Measuring and Clustering Cryptocurrency Market Efficiency Over Time

Summary

This study assesses how informational efficiency varies across more than four hundred cryptocurrencies. It calculates permutation entropy and statistical complexity from log returns in sliding time windows, then compares those measures with results from randomly shuffled data. A currency is treated as efficient in a window when its measures are statistically indistinguishable from the shuffled-data values.

The authors report that 37% of the cryptocurrencies were efficient in more than 80% of the observed time, while 20% were efficient in less than 20%. Efficiency was not correlated with market capitalization. The time-varying patterns also grouped currencies into four clusters, with younger currencies in each group appearing to follow patterns seen in older ones. These findings depend on the chosen measures, windowing, sample, and efficiency criterion; they describe statistical behavior and do not directly show whether a trading strategy can earn returns.

Key ideas

• The study uses permutation entropy and statistical complexity to estimate efficiency in rolling windows.
• Efficiency is defined by comparison with shuffled return data.
• Reported efficiency persistence varies substantially across the sampled cryptocurrencies.
• Market capitalization was not correlated with the measured efficiency.
• Temporal patterns formed four clusters, with younger currencies appearing to follow older ones.

Tags

# Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market


# Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.