Is There Any Witching in the Cryptocurrency Market?
Bibliographic record
Abstract
This paper explores price effects caused by the expiration of derivatives in the cryptocurrency market. Applying different statistical tests (ANOVA, Mann–Whitney, and t-tests) and econometric methods (the modified cumulative abnormal return approach, regression analysis with dummy variables, and the trading simulation approach) to daily and weekly Bitcoin data over the period 2018–2021, the following hypotheses are tested: (H1) Expiration days create patterns in price behavior in the cryptocurrency market; and (H2) Price patterns can be exploited to generate abnormal profits from trading. The results suggest that expiration effects are only nominally present in the cryptocurrency market. There are differences in returns between expiration-related periods and average returns, but these differences are statistically insignificant. The only case in which an anomaly was detected was related to abnormally high returns during the week of expiration: returns during such weeks were positive in 65% of cases, and were on average 5 times higher than during usual weeks. Trading strategies based on this fact were able to generate results different from those of random trading, with a Sharpe ratio above 1. This is evidence in favor of the existence of a real price anomaly, which contradicts the efficient market hypothesis, and this could be implemented in the practice of traders and investors by creating trading strategies based on detected price effects or special technical analysis indicators to generate trading signals. For academics, these results might provide an opportunity to improve time series forecasting analysis in the case of Bitcoin.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".