Adoption of International Financial Reporting Standard, Capital Structure and Profitability of Listed Firms in Nigeria
Bibliographic record
Abstract
The extant literature on the relationship between capital structure and profitability of listed firms when their financial statement is prepared as per Nigerian Accounting Standard revealed that a relationship exists between capital structure and profitability of listed firms. Listed firms in Nigeria were mandated to adopt International Financial Reporting Standard (hereafter referred to as IFRS) since the year 2012. Since the year 2012, it is not fully known if the adoption of IFRS affects the relationship between capital structure and profitability of listed firms or it is still the same before the adoption of IFRS. This is a gap which this research study intended to address. This study used multiple regression analysis to find out whether the same relationship exists between capital structure and profitability of listed firms in Nigeria or not when listed firms prepare their financial statement as per IFRS from the adoption of IFRS in 2012 to 2015. This research limits its analysis to the use of data taken from the selected firms’ financial statement for the period under study. This finding shows that the relationship between capital structure and profitability of the firm is the same both before and after the adoption of IFRS. It means adoption of IFRS does not influence the relationship between capital structure and profitability of the firm significantly
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".