在加密货币的信息理论量化器时间序列分析分析
Micaela Suriano1,2, Leonidas Facundo Caram2, Cesar Caiafa3
1Departamento de Hidráulica, Facultad de Ingeniería, Universidad de Buenos Aires, Av. Las Heras 2214, Buenos Aires C1127AAR, Argentina.
加密货币时间序列在较短的数据集 (不到两年) 中表现出混乱的行为,在较长的数据集中表现出随机的行为. 白皮书中的项目叙述不会显著影响市场动态,建议专注于投资的实时指标.
科学领域:
- 量化金融 量化金融
- 复杂性科学 复杂性科学
- 数据科学数据科学数据科学
背景情况:
- 加密货币市场表现出复杂的时间动态.
- 了解金融时间序列中的随机性和混乱对于市场分析至关重要.
- 现有的研究往往忽视了项目叙述和市场行为之间的相互作用.
研究的目的:
- 通过信息理论的措施来研究加密货币时间序列的时间演变.
- 在加密货币价格数据中区分混乱和随机行为.
- 评估白皮书内容对加密货币市场动态的影响.
主要方法:
- 将复杂性,和费舍尔信息等信息措施应用于176个每日加密货币收盘价格时间序列.
- 利用复杂性-性因果平面 (CECP) 分析来分类时间序列行为.
- 使用自然语言处理 (NLP) 进行白皮书分析和聚类,然后进行时间序列动态比较.
主要成果:
- 两年以下的加密货币时间序列表现出混乱的行为;超过两年的序列表现出随机的行为,通常类似于彩色噪音 (k在0和2之间).
- 基于白皮书内容的NLP分析揭示了四个不同的集群.
- 在白皮书集群和时间序列动态之间没有发现显著的相关性.
结论:
- 随着数据长度的增加,加密货币市场行为从混乱过渡到随机变化.
- 项目叙述,正如白皮书所反映的那样,似乎不会决定短期到中期的市场动态.
- 投资策略应该优先考虑实时信息指标,而不是静态白皮书分析.
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