Determinants of Capital Structure and Testing of Theories: A Study on the Listed Manufacturing Companies in Bangladesh
Bibliographic record
Abstract
The objectives of this study are to identify the significant determinants of capital structure of the listed manufacturing companies in Bangladesh and to test the relevant capital structure theories. This study used a panel dataset including 74 manufacturing companies listed under 8 industries in Dhaka Stock Exchange (DSE) for the period of 2002-2011. The Unit Root tests suggested that all series were stationary. Using Panel Corrected Standard Error Regression Model and Random Effects Tobit Regression Model, all selected variables were found significant. Managerial ownership positively and Growth rate, Profitability, Debt service coverage ratio, Non-debt tax shield, Financial costs, Free cash flow to firm, Agency costs and Dividend payment negatively affect the capital structure. Tangibility and Liquidity ratio have positive relationship with Long term debt and negative relationship with Short term debt and Total debt. It was also found that Pecking-order theory and Static Trade-off theory are the most dominant capital structure theories in Bangladesh. The policy implication is that the financial managers should consider these determinants as yardsticks before taking the leverage decisions in order to choose the most favorable capital structure for the company so that it maximizes the shareholders’ value.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".