Firm Structural Characteristics and Financial Reporting Quality of Listed Deposit Money Banks in Nigeria
Bibliographic record
Abstract
<p>The quality of financial report is very crucial as published financial reports remains, for the most part, the only means by which outside shareholders and investors keep themselves informed about the performance of the firm. In the present economic scenario, this concern for financial reporting quality becomes more acute as emerging market economies and more importantly mono economies like Nigeria face greater uncertainties as they combat the challenges of unprecedented fall in oil prices. In addition to this, the suspension of the CEO, Chairman and two other directors of Stambic IBTC bank by the Financial Reporting Council of Nigeria for filling a misleading financial statement for 2013 and 2014 has also shown that the issue of financial reporting quality cannot be overemphasized. Using secondary data from the published reports of thirteen listed deposit money banks in Nigeria for over a period of ten years between 2005 and 2014, this paper seeks to find the determinants of financial reporting quality and reports the findings of the impact of structural characteristics like age, size and level of leverage on financial reporting quality. Using prio studies as a guide, we developed a model for loan loss provisions and generated the residuals, using these residuals know as abnormal loan loss provisions as the dependent variable for the multiple regression analysis, the study did not find any evidence of significant relationship between firm age, size, leverage and financial reporting quality.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".