EARNINGS QUALITY AND FIRMS BOOK VALUE: AN EMPIRICAL EVIDENCE FROM THE LISTED FIRMS IN NIGERIA
Bibliographic record
Abstract
The study examined the effect of earnings quality on book value of quoted companies in Nigeria from 2000 to 2016. A sample of 51 firms was purposively selected for the study out of the population of 173 firms that were listed on the Nigerian Stock Exchange for the period. The study adopted Ex-post facto research design. Data extracted from the published audited financial statements of the firms. Pooled OLS technique was employed in data analysis. The study measured earnings quality with four separate earnings attributes: Accruals quality (AQ), earnings persistence (EPERS), earnings predictability (EPRED), and earnings smoothness (ESMOTH). The study revealed that earnings quality significantly affected book value of the listed firms in Nigeria. Specifically, accruals quality (AQ), earnings persistence (EPERS) and earnings smoothness (ESMOTH) each had a positive effect on book value while earnings predictability (EPRED) had negative effect on book value. By implication, since investors and analysts value high earnings quality, the study suggested that, constancy of earnings and discretionary nature of accruals should be considered, managers should equally ensure information disclosure to enhance quality of earnings and credibility of reported book value.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".