The Impact of Unsystematic Risk on Stock Returns in an Emerging Capital Markets (ECM’s) Country: An Empirical Study
Bibliographic record
Abstract
In this study, we aim to introduce behavior of unsystemayic risk and its forecasting ability in prediction of future return in Egyptian Stock Exchange (ESE) as an Emerging Capital market (ECM), over the period of 2006 to 2015. We measure equally weighted unsystemayic volatility by following the Campbell’s (2001) Indirect Method, by considering market size and weekly basis. Our results reveal that unsystemayic risk is the biggest component of total volatility and show no trend, although market volatility has a slow decreasing trend in this period. We also find that small size stocks have slightly higher volatility than the big size stocks but both portfolios have similar idiosyncratic risk behavior. Finally, our analyses about the predictive ability of various measures of unsystematic risk provide evidence that unsystematic risk volatility is not a significant predictor for future return in ESE.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".