Factors Used to Determine the Financing Structure for Shareholding Companies Listed in Amman Stock Exchange in Jordan
Bibliographic record
Abstract
Purpose – This study aims to explore the main factors that could be used to determine the financing structure for companies listed in Amman Stock Exchange in Jordan. The hypotheses were developed in light of three theories relating financing structure, trade-off theory (TOT), agency theory and pecking order theory (POT).Design/methodology/approach – The study using unbalanced panel data sample applying regression models for two-dimensions combines cross-sectional and time series for non- financial Jordanian companies listed in ASE over the period 2014–2018. Using standard models include entering a fixed effect, random effect, or not using any effect.Findings - The findings showed that company size, growth opportunities, and tax-deductible items other than interest have a positive effect on the borrowing, but profitability affects negatively on borrowing. These results are consistent with the arrangement theories (trade-off theory (TOT) and pecking order theory (POT). indicating the factors associated with trade-off theory were stronger than what is usually found in developed countries, and this is an indication of deep problem resulted from asymmetry information in the Jordanian market. which require to focus efforts on how to building the confidence with investors by providing the data that serve them in decision making. This needs to develop the accounting profession which will open the future for an active market for bonds and stocks that serve companies to financing their projects, and not only for speculation.Practical implications – The results detected how to constitute the suitable financial structure and how making a balance between sources of finance. Also detected weakens role of companies managers and ASE regulators, which require to focusing efforts to improving information deloused to users.Originality/value – limited number researches which have been discuss this issue. Therefore, this study extensively contributes to the shortage literature on the perceived the main factors that may effect on financial structure.Research limitations – Availability of financial information plus difficulty accessing information in developing countries such as Jordan.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".