Stock Yield and Information Asymmetry
Bibliographic record
Abstract
One of the most fundamental economic problems is optimal allocation of resources towards high-yield investments with reasonable risk. For this achievement, it is required to have operational evaluation criteria that some of which emphasize on cash flow variables and some others on the information content of accounting interest. In order to achieve this end, in this study, the relationship between yields before interest and tax and operational cash flow with shareholders’ output in information asymmetry conditions during the life cycle of the listed companies in Tehran Stock Exchange was investigated. To do testing hypotheses, multiple pooled data Regression was used. To separate the companies through life-cycle, Anthony and Ramesh’s (1992) research was used. The results of empirical tests results, by using information related to 142 firms during 2008 to 2013, indicate that in each of three stages of growth-maturity and the firms’ fading and interest before benefit and tax-have no positive and significant relationship with the output of the firms’ stock on the other hand, the interest before benefit and tax rather than operational cash flow have more information content.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".