ArticleScientific reports2019
Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Information Theory Quantifiers in Cryptocurrency Time Series Analysis.Entropy (Basel, Switzerland) · 2025Article
- Collective Dynamics, Diversification and Optimal Portfolio Construction for Cryptocurrencies.Entropy (Basel, Switzerland) · 2023Article
- Age and market capitalization drive large price variations of cryptocurrencies.Scientific reports · 2023Article
- Market efficiency of cryptocurrency: evidence from the Bitcoin market.Scientific reports · 2023Article
- The transaction behavior of cryptocurrency and electricity consumption.Financial innovation · 2023Article
- Effect of twitter investor engagement on cryptocurrencies during the COVID-19 pandemic.Research in international business and finance · 2023Article
- Article
- Spatial analysis of global Bitcoin mining.Scientific reports · 2022Article
- Collective dynamics of stock market efficiency.Scientific reports · 2020Article
- A Labeling Method for Financial Time Series Prediction Based on Trends.Entropy (Basel, Switzerland) · 2020Article
- A model of the optimal selection of crypto assets.Royal Society open science · 2020Article
- A percolation model for the emergence of the Bitcoin Lightning Network.Scientific reports · 2020Article
- Complexity Changes in the US and China's Stock Markets: Differences, Causes, and Wider Social Implications.Entropy (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The efficient market hypothesis has far-reaching implications for financial trading and market stability. Whether or not cryptocurrencies are informationally efficient has therefore been the subject of intense recent investigation. Here, we use permutation entropy and statistical complexity over sliding time-windows of price log returns to quantify the dynamic efficiency of more than four hundred cryptocurrencies. We consider that a cryptocurrency is efficient within a time-window when these two complexity measures are statistically indistinguishable from their values obtained on randomly shuffled data. We find that 37% of the cryptocurrencies in our study stay efficient over 80% of the time, whereas 20% are informationally efficient in less than 20% of the time. Our results also show that the efficiency is not correlated with the market capitalization of the cryptocurrencies. A dynamic analysis of informational efficiency over time reveals clustering patterns in which different cryptocurrencies with similar temporal patterns form four clusters, and moreover, younger currencies in each group appear poised to follow the trend of their 'elders'. The cryptocurrency market thus already shows notable adherence to the efficient market hypothesis, although data also reveals that the coming-of-age of digital currencies is in this regard still very much underway.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.