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加密货币市场效率的测量与聚类分析

文章 arXiv papers · 作者: Higor Y. D. Sigaki et al.
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总结

本研究评估四百多种加密货币的信息效率如何变化。研究在滚动时间窗口内，根据对数收益率计算排列熵和统计复杂度，再将这些指标与随机打乱数据所得的结果进行比较。当某一窗口中的指标与打乱数据的结果在统计上无法区分时，研究便将该窗口中的货币视为有效率。

作者报告称，在观测期内，有超过80%的时间呈现有效率的加密货币占37%，而有效率时间不到20%的占20%。效率与市值无相关性。随时间变化的模式还将货币划分为四个聚类，每组中较新的货币似乎都呈现出与较早货币相似的模式。这些发现取决于所选指标、时间窗口、样本和效率判定标准；它们描述的是统计行为，并未直接表明交易策略能否获得收益。

核心观点

  • 研究使用排列熵和统计复杂度，在滚动窗口中估算市场效率。
  • 效率的定义是将结果与随机打乱的收益数据进行比较。
  • 报告称，样本中不同加密货币的效率持续时间差异很大。
  • 市值与测得的效率没有相关性。
  • 随时间变化的模式形成四个聚类，较新的货币似乎会呈现较早货币的模式。

标签

加密资产统计学市场微观结构
全文
# 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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。