Corporate Finance Principles and Wood Industry Growth: Empirical Evidence from Bosnia and Herzegovina
Bibliographic record
Abstract
The use of fundamental corporate finance principles as well as the combination of their effects does not always contribute to the same performance effects and firms’ growth. It can considerably vary in certain periods, market turbulences and various industries. Referring to the permanent need for exploration of the individual and integrated effects of the corporate finance principles on firms’ growth in specific circumstances, on the one hand, and particular importance of wood industry in Bosnia and Herzegovina (B&H), on the other, in this paper we examine the extent to which corporate finance principles support wood industry growth in B&H. Specifically, the aim of this research is to investigate the contribution of investments, financial leverage and dividend policy to the growth of firms in this particular industry. Furthermore, the compliance of the paper findings with theoretical concepts and expectations of corporate finance principles role arises as the additional research challenge. Finally, we are interested to find out if the empirical results provide a solid base to apply consistent incentives policy that may further stimulate wood industry growth in B&H. We select all wood-processing firms in B&H from 2008 to 2016. Stata 15 is applied in the empirical analyses and calculations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".