Firm Structural Characteristics and Financial Reporting Quality of Listed Deposit Money Banks in Nigeria
Bibliographic record
Abstract
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.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".