Bibliographic record
Abstract
This article considered the effect of different variables in the form of 3 ratio of debt capital structure, the ratio of equity to total assets and the ratio of debt to equity based working capital and growth opportunities.This article from the purpose point of view is applied.The research is quasi-experimental research used a casual approach.The sample consists of all firms listed on Tehran Stock Exchange respectively.The study period is considered from 2009 to 2013.Using the sampling method to remove systematic, 90 companies formed the study population.Data research through data collection firms in the sample by reference to the financial statements, explanatory notes, weekly and monthly reports Dinasahm Stock Exchange and using the software, desert, ATMs do verify hypotheses were embargoed management and given the nature of information and data from past research that is based on actual data, the methods employed in this study, multivariate linear regression using data fusion panel.In this regard, used Excel and Views software's.Results showed that, between the debt ratio and debt-to-equity ratio of working capital there is no significant relationship and the ratio of equity to total assets working capital between the total assets of the working capital there is a positive and significant relationship.Also between the depth ratio and debt ratio to equity ratio there is a significant negative relationship and between growth opportunities and between the ratio of equity and total assets on growth opportunity there is positive and significant relationship.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.891 | 0.876 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".