Bibliographic record
Abstract
This paper tested the effectiveness of the popular trading rule based on the 50-day and 200-day moving averages on two ETFs: QQQ and SPY using daily and weekly data. We find that for both weekly and daily data, the trading rule shows good results for the entire sample. When we introduce subperiods by decades, we find that the technical rules only work around 50% of the time. When we explored the performance on shorter subperiods of 2.5 years, we found a strong correlation between realized volatility (standard deviation of returns) and the performance of active strategies. To take advantage of this correlation, we modified the basic moving average strategy so we will be invested in the asset when volatility is low but will employ the MA trading rule when volatility increases. We find that the performance of active strategies improved when volatility is considered. Overall evidence in this paper supports the continued usage of technical analysis as a protective tool for high volatility periods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".