The Impact of Technical Analysis on Stock Returns in an Emerging Capital Markets (ECM’s) Country: Theoretical and Empirical Study
Bibliographic record
Abstract
Technical analysis, even if deliberated by some as purely conjecture, is still generally acknowledged as additional information to main brokerage companies. There are existent two reasons for the achievement of technical analysis and why its success is still debated: (1) stock return predictability stems from efficient markets that can be analysed by time-varying equilibrium returns, and (2) stock return predictability forms from prices wandering apart from their fundamental valuations. Fundamentally, both explanations show some kind of overall market inefficiency where investors are capable of exploiting. Therefore, technical analysis derived its importance from its ability to train investors to take investment decision based on historical trends of securities prices. To help find answers to the issues raised and to structure the study, the following general research question is set: is it possible for technical analysis to achieve abnormal returns in an Emerging Capital Markets (ECM’s) country, more specifically, the Egyptian Stock Exchange? If yes, hence it could be possibly used to help individual investors to take effective investment decision. By means of theoretical and empirical investigation, this study provides significant evidences that technical analysis achieved abnormal returns in inefficiency periods. This study suggests that simple trading rules, more specifically; the simple moving average beat the standard buy-and-hold strategy for the Egyptian stock exchange.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".