Board Attributes and Banks’ Abnormal Loan Loss Provision
Bibliographic record
Abstract
It is pertinent to the document that BOD has the responsibility to scrutinize the information enclosed financial statements to ensure a quality and reliable financial information. More so, this paper examined the correlation between board attributes and loan loss provision of sampled Nigerian banks. This research covers a ten years period (2012-2021). Information related to data was got from the sampled banks’ yearly information. Paris-winsten regression, heteroskedastic panel corrected standard error (PCSE) was used because the data is homoskedastic in nature. This study introduced the whistle blowing policy as a control variable. Again, this study introduced learning by theory to explain firm age (control variable). Findings in the results show a significant positive correlation between board size and abnormal loan loss provision (ABLLP). Board meetings may have a negative insignificant connection with ABLLP. While, board autonomy has a positive insignificant correlation with ABLLP. Finally, it was found that whistleblowing may not significantly prevent the practice of ABLLP by banks in Nigeria. As a result, this study established that authorities like the Nigerian Apex Bank should ensure the compliance of their codes of best practice which will serve as an avenue to achieve a financial reporting quality. Federal Republic of Nigeria should ensure the full implementation of the law on whistleblowing policy and protection of whistleblowers. Furthermore, all findings and recommendations are restricted to the DMBs in Nigeria. Lastly, studies in the future are advised to focus on board attributes in the other domains where there is a practical problem to be addressed.
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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 | 0.001 | 0.009 |
| 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) | 0.005 | 0.001 |
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".