Effect of Corporate Financial Leverage on Financial Performance: A Study on Publicly Traded Manufacturing Companies in Bangladesh
Bibliographic record
Abstract
The study strives to examine the effect of financial leverage on financial performance in a developing country context using two OLS regression models based on panel data consisting of 816 cases (48 companies x 17 years). Financial performance is measured using ROA, ROE, EPS, and Tobin’s Q, and financial leverage is measured using the debt-assets ratio and debt-equity ratio. It is observed that ROA and Tobin’s Q are negatively correlated with financial leverage, which is in line with the assumptions of the pecking order theory, market timing theory, and many empirical studies. However, financial leverage has a positive effect on ROE and no effect on EPS. These results are also consistent with the MM theorem, static trade off theory and many other empirical studies. Yet again, the two OLS models have put forward conflicting results while taking EPS as the dependent variable. The results corroborate the inefficient use of debt capital and suggest the need to improve the reliability of accounting information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".