Investigating the Impact of Firm Characteristics on Capital Structure of Quoted and Unquoted SMEs
Bibliographic record
Abstract
This study aims to enhance the understanding of SMEs’ capital structure in Egypt. The study tests the impact of asset structure, size, profitability, liquidity, growth, age, and ownership structure as independent variables on the leverage ratio. Three alternative variables are used as a proxy for leverage: total, long term, and short term leverage. The study further investigates the significance of the relationship between the economic sector as a control variable and the three leverage ratios. Multiple regression analysis is used to develop the explanatory models for two samples of SMEs. The first sample comprises of 28 firms, which represent all listed and traded SMEs in Egypt as of 31/12/2016, covering the period from 2008 till 2015. The second sample includes panel data of 95 non-quoted SMEs. The overall model recommends that all the independent and control variables are significantly explaining the capital structure decisions of SMEs in Egypt. The results of the two samples show a high degree of similarities. The managerial ownership is found to be negatively correlated to short term leverage, while the block holding ownership is positively correlated to the total and the short term leverage. Moreover, the sector shows a significant relationship with the capital structure. The results of the study demonstrate that the best explanation of the SMEs behavior in Egypt is the pecking order theory. Finally, the study introduces useful recommendations for policy makers and SMEs’ management in Egypt.
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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 | 0.000 | 0.003 |
| 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".