An approach to Hang Seng Index in Hong Kong stock market based on network topological statistics  

作　　者：LI Ping WANG Binghong 

机构地区：[1]Department of Basic Sciences, Nanjing Institute of Technology, Nanjing 210013, China [2]Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei 230026, China

出　　处：《Chinese Science Bulletin》2006年第5期624-629,共6页

基　　金：partially supported by the National Key Basic Research Special Foundation of China;the National Natural Science Foundation of China(Grant Nos.70171053,70271070,70471033 and 10472116).

摘　　要：Using homogenous partition of coarse graining process, the time series of Hang Seng Index (HSI) in Hong Kong stock market is transformed into discrete symbolic sequences S={S1S2S3…}, Si∈(R, r, d, D). Weighted networks of stock market are con- structed by vertices that are 16 2-symbol strings (i.e. 16 patterns of HSI variations), and encode stock market relevant information about interconnections and interactions between fluctuation patterns of HSI in networks topology. By means of the measure- ments of betweenness centrality (BC) in networks, we have at least obtained 3 highest betweenness centrality uniform vertices in 2 order of magnitude of time subinterval scale, i.e. 18.7% vertices undertake 71.9% betweenness centrality of networks, showing statistical stability. These properties cannot be found in random networks; here vertices almost have iden- tical betweenness centrality. By comparison to ran- dom networks, we conclude that Hong Kong stock market, rather than a random system, is statistically stable.Using homogenous partition of coarse graining process, the time series of Hang Seng Index （HSI） in Hong Kong stock market is transformed into discrete symbolic sequences S={S1S2S3…}, Si∈ （R, r, d, D）. Weighted networks of stock market are constructed by vertices that are 16 2-symbol strings （i.e. 16 patterns of HSl variations）, and encode stock market relevant information about interconnections and interactions between fluctuation patterns of HSl in networks topology. By means of the measurements of betweenness centrality （BC） in networks, we have at least obtained 3 highest betweenness centrality uniform vertices in 2 order of magnitude of time subinterval scale, i.e. 18.7% vertices undertake 71.9% betweenness centrality of networks, showing statistical stability. These properties cannot be found in random networks; here vertices almost have identical betweenness centrality. By comparison to random networks, we＇conclude that Hong Kong stock market, rather than a random system, is statistically stable.

关 键 词：香港 股票市场 恒生指数 拓扑统计学 

分 类 号：F832.5[经济管理—金融学]