Should Investors on Equity Markets Be Superstitious? (Example of 7 World Stock Indexes Components)
Bibliographic record
Abstract
The problem of efficiency of financial markets, especially the weekend effect has always fascinated scholars and practitioners due to its relationship with the financial market efficiency. The issue is significant from the point of view of assessing the portfolio management effectiveness and behavioral finance. This paper tests the hypothesis of the unfortunate dates effect upon 7 equity indexes components (CAC40, DAX, DJIA, FTSE30, FTSEBIT, NIKKEI225 and SENSE), i.e. 419 companies. For all these equities the following rates of return were analyzed: Close-close, Overnight, Open-open, Open-close. As unfortunate days, the sessions falling on the following dates were selected: 13th and 4th day of the month, Friday the 13th and Tuesday the 13th. The research proved the presence of all kinds of the “unfortunate dates” effects on analyzed markets. The effects were registered for all analyzed rates of return. The most dominating “unfortunate dates effects” resulted to be Tuesday the 13th, proceeding the 4th day of the month effect. This is the first analysis of the presence of the “unfortunate dates effect”, in which other than Close-close returns were examined and fulfils the research gap.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".