Portfolios Effective Time Formation/Holding Period Based On Momentum Investment Strategy
Bibliographic record
Abstract
This paper examines the momentum investment strategy based on past market information to evaluate performance, time formation/holding period and seasonality impact on the Canadian Market. In doing so, we assess the effectiveness of portfolio formation and holding periods of this strategy. Utilizing variant models of different methodologies, we find strong evidence that assesses a 9 month formation and a 9 month holding period as the most effective formation/holding period in implementing a Momentum Investment Strategy when the formation period begins in January. We also find that regardless of when the formation period begins, the most effective portfolio will be held for 9 months beginning in October. While these findings confirm the short term nature of this investment strategy, they however differ in terms of the length of formation/holding periods commonly utilized in the literature. The shortness of the actual effective formation/holding periods may be caused mainly by the growing knowledgeable participants in the market. Investors who base their portfolio construction on momentum investment strategy would achieve higher returns by shortening their portfolio formation/holding periods.
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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 | 0.003 | 0.014 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".