Effect of Capital Structure on the Profitability of Non-Financial Institutions in Nigeria
Bibliographic record
Abstract
Capital structure decision primarily deals with the question of how much debt is needed to optimize the value of a firm. The research objective was to establish effects of capital structure on the profitability of non- financial institutions in Nigeria. Theoretically it is assumed that the capital mix a firm uses to finance its operations does not matter and that its future operating income generated by its asset is what determines its value. Multiple linear regression which is capital structure determinant independent variable, leverage ratio, growth of the firm and earnings management. These variables were used to establish whether capital structure decisions affect profitability of non-financial intuitions in Nigeria. Secondary data was collected from 2015 to 2020 and analyzed with the aid of statistical tools. Descriptive study research design was used to determine frequency of occurrence or extent to which variables were related. The population used in this study was five non-financial institution listed at the NSE, study further found out that profitability improved with increase in liquidity and sales growth. From the findings outlined above, the study recommends that companies, should consider borrowing less funds and use internal funds economically so that they can consequently reap from such funds and increase their profit. The study concludes that the firm management should take into account their liquidity which is significant and growth as this also turned to be critical factors in determining profit.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".