Extracting hidden fluctuation patterns of Hang Seng stock index from network topologies

Abstract

We present a model of complex network generated from Hang Seng index (HSI) of Hong Kong stock market, which encodes stock market relevant both interconnections and interactions between fluctuation patterns of HSI in the network topologies. In the network, the nodes (edges) represent all kinds of patterns of HSI fluctuation (their interconnections). Based on network topological statistic, we present efficient algorithms, measuring betweenness centrality ( BC) and inverse participation ratio ( IPR) of network adjacency matrix, for detecting topological important nodes. We have at least obtained three uniform nodes of topological importance, and find the three nodes, i.e. 18.7% nodes undertake 71.9% betweenness centrality and closely correlate other nodes. From these topological important nodes, we can extract hidden significant fluctuation patterns of HSI. We also find these patterns are independent the time intervals scales. The results contain important physical implication, i.e. the significant patterns play much more important roles in both information control and transport of stock market, and should be useful for us to more understand fluctuations regularity of stock market index. Moreover, we could conclude that Hong Kong stock market, rather than a random system, is statistically stable, by comparison to random networks.