Fair Value Accounting and Corporate Reporting in Nigeria: A Logistics Regression Approach
Bibliographic record
Abstract
This study examined the impact of fair value accounting on corporate reporting in Nigeria. The primary data used were gathered through a well-structured questionnaire, designed and administered to 120 respondents, who are made up of accountants, auditors, bankers, financial experts and practitioners in Lagos State, Nigeria. We adopted the logistic regression approach in analyzing the research questions. We found that fair value accounting has impact on corporate reporting. The Cox and Snell’s R-Square revealed that 67.1% of the variation in the corporate reporting was explained by the logistic model. We further found a moderate strong relationship between the fair value accounting and corporate reporting. Based on this finding, the study concluded that the used of fair value helped in predicting the earnings and assessment of the amounts, timing and uncertainty of future cash flows in corporate reporting which dependent on its reliability. However, institutional factors played an essential role in enhancing the reliability of discretionary fair value estimates which in return increased the informativeness of accounting information in corporate 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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".