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Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations

机译：价格和符号波动揭示了金融市场的介观社区结构

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摘要

The mesoscopic organization of complex systems, from financial markets to the brain, is an intermediate between the microscopic dynamics of individual units (stocks or neurons, in the mentioned cases), and the macroscopic dynamics of the system as a whole. The organization is determined by “communities” of units whose dynamics, represented by time series of activity, is more strongly correlated internally than with the rest of the system. Recent studies have shown that the binary projections of various financial and neural time series exhibit nontrivial dynamical features that resemble those of the original data. This implies that a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. Here, we explore whether the binary signatures of multiple time series can replicate the same complex community organization of the financial market, as the original weighted time series. We adopt a method that has been specifically designed to detect communities from cross-correlation matrices of time series data. Our analysis shows that the simpler binary representation leads to a community structure that is almost identical with that obtained using the full weighted representation. These results confirm that binary projections of financial time series contain significant structural information.
机译：从金融市场到大脑，复杂系统的介观组织是各个单元（股票或神经元，在上述情况下）的微观动态与整个系统的宏观动态之间的中介。组织由单位的“社区”决定，这些单位的动态性（由活动的时间序列表示）在内部比与系统的其余部分更紧密地相关。最近的研究表明，各种财务和神经时间序列的二元预测都表现出与原始数据相似的非平凡的动力学特征。这意味着将大量信息编码到这种增量的二进制投影（即符号）中。在这里，我们探索多个时间序列的二进制签名是否可以像原始加权时间序列那样复制金融市场中相同的复杂社区组织。我们采用了一种专门设计用于从时间序列数据的互相关矩阵中检测社区的方法。我们的分析表明，较简单的二进制表示形式导致的社区结构与使用全权表示形式获得的社区结构几乎相同。这些结果证实了金融时间序列的二元预测包含重要的结构信息。

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