Stock Market Anomalies : A Literature Review and Estimation of Calendar affects on the S&P 500 index
Bibliographic record
Abstract
This thesis investigates the Day-of-the-week, Month-of-the-year and Quarter-of-the-year effects. Historical data from the S&P 500 index between 1970- 2005 is analyzed. The purpose is to investigate if there is any evidence of increased returns (ROR) pattern related to seasonality during this period. The conclusion is that Wednesdays, December and Quarter 4 have had the highest ROR while Mondays, September and Quarter 3 have had the lowest ROR. The empirical analysis found support for the Monday effect that Mondays are the days with the lowest stock returns. An investor would have earned on approximately four times more if you invested on Wednesdays instead of Mondays. Mondays was the only days with a negative ROR. I also found support for the weekend effect that return on Fridays are higher than returns on Mondays. Based on the empirical analysis a mid-of-the-week effect or Wednesday effect is also present. No support was found for the January effect that stock prices should be higher in January than in December. What I however clearly could see was a September effect. September is the only month with negative returns. You would have on earned approximately three times as much if you invest in the beginning of December instead of the beginning of September. This leads to that the quarter 3 should be avoided due to a negative historical ROR.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".