Analysis of Selected Seasonality Effects in Market of Rubber Future Contracts Quoted on Tokyo Commodity Exchange
Bibliographic record
Abstract
The commodity market has been becoming one of the main popular segments of the financial markets among individual and institutional investors in recent years, due to downward trend on the stock exchanges. Likely to the equity market, the problem of anomalies in the commodities market is becoming an interesting phenomenon, particularly in the segment of the agricultural market. This paper tests the hypothesis of: monthly, daily, the day-of-the week, the first and the second half of monthly effects on the market of rubber futures, quoted in the period from 01.12.1981 to 31.03.2015. Calculations presented in this paper indicate the existence of monthly effect: in May and November, with the use of the average monthly rates of return and in February, March, April, June, July, August, October and December, when the daily average rates of return were implemented. The seasonal effects were also observed in the case of testing the statistical hypothesis for daily averaged rates of returns for different days of the month (15th), as well as for the daily average rates of retuarn on various days of the week (Thursday). The seasonal effects were no registered for the daily average rates of return in the first and in the second half of a month.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".