The choice of capital structure: A study on energy industry in a developing country
Bibliographic record
Abstract
The choice of capital structure has greatly contributed to the success of the firms in general and energy in particular. This study uses a sample data set of 250 energy firms over the period 2010-2019, and by using generalized least square (GLS) method to perform a survey. The main factors in this study include profitability, firm age, state shareholding and depreciation tax shield, etc. The study found that except firm growth, all factors including firm performance, age of firm, size of firm, asset structure, short-term solvency, and depreciation have significantly affected firm’s capital structure choice in the case of energy industry in a developing country. Furthermore, a positive effect was also found for size of firm and asset structure while a negative effect was detected for other factors such as firm performance, asset structure, firm age, short-term solvency, and depreciation. Through this research, we also conclude that the theory of pecking order, and the theory of representative cost are known as the basis for financial managers to build sound capital structures for businesses.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".