Bibliographic record
Abstract
We examine the potential effect of Chinese superstition on returns in eight major currencies. We focus on market responses to days that are superstitiously deemed by the Chinese to be either lucky or unlucky. After controlling for the weekend and calendar month anomalies, our results suggest that lucky day 8 in the month is associated with significant lower currency returns for four currencies (Canadian dollars, Euros, Swiss Francs, and British Pounds). In contrast, lucky day 18 is associated with significant higher currency returns for Australian dollars, and unlucky day 24 is associated with significant higher returns for Euros. The results support the argument that Chinese manufacturers convert the foreign currency receipt into US dollars on day 8, and that Chinese companies buy Australian dollars on day 18, possibly to pay for importing natural resources. Our evidence is also consistent with the argument that the Chinese Government buys Euros in unlucky day 24 for investment in European Government debt securities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".