Bitcoin Trading in Australian and Canadian Dollars, the Pound and Euro: Pre- and Post-COVID-19
Bibliographic record
Abstract
Much of the research relating to the impact on Bitcoin, of the COVID-19 pandemic focuses on the United States (US) market using Bitcoin prices in US dollars, but this is a market not open to a large proportion of the world’s population who must trade Bitcoin in their local currency. The aim here is to compare Bitcoin trading behaviour pre- and post-COVID-19 in four currencies, the Australian dollar, the Canadian dollar, the UK pound and the European euro, to see if there is any consistency across currencies. The Bitcoin price may be universal but Bitcoin trading in local currencies can reflect local conditions. What becomes obvious is that when comparing across currencies, there is no consistent pattern. No two currencies are the same. The pre-COVID-19 period dominates in the Australian dollar market. The UK pound is similar except for transactions per day which is higher post-COVID-19. In the Canadian dollar and euro markets neither period dominates with each currency finding ‘not significantly different’ for a number of metrics. Surprisingly, there is not even any consensus with regard to the Bitcoin price which decreases in the Australian dollar and the UK pound markets in the post-COVID-19 period while this period sees an increase in the Bitcoin price in the Canadian dollar and euro markets. Consequently, observing Bitcoin trading behaviour in one currency does not indicate patterns of trade in another currency. This is particularly evident when comparing Bitcoin trading across countries with very different economic conditions, during a period of worldwide economic uncertainty as the COVID-19 pandemic continues to take a toll on local economies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".