Bibliographic record
Abstract
The aim of this paper is to examine calendar anomalies, which had been studied in great details since early 1900s on US market. We study, specifically, the day-o week (Monday) effect, the turn-of-month effect, the turn-of-year (January) effect, the preholiday effect, and the sell-in May and go away (Halloween indicator) with the Canadian stock market. Several papers that studies anomalies on Canadian stocks have been identified but none of which studied every possible anomalies there is in history and documented them in one paper, and none of which as specifically studied the anomalies on Canadian stock market in recent years. Well researched papers such as Haugen and Jorion (1996) that studied the January effect on US market with data from years 1927 to 1942 and Athanasakos (1992) also studied the January effect but on Canadian market with data from years 1960 to 1989 all yielded significant calendar anomalies. Calendar anomalies - January effect was discovered in Haugen and Jorion (1996) after all these years. Dzhabarov and Ziemba (2010) comprised all the calendar anomalies on US market; hence, changes of calendar effects over time on Canadian market are of major interest in this paper.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".