Does Asymmetry of Information Drive Banks’ Capital Structure? Empirical Evidence from Jordan
Bibliographic record
Abstract
In search of the applicability of the capital structure theory (Pecking Order Theory) this study seek to penetrate into the most important factors on a bank’s capital structure using panel data derived from 14 Jordanian banks quoted on the Amman Stock Exchange of 2013 over the time span of 15 years (1999-2013). The feasible generalised least squareis used in this study as the analysis model and Size serves to be a moderator variable. The results have demonstrated that out of three variables, tow (dividends and tangibility) are significantly linked with leverage, whereas the remaining is insignificantly associated with leverage. It is indicated that dividends and tangibility appear to function as the determinants of capital structure. The dividend has a negative effect on capital structure. It implies that although banks favor to payout dividends to shareholders, less debt capital is used. Tangibility affects capital structure positively. The greater tangibility necessitates the use of more debt in capital structure to fund all the activities. Bank size does not moderate the effects of growth, dividends and tangibility on the capital structure. It also appears that this study shows evidence in Jordan banks relatively and somewhat complies with the pecking order theory. The findings are of useful for both investors and managers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".