A study of the effects of company size on systematic risk based on the capital asset pricing model among accepted companies in Tehran Stock Market ,
Bibliographic record
Abstract
Systematic risk (beta) is one of the most effective factors in predicting the appropriate required rate of return of portfolios. Understanding systematic risk of usual portfolio of various companies helps investors consider financial investment, more confidentially. The aim of this study is to determine if there is any significant relationship between Company Size (Market value of stocks, Book value of stocks, level of company sale, trade volume of stocks, Price dividend ratio) as independent variables and Systematic risk (Beta) as dependent variables. The study chooses 112 companies accepted in Tehran Stock Market based on screening (systematic deletion) in a six-year-period from 2005 to 2010. The required data were gathered from basic financial statement, committee reports, and other available documents in Tehran Stock Market. Regression and Pearson correlation were used to analyze the data. The results of the study revealed that there is a significant relationship between the variables. Some suggestions regarding the topic of the research are given too.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".