Does Economic Policy Uncertainty Predict Cryptocurrency Returns?
Bibliographic record
Abstract
We examine whether the economic policy uncertainty (EPU) index can predict cryptocurrency returns in countries with the highest number of Bitcoin nodes, which include the US, Germany, France, the Netherlands, Singapore, Canada, the UK, China, Russia and Japan. To the extent that cryptocurrencies are a speculative asset, we hypothesize that an increase in EPU drives cryptocurrency prices below their fundamental values due to the flight-to-quality effect. Then, the prices subsequently undergo correction. Furthermore, we hypothesize that the EPU index predicts better in the long run than in the short run since mispricing takes time to correct. Consistent with our hypothesis, we find that EPU positively predicts cryptocurrency returns in the short run for subsequent 1-month returns and in the long run for subsequent 6- and 12-month returns. Thus, cryptocurrencies cannot act as a hedge or safe haven against other financial assets during uncertain times.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".