20.500.12556/DKUM-90126 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. cryptocurrency market efficiency financial trading social physics kriptovalute tržna učinkovitost finančno trgovanje fizika družbe true false true Angleški jezik Slovenski jezik Znanstveno delo 2024-08-23 04:12:38 2025-02-26 10:17:38 2025-02-27 03:11:28 0000-00-00 00:00:00 2019 0 0 str. 1-9 št. članka 440 Letn. 9 2019 0000-00-00 Zaloznikova Objavljeno apc 2018-10-29 2018-12-13 2019-02-05 53 24459272 10.1038/s41598-018-37773-3 2045-2322 RAZ_Sigaki_Higor_Y._D._2019.pdf RAZ_Sigaki_Higor_Y._D._2019.pdf 1 8141B9A57AB68E9CA1B5576F21D9DE2B 076d8cf22c96cf65097472fd06616eb79a30c076fbbe7ef909732d3f0100176a 6f4dd1ed-f422-11ef-a5fa-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=188836 https://doi.org/10.1038/s41598-018-37773-3 0 7c70cbf8-f422-11ef-a5fa-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=188837 Fakulteta za naravoslovje in matematiko 0 0 0