Studying the Impact of Accruals Quality and Market Risk Premium on Stock return Excess Using Fama-French Three Factor Model
Bibliographic record
Abstract
Determining effective agents in stock return excess behavior (stock risk premium) is one of the key points in investors decision that its benefit and quality of its elements (like accruals) are the impressive factors on stock return excess (stock risk premium) which influence users decision making. Accruals are temporary adjustments that postpone fulfilled cash flows recognition and estimate error degree. Criteria estimating to study these items quality seem necessary because of affecting over future cash flows. Therefore the current study aims to investigate the effect of accruals quality and market risk premium on stock return excess. In order to examine probe hypothesis, we used Fama-French three-factor model that accruals were added to it. For this aim a sample that includes 88 of accepted companies in Tehran stock exchange between 2005 until 2013 was studied. In order to calculate accruals, we used Dechow et al model (1995) utilizing sectional data and to estimate we used Fama-French model (1993) by multivariable regression method and time series data. This study in nature is collateral and in goal is fundamental-experimental. The conclusions show that between accruals quality factors and stock risk premium there is a negative significant relation and between market and stock risk premium there is a positive significant relation. Moreover, the results indicate that among size agent and stock risk premium also between book value to market value ratio factors and stock risk premium there is a negative significant link.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".