Audit Committee Chair Attributes and Audit Report Lag in an Emerging Market
Bibliographic record
Abstract
This study is motivated by the new listing requirement of Bursa Malaysia (formerly known as Kuala Lumpur Stock Exchange, KLSE) concerning the shorter timeframe of annual report release. Similarly, the call for future research on the efficacy of audit committee chair (ACC) attributes with audit report lag (ARL) has further driven this study. Therefore, this paper aimed to analyse the relationship between the ACC expertise and ACC tenure with the ARL of companies in the Main Market of Bursa Malaysia in 2015 using a sample of 139 companies. Furthermore, other audit committee (AC) attributes such as AC overlap and AC independence were also examined. Results of the study revealed an average of 95 days is required by the companies to conclude their respective audit reports. ACC with accounting expertise enhanced the ARL, whereas AC overlap and AC independence did not reduce the ARL. Concurrently, other control variables like AC size, frequency of AC meetings, firm size, leverage, and profitability depicted significant relationship with the ARL. Hence, this research is relevant to the current ARL literature via the provision of evidence and justification regarding the important role of ACC with accounting expertise towards the AC effectiveness, thus enhancing the timelines of financial reporting.
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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 | 0.009 | 0.026 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".