Historical Evolution of Monthly Anomalies in International Stock Markets
Bibliographic record
Abstract
This paper is a comprehensive investigation of the evolution of various monthly anomalies (January effect, December effect, and the Mark Twain effect) in the US stock market for its entire history. This is done using various statistical techniques (average analysis, Student's t-test, ANOVA, the Mann–Whitney test) and a trading simulation approach). To confirm our results we extended the analysis to the UK, Japan, Canada, France, Switzerland, Germany and Italy stock markets. The results indicate that the January effect was most prevalent in the US and that the December effect and the Mark Twain effect were never prevalent in the US. This result was confirmed in other markets as well. The January effect was most prevalent in the middle of the 20th century but has since disappeared. Furthermore, the January effect provided exploitable profit opportunities. Our results are consistent and add to the existing literature through the use of a complete history of the US market. Overall, the US stock market is consistent with the Adaptive Market Hypothesis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".