The Law of One Price and its Impact on Arbitrage Opportunities for Cryptocurrencies
Bibliographic record
Abstract
Understanding the unprecedented growth of cryptocurrency has challenged professionals and scholars. This study involved addressing the existence of arbitrage opportunities in the Canadian cryptocurrency market. The purpose of this study was to test the theory of the law of one price (LOP) on cryptocurrency in Canada. The LOP demonstrates the value of a financial asset should be the same across different markets. The research questions for this study examined if different exchanges cause arbitrage opportunities in the Canadian cryptocurrency market and if volatility and liquidity were influencers of the arbitrage opportunities between Canadian cryptocurrency exchanges. A quantitative nonexperimental cross-sectional research design was employed with a sample population of almost 3,000 data points collected for four cryptocurrencies across four cryptocurrency exchanges. The data analysis techniques were predictive modeling and a binary logistic regression model. The study results indicated that arbitrage opportunities were found almost 100% of the time, and volatility and liquidity were weak influencers of the arbitrage opportunities. Professionals will become better equipped to protect average and inexperienced investors in cryptocurrency from the study results. The positive social change implications can enable professionals to gain greater insights into supporting and educating investors in high-risk cryptocurrencies who lack risk management knowledge or financial stability to lose a portion or all of their savings.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".