Target Capital Structure of Egyptian Listed Firms: Importance of Growth and Risk Factors
Bibliographic record
Abstract
This paper investigates the determinants and adjustment speed to the target capital structure of the Egyptian Listed firms over the period of 2009 – 2018. We use panel regression analysis to examine role of growth factors as well as risk factors in explaining the dynamics of target leverage. The main findings of the growth factors model (GFM) reveal that political risk, profitability and stock market return are negatively affect the target leverage of Egyptian firms. In contrast, investment opportunities, non-debt tax shield, firm size have significant positive effect on the target leverage. On the other hand, the results of risk factors model (RFM) indicate that political risk, size and profitability lose their significant effects for the account of firm risk, stock return, investment and asset tangibility. The business risk captures the effect of political risk on the target leverage. Interestingly, both the investment opportunities and the non-debt tax shield preserve their positive effects and thereby they are considered as the most important firm-specific determinants of the target leverage. We find no significant effects of the economic growth, macroeconomic risk and stock market volatility on the target leverage in Egypt. Regarding the adjustment speed and in the presence of growth (risk) factors, the Egyptian firms take 2.7 (4.4) years to adjust their current leverage toward the target leverage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".