Abstract：Daily data collected from Shanghai 50 Index constituent stocks from January 4, 2005 to December 31, 2008 is divided into three stages: bear I, bull and bear II. We study dynamic topology of stock correlation networks in each stage by using the minimal spannin tree (MST), hierarchical tree (HT) and main network property measures. The results show that: Industrial clustering exists and becomes more and more obvious in stock market; Manufacturing industry turns into the absolute center in bull market, which lasts until bear II market; Internal stocks of finance & insurance industry and steelmaking industry always maintain a high correlation, and the stocks of parent company and subsidiary and the stocks of the cross holdings companies are also close to each other; In addition, the main network property measures reveal that the structure of the stock market’s correlation network is closer but worse in bull market than in bear markets.
谢赤, 边慧东, 王纲金. 牛熊市视角下股票关联网络动态拓扑结构研究[J]. 复杂系统与复杂性科学, 2017, 14(1): 66-74.
XIE Chi, BIAN Huidong, WANG Gangjin. Dynamic Topology of Stock Correlation Networks from the Bull and Bear Perspective: a Case of Shanghai 50 Index[J]. Complex Systems and Complexity Science, 2017, 14(1): 66-74.
[1] Mantegna R N. Hierarchical structure in financial markets[J].The European Physical Journal B, 1999, 11(1): 193-197. [2] Tabak B M, Serra T R, Cajueiro D O. Topological properties of stock market networks: the case of Brazil[J].Physica A, 2010, 389(16): 3240-3249. [3] Kwapień J, Gworek S, Drod S, et al. Analysis of a network structure of the foreign currency exchange market[J].Journal of Economic Interaction and Coordination, 2009, 4(1): 55-72. [4] Tabak B M, Serra T R, Cajueiro D O. The expectation hypothesis of interest rates and network theory: the case of Brazil[J].Physica A, 2009, 388(7): 1137-1149. [5] Tabak B M, Serra T R, Cajueiro D O. Topological properties of commodities networks[J].The European Physical Journal B, 2010, 74(2): 243-249. [6] Wang G J, Xie C. Correlation structure and dynamics of international real estate securities markets: a network perspective[J].Physica A, 2015, 424(8): 176-193. [7] Matesanz D, Ortega G J. Sovereign public debt crisis in Europe. a network analysis[J].Physica A, 2015, 436(20):756-766. [8] Zhong T, Peng Q, Wang X, et al. Novel indexes based on network structure to indicate financial market[J].Physica A:Statistical Mechanics &Its Applications,2015,443:583-594. [9] 黄飞雪, 赵昕, 侯铁珊. 基于最小生成树的上证50指数分层结构[J].系统工程, 2009, 27(1): 71-76. Huang FeiXue, Zhao Xin, Hou TieShan. Index hierarchical structure of Shanghai 50 stock based on minimum spanning tree[J].Systems Engineering, 2009, 27(1): 71-76. [10] Huang W Q, Zhuang X T, Yao S. A network analysis of the Chinese stock market[J].Physica A, 2009, 388(14): 2956-2964. [11] 尹群耀, 何建敏, 卞曰瑭, 等. 基于STSA的中国股市的聚集效应研究——以上证50指数为例[J].系统工程, 2013, 31(1): 10-17. Yin QunYao, He JianMin, Bian YueTang, et al. Aggregation effect of the China stock market based on STAT method: Take SSE 50 Index as an example[J].Systems Engineering, 2013, 31(1): 10-17. [12] Mai Y, Chen H, Meng L. An analysis of the sectorial influence of CSI300 stocks within the directed network[J].Physica A, 2014, 396(2): 235-241. [13] Tu C. Cointegration-based financial networks study in Chinese stock market[J].Physica A, 2014, 402(10): 245-254. [14] Yang R, Li X, Zhang T. Analysis of linkage effects among industry sectors in China’s stock market before and after the financial crisis[J].Physica A, 2014, 411(19): 12-20. [15] Chen H, Mai Y, Li S P. Analysis of network clustering behavior of the Chinese stock market[J].Physica A, 2014, 414(22): 360-367. [16] 吴翎燕, 韩华, 宋宁宁. 基于相关系数和最佳阈值的股票网络模型构建[J].复杂系统与复杂性科学, 2013, 10(4): 49-55. Wu LingYan, Han Hua, Song NingNing. The construction of stock network model based on correlation coefficient and optimal threshold[J].Complex Systems and Complexity Science, 2013, 10(4): 49-55. [17] John Y C. The new palgrave dictionary of money and finance[J].Journal of Economic Literature, 1994, 32(2): 667-673. [18] Lindahl-Stevens M. Redefining bull and bear markets[J].Financial Analysts Journal, 1980, 36(6): 76-77. [19] 王林, 张婧婧. 复杂网络的中心化[J].复杂系统与复杂性科学, 2006, 3(1): 13-20. Wang Lin, Zhang JingJing. Centralization of complex networks[J].Complex Systems and Complexity Science, 2006, 3(1): 13-20. [20] Wang G J, Xie C, Han F, et al. Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree[J].Physica A, 2012, 391(16): 4136-4146. [21] Onnela J P, Chakraborti A, Kaski K, et al. Dynamics of market correlations: taxonomy and portfolio analysis[J].Physical Review E, 2003, 68(5): 056110. [22] Kwapien J, Gworek S, Drozdz S. Structure and evolution of the foreign exchange networks[J].Acta Physica Polonica B, 2009, 40(1):175-194. [23] Onnela J P, Chakraborti A, Kaski K, et al. Dynamic asset trees and portfolio analysis[J].The European Physical Journal B, 2002, 30(3): 285-288.
