Analysis of Sell-in-May-and-Go-Away Strategy on the Markets of 122 Equity Indices and 39 Commodities
Bibliographic record
Abstract
The problem of efficiency of financial markets has always been one of the most important, including, among others, calendar effects. The sell-in-May-and-go-away (also called Halloween) effect is worth considering from the point of view of assessing the portfolio management effectiveness and behavioral finance. This paper tests the sell-in-May-and-go-away strategy and its modifications on the market of 122 equity indices and 39 commodities in the eight approaches, depending on the investment time horizon (October-15th May, November-15th May, October-1st May, November-1stMay) and types of computed rates of return (accrued rates of return and average daily geometric rates of return). Calculations presented in this paper indicate the presence of the sell-in-May-and-go-away effect on the analyzed markets in the classic time frame, as well as in the different time frames. ation in the country. Markets determine nominal exchange rate should prevail in the economy. The country should regulate its foreign reserve policy by setting a threshold, above which excess deposit should be plough back to the domestic economy inform of investments rather than support excessive importation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".