The Impact Of Trading Volume On Portfolios Effective Time Formation/Holding Periods Based On Momentum Investment Strategies
Bibliographic record
Abstract
This paper analyzes momentum investment strategies based on past market data to evaluate the impact of trading volume on price momentum for the Canadian Stock Market. Utilizing variant models of Jegadeesh and Titman (1993) and Lee and Swaminathan (2000), we evaluate the effective time formation/holding periods of portfolios using both past price and trading volume. The findings suggest that taking high trading volume into consideration in momentum investment strategies on the TSX between 1996 to 2004 generally outperformed a strictly price-based momentum strategy for both winners (t= 2.118, p< .05) and losers (t= 2.174, p< .05). The most effective time period for a winning-high-volume portfolio was nine months of formation, starting in April and a 3-month holding period. The holding period is shorter by six months compared to what is suggested by Assogbavi, et al. (2008). In addition, high-volume portfolios consistently bettered low-volume portfolios for both winners (t= 4.121, p< .001) and losers (t= 3.956, p< .001). For investors who base their portfolio construction on momentum investment strategies, these findings suggest that it would be wise to incorporate past trading volume in their selection process.
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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 | 0.025 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".