Impact of Financial Leverage, Size and Assets Structure on Firm Value: Evidence from Industrial Sector, Jordan
Bibliographic record
Abstract
The present study aims at revealing the financial leverage, Size, and asset structure and its impact on the values of firms. The researcher used the analytical method approach for a sample of 13 firms from the mining and extraction industry sector listed on the Amman stock exchange of the period 2010-2018.The model of simple line regression was used for testing the hypotheses of the study by using both programs of (E-views, STATA) in addition to both programs of unit root test and variance inflation factor to make sure of the data stability and no relationship between variables. The study concluded the non-existence of the impact of financial leverage on the firm value and the relationship between the financial leverage and Tobin’s q scale was negative. However, there was an impact of each size and asset structure on firm value and the relationship between the natural logarithm of size and asset structure was positive with Tobin’s q. The study recommends that Companies must achieve an optimal mixture of debt and equity, for long-term survival and hence the growth of the company.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".