Profitability of the Short‐Run Contrarian Strategy in Canadian Stock Markets
Bibliographic record
Abstract
Abstract Using a time‐varying three‐factor pricing model, this paper examines the profitability of the short‐term contrarian strategy in Canadian stock markets from January 1964 to December 1998. This strategy, which consists in buying losing stocks and selling winning stocks of the previous month, generates statistically significant excess unrestricted returns. However, we show that this result is mainly driven by small firms, especially in January. Moreover, results indicate that short‐term contrarian investing is not economically profitable when we account for transaction costs. Résumé La presente éetude utilise un modele d'evaluation conditionnelle a trois facteurs pour examiner la rentabilitée a cours terme de la strategie dite «contrarian» sur les marches boursiers canadiens entre janvier 1964 et decembre 1998. Cette strategie, qui consiste a acheter les titres les mains performants et a vendre les titres les plus performants du mois precedent, genere des rendements anormaux statistiquement significatifs. Cependant, nous montrons que ce resultat est principalemenl attribuable aux titres des firmes de petite taille, surtout en janvier. Plus encore, la strategie contrarian n'est pas economiquement rentable lorsqu'on prend en consideration les couts de transaction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".