Financial Policy and Corporate Performance: An Empirical Analysis of Nigerian Listed Companies
Bibliographic record
Abstract
This study investigates the effects of financial policy and firm specific characteristics on corporate performance. Panel data covering a period from 1990 to 2006 for 70 firms were analyzed. Pooled OLS, Fixed Effect Model and Generalized Method of Moment panel model were employed in the estimation and data were sourced from the annual report and financial statement of the sampled firms. The estimation of the dynamic panel-data results show that long-term debts, tangibility, corporate tax rate, dividend policy, financial and stock market development were all positively related with firms’ performance. Furthermore, the positive relationship between stock market development and ROA suggest that as stock market develops, various investment opportunities are opened to firms. Therefore, there is need to monitor the performance of these variables in order to stabilize and enhance performance of listed firms in Nigeria. In addition, the result shows that growth, size and foreign direct investment are negatively related with firms’ performance (ROA). In addition, the result indicates that higher income variability increases the risk that a firm may not be able to cover its interest payment, leading to higher expected costs of financial distress. This may leads to reduce their profitability. The results of the study generally support existing literature on the impact of financial policy on corporate performance.
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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 | 0.001 |
| 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 | 0.000 |
| 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".