| An approach to Hang Seng Index in Hong Kong stock market based on network topological statistics |
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| 作者姓名： | LI Ping WANG Binghong |
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| 作者单位： | [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 |
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| 基金项目： | Acknowledgements This work was partially supported by the National Key Basic Research Special Foundation of China and the National Natural Science Foundation of China （Grant Nos. 70171053, 70271070, 70471033 and 10472116）. |
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| 摘 要： | THE STOCK MARKET IS A STRONGLY FLUCTUATING AND INTER- ACTING COMPLEX SYSTEM. IN A STOCK MARKET, A SINGLE NUMBER, NAMELY THE STOCK INDEX, COMPACTLY CHARACTER- IZES ITS PERFORMANCE. THE FLUCTUATIONS OF THE STOCK IN- DEX BRING ABOUT AN EVOLVING COMPLEX SYSTE…
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| 关 键 词： | 香港 股票市场 恒生指数 拓扑统计学 |
| 收稿时间： | 2005-08-19 |
| 修稿时间： | 2005-08-192005-12-02 |
An approach to Hang Seng Index in Hong Kong stock market based on network topological statistics |
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| LI Ping WANG Binghong.An approach to Hang Seng Index in Hong Kong stock market based on network topological statistics[J].Chinese Science Bulletin,2006,51(5):624-629. |
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| Authors: | Ping Li Binghong Wang |
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| Institution: | (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 |
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| Abstract: | 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={S
1
S
2
S
3...}, S
i
∈ (R, r, d, D). Weighted networks of stock market are constructed 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 measurements of betweenness centrality (BC) in networks, we have at least obtained 3 highest
betweenneess 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. |
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| Keywords: | stock index complex networks topological statistics |
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