Rocchi Jacopo, Tsui Enoch Yan Lok, Saad David
Nonlinearity and Complexity Research Group, Aston University, Birmingham, B4 7ET, United Kingdom.
Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
PLoS One. 2017 May 25;12(5):e0176764. doi: 10.1371/journal.pone.0176764. eCollection 2017.
To identify emerging interdependencies between traded stocks we investigate the behavior of the stocks of FTSE 100 companies in the period 2000-2015, by looking at daily stock values. Exploiting the power of information theoretical measures to extract direct influences between multiple time series, we compute the information flow across stock values to identify several different regimes. While small information flows is detected in most of the period, a dramatically different situation occurs in the proximity of global financial crises, where stock values exhibit strong and substantial interdependence for a prolonged period. This behavior is consistent with what one would generally expect from a complex system near criticality in physical systems, showing the long lasting effects of crashes on stock markets.
为了识别交易股票之间新出现的相互依存关系,我们通过观察富时100指数成份股公司2000年至2015年期间的每日股价,来研究这些股票的行为。利用信息理论方法的强大功能来提取多个时间序列之间的直接影响,我们计算了股价之间的信息流,以识别几种不同的状态。虽然在大部分时间里检测到的信息流较小,但在全球金融危机临近时出现了截然不同的情况,此时股价在较长一段时间内表现出强烈且显著的相互依存关系。这种行为与人们通常对物理系统中接近临界状态的复杂系统的预期一致,显示了股市暴跌的长期影响。