, thereby avoiding the strong negative share price reaction that quickly follow a failure to meet investor expectations. Too much earning management, however, reduces the earning quality and the ability investor to interpret current net income as well, particularly if the earning management is buried in core earning or otherwise not fully disclosed. The reported net income is useful to investor in evaluating future firm performance but excessive earning management may reduce this usefulness. Thus this study is very important because from the research findings shows that the size of audit firm, internal audit establishment and former senior auditor as company director have no significant effect on earning management. An understanding of the earning management is also important to accountants because it enables an improved understanding of the usefulness of the net income, especially for reporting to investor. It also may assist them to avoid some of the serious legal and reputation consequences that arise when firms become financially distress where such distress is often preceded by serious abuse of earnings management.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".