EXAMINING THE QUALITY OF FINANCIAL REPORTING IN THE BANKING SECTOR IN NIGERIA: DOES AUDIT COMMITTEE ACCOUNTING EXPERTISE MATTER?
Bibliographic record
Abstract
The inclusion of an accounting expert in the audit committee has been seen as a major feat recorded in the Nigerian Security and Exchange Commission Code reform in 2011. The code mandated all listed firms in Nigeria to make sure that at least one member possesses accounting skill that could help the committee perform its functions effectively. This study therefore, examined the effect of audit committee accounting expertise (when compared to finance and supervisory expertise) on the quality of financial reporting. The study measured financial reporting quality by reliability (total accrual quality) and relevance (audit report lag). The study considered fifteen listed money deposit banks for the period (2003-2012). Analyses were carried out using descriptive statistics and Panel Lest Square. It was found that, the inclusion of accounting expert in the audit committee showed a greater negative coefficient with quality financial report. This means having an accounting expert on the audit committee board, exerts greater positive impacts on quality financial report in term of reliability (TAQ) and relevance (ADLAG) when compared to finance and supervisory expertise. It is therefore recommends that, all stakeholders’ especially regulatory agencies should ensure compliance with the provision of Nigerian Security and Exchange Commission Code reform in 2011 in term of inclusion of accounting experts on the audit committee. This would also help the firms in reducing agency costs.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".