Testing the Predicting Ability of Technical Analysis Classical Patterns in the Egyptian Stock Market
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As an emerging market, Egyptian stock market is characterized by inefficiency which is confirmed empirically in this research. This provoked us to test the ability of technical analysis classical patterns in predicting the future returns through calculating the expected price target consequently the expected future return and compare it with the actual return.Statistical techniques and models including Box Pierce (Ljung-Box), Variance ratio test, Runs test, and t-test bootstrapping technique have been applied to test the research proposed hypotheses. The empirical results revealed that the Egyptian stock market is inefficient as returns don’t follow random walk and are dependent, it is found also that the actual returns have significantly exceeded the expected returns of the detected patterns indicating that classical patterns can perfectly predict the direction of the price movements rather than the exact price targets.
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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 | 0.100 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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 it