Cash to Price Ratio & Stock Returns: Evidence from Emerging Markets
Bibliographic record
Abstract
This study examines the impact of size premium and value premium on average return in emerging economies i.e. Pakistan, India and China equity markets for the period from June 2000 to June 2015 by using three factors model. This study predicts the significance and positive relationship between value premium(C/P Ratio) and stock return for all non-financial companies listed on Karachi stock exchange, Bombay stock exchange and Shanghai stock exchange on the basis of market Capitalization. The regression results of the study illustrate that size premium predict returns more for small firms than big firms while market premium found significantly positive with stock returns in Pakistan, India, and China. Value premium is found positive for all created portfolios. Therefore, it can be concluded that value effect is present in three emerging markets. High C/P ratio outperforms the low C/P ratio stocks. In this study C/P ratio (value premium) integrated with size and market premium to check whether it can predict stock returns of small and large firms for high or low C/P ratio. The finding is similar that the positive relationship of value premium and stock return and the negative relationship of size premium and stock return. The explanatory power of Fama and French three-factor model is greater than CAPM for all three equity markets, so, the asset pricing model can facilitate investors in efficient portfolio diversification for getting enhanced returns.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".