The Influence of Weather Conditions on Rates of Return of Polish Equity Indices
Bibliographic record
Abstract
The influence of the weather on human behavior have been featured in many, not only scientific publications. This paper tests the hypothesis that the one-session average rates of return of equity indices (WIG, WIG20, mWIG40 and sWIG80) calculated for the different weather conditions differ in two populations. The atmospheric conditions taken into consideration in this paper are as follows: maximum and minimum daily temperature, sunny hours, rainfall, maximum and average wind velocity, atmospheric pressure, snow depth, sun energy ultraviolet radiation index. In the analyzed period, the impact on the daily rates of return was observed in the case of the following weather conditions: maximum daily temperature, sunny hours, rainfall, maximum wind velocity and atmospheric pressure. The other analyzed weather conditions such as average wind velocity, minimum daily temperature, snow depth, sun energy ultraviolet radiation index, turned out to be irrelevant. Thus, the influence of some weather condition on registered rates of return on the Polish equity markets has been proved.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".