A study on effects of cost-of-equity models on cost-of-capital and capital structure
Bibliographic record
Abstract
Article history: Received March 26, 2012 Received in Revised form June, 12, 2012 Accepted 19 June 2012 Available online June 26 2012 Most financial managers believe that there are different factors hindering decision-making about the capital structure of a company. This hindrance is so that, in some financial management literatures capital structure is called the mystery of capital. Financial managers widely believe that financial leverage enjoys a noticeable status in managerial decision making as well as management of the framework of balance sheet. The primary purpose of this research is to present applications of equity modules and to study effective factors on such models on Tehran stock exchange. The study covers data over a period of five years from 2001 to 2005. The study analyzes and tests relevant data to firm’s debt ratio and corporate size as effective factors on cost-of-equity. The preliminary findings indicate that contrary to the commonly held belief in financial management theorems, debts ratio has the least effect on cost-of-equity. Nevertheless, the study suggests that the variant of company’s size has a meaningful relationship with cost-of-equity. To calculate cost-of-equity, CAPM, Gordon and return ratio methods are used. Findings show that CAPM has more validity compared with other varieties. On the other hand, the results indicate that there is a 95-percent probability proving that liquidity has a significant negative effect on financial leverage. © 2012 Growing Science Ltd. All rights reserved.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".