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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 markets are con- structed by vertices that are 16 2-symbol strings (ie 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, ie 18.7% vertices undertake 71.9% betweenness centrality of networks, showing statistical stability. These properties can not 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 syst em, is statistically stable.