Price Delay and Market Efficiency of Cryptocurrencies: The Impact of Liquidity and Volatility during the COVID-19 Pandemic
Bibliographic record
Abstract
The rise of cryptocurrencies as alternative financial investments, with potential safe-haven and hedging properties, highlights the need to examine their market efficiency. This study is the first to investigate the combined impact of liquidity and volatility features of cryptocurrencies on their price delays. Using a wide spectrum of cryptocurrencies, we investigate whether the COVID-19 outbreak has affected market efficiency by studying price delays to market information. We find that as liquidity increases and volatility decreases, cryptocurrencies demonstrate stronger market efficiency. Additionally, we show that price delay differences during the COVID-19 outbreak increase with higher levels of illiquidity, particularly for highly volatile quintiles. We suggest that perceived risks and high transaction costs in illiquid and highly volatile cryptocurrencies reduce active traders’ willingness to engage in arbitrage trading, leading to increased market inefficiencies. Our findings are relevant to investors, aiding in improving their decision-making processes and enhancing their investment efficiency. Our paper also presents significant implications for policymakers, emphasizing the need for reforms aimed at enhancing the speed at which information is incorporated into cryptocurrency returns. These reforms would help mitigate market distortions and increase the sustainability of cryptocurrency markets.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".