The Scrutinized-firm Effect, Portfolio Rebalancing, Stock Return Seasonality, and the Pervasiveness of the January Effect in Canada
Bibliographic record
Abstract
This article examines whether seasonality is present in the excess returns of low risk Canadian firms in safe industries for a sample of firms that are highly scrutinized and visible and uses such tests as the foundation to empirically test competing explanations of stock market seasonality, namely, the tax-loss selling hypothesis and the gamesmanship hypothesis. The tests cover the period 1980 to 1998. For a sample of highly scrutinized and visible firms strong seasonality in excess returns is reported. However, the firms in our sample have unusually low excess returns in January and returns adjust upwards over the remainder of the year. The results hold even after we control for various risk differences among the stocks of our sample. Further, this article’s findings imply that the January effect is not as pervasive across risk classes and industry sectors as earlier studies using aggregate data have shown it to be. The disaggregated data of this study provide evidence in support of the gamesmanship hypothesis, but not the tax-loss selling hypothesis. Whenever a January effect is observed, the last quarter of the year tends to be weak for those companies in our sample that experienced a strong January. The opposite is true when a January effect is not evident, as the gamesmanship hypothesis would predict.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".