The Impact of the Capital Structure on Financial Performance: An Applied Study on the Saudi Basic Materials Companies
Bibliographic record
Abstract
This study aimed to measure the impact of the capital structure on the financial performance of basic materials companies listed on the Saudi Stock Exchange (Tadawul) during the period (2014-2021). The study relied on a number of independent variables to measure financial performance (the ratio of total debts to total assets, the ratio of Total debt to equity, the ratio of long-term debt to total assets), and ROE was used as an independent variable to measure financial performance. The study targeted all Saudi public shareholding basic materials companies listed in the Saudi financial market, a sample of (42) companies were selected, which represent the study population. To verify the acceptance or rejection of the study’s hypotheses, the descriptive statistics method was followed, and the multiple linear regression model was used. The researcher concluded that there is a positive and statistically significant effect of the total debt to the equity of the study sample companies, and the absence of a significant effect of (total debt to total assets and long-term debt to total assets) on the financial performance. The study recommended that Saudi companies should use both short-term and long-term debt to finance their operations, which has a positive impact on financial performance. The study also recommended the Saudi Capital Market Authority to issue guidelines for Saudi public shareholding companies that contain the advantages that companies obtain from managing the capital structure with high efficiency and its repercussions on financial performance.
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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 | 0.002 |
| 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".