Determinants of capital structure of Nigerian non -- financial firms.
Bibliographic record
Abstract
The research report presents empirical findings on the determinants of capital structure of selected sample population of non-financial firms of Nigeria. The study was based on quantitative research orientation, and descriptive research design. Secondary data was obtained from 2000-2012 Standard and Poor (S&P) Nigeria Stock Exchange's twenty seven non-financial firms as sample population for the study. ... Major research findings of the study revealed the impact of liquidity in the leverage of Nigerian non-financial firms as a result of institutional factors such as size, return, growth, tangibility, liquidity and dividend on firms' impact and methods of financing. Also, the visibility of static Trade-off Theory as more constant in determining the wave of capital structures of Nigerian non-financial firms. The study concludes by reiterating that even though the selected firms used for the study is not a reflection of all the non-financial firms in Nigeria, however, it asserts that most Nigerian's non-financial firms experience high leverage and dividend payments to investors (foreign and local) as well as experience low liquidity, which needs to be minimized of avoided. In sum, further empirical research is required, especially with the most recent data of S&P and Fitch's (2014) global ratings of Nigeria's economic performance as the leading economy in Africa (Chima, 2014). --Leaf iii.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".