AN EMPIRICAL STUDY OF FINANCING DECISION RATIOS AND FIRM PERFORMANCE IN NIGERIA'S INDUSTRIAL SECTOR
Bibliographic record
Abstract
The crux of this study is to evaluate the impact of financing decision ratios on the financial performance of listed industrial goods companies in Nigeria. The study aligned with the ex post facto research design. Data was collected from the thirteen (13) listed industrial goods companies on the Nigerian Exchange Group. All the companies were considered as the population of the study. Data was collected from the annual financial statements of the companies for a period of ten (10) years from 2014 to 2023. One hundred and thirty observations were made and, on those observations, the Generalized Method of Moment (GMM) technique was applied for the analysis of data. The study measured financial performance using the return on assets (ROA) while different measures were used to measure the independent and control variables. The study found that the impacts of total debt to total assets and dividend to total debt are significant on return on assets. It was also seen that firm size, firm age, and leverage also play a significant role as control variables. From the findings, the study concluded that focusing closely on the dynamics and interplay of debt financing and the proportion of dividend paid in the face of existing debt structure significantly help out the firms make the right financing decisions as the success of companies mostly depends on the combination of capital obtained and dividend decisions. This percentage decides the level of return on assets. From the findings and conclusion, the study recommended that the management of industrial goods firms should balance the quality of the financial structure through rational financing decisions because appropriate debt capacity and rational dividend payments offer the best advantage to the companies through maximizing return on assets.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".