Microeconomics and raw material price on capital structure adjustment through dynamic target in Indonesian textile industries
Why this work is in the frame
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Bibliographic record
Abstract
This study aims to examine the effects of company-specific macroeconomic fluctuation in raw materials prices on the speed of adjustment through dynamic targeting capital structure on textile companies listed on the Indonesia Stock Exchange during 2012 and the second quarter of 2020. Using panel data regression of the fixed-effect method, we discovered that the speed of adjustment varies in each industry and period. Textile companies listed on the Indonesia Stock Exchange adjust their capital structure through a dynamic target of 53.3% per year. It takes 1 year and 10 months to close the target capital structure. The factors that determine the target capital structure include company size, tangibility, liquidity and growth opportunity, asset utilization, as well as retained earnings. On the other side, factors that contribute to the speed of adjustment include company size, growth opportunity, earnings volatility, asset utilization, retained earnings, distance to the target, and economic growth. Other factors that also affect the speed of adjustment include fluctuations in the prices of cotton and crude oil. The result of this study is expected to provide an optimal capital structure formulation to the textile industries in Indonesia to finance companies’ operational activities and growth opportunities effectively. This study also provides an overview of how textile companies make capital structure adjustment, as there are changes in company-specific factors, macroeconomic conditions, and fluctuation in raw material prices.
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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 | 0.001 |
| 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 it