Determinants of Capital Structure and Testing of Applicable Theories: Evidence from Pharmaceutical Firms of Bangladesh
Bibliographic record
Abstract
The objectives of this paper are to determine the significant factors that affect the capital structure of listed pharmaceutical firms in Bangladesh and to test the capital structure theories. To achieve the intended objectives a panel dataset including 8 major pharmaceutical firms were taken over the time period from 2009 to 2013. The collected data were analyzed by employing correlated panels corrected standard error model using six variables i.e. profitability, tangibility, growth, size, liquidity and operating leverage. Among the 6 variables tangibility, profitability and operating leverage were found to be statistically significant determinants of capital structure. Profitability, tangibility, growth and operating leverage are negatively related to the capital structure while size and liquidity are positively related to the capital structure of the pharmaceutical firms of Bangladesh. The empirical analysis finds that the static trade-off theory and the pecking order theory are the most dominant capital structure theories for the pharmaceutical firms of Bangladesh. These factors must be considered by the financial manager to determine the appropriate capital structure for the company to maximize value of the firm.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".