中国股票市场信息流关联网络——基于转移熵的实证研究

姓名
邮箱
手机号码
标题
留言内容
验证码

doi:10.15918/j.jbitss1009-3370.2018.2124

邬松涛¹,
何建敏¹

1.
东南大学经济管理学院, 江苏南京 211189

基金项目:

国家自然科学基金资助项目（71671037，71371051）;东南大学优秀博士学位论文培育基金资助项目（YBJJ1569）;中央高校基本科研业务费专项资金资助;江苏省普通高校研究生科研创新计划资助项目（KYZZ16_0139）;教育部人文社会科学研究规划基金资助项目（16YJA630026）;江苏省社会科学基金青年项目资助（15GLC003）

详细信息

中图分类号:F830.91

Information Flow Correlation Network to China's Stock Market—Empirical Study based on Transfer Entropy

WU Songtao¹,
HE Jianmin¹

1.
School of Economics and Management, Southeast University, Nanjing Jiangsu 211189, China

摘要:以中国A股市场为研究对象，对2005-2016年股票间信息流关联网络进行研究。利用转移熵对股票间非对称、非线性信息流进行测度；以此为关联性指标，基于阈值法和滚动时间窗方法构建随时间演化的股票间有向加权信息流关联网络。运用平均最短路径和聚集系数等复杂网络理论，以及蓄意攻击和随机故障等分析方法，对信息流关联网络的宏观拓扑特征、关键节点、小世界性等进行分析。研究发现：在市场总体行情剧烈波动和相对平稳时期，股票间皆可能存在强烈的信息交互。随时间演化的网络拓扑结构具有结构性差异，部分时期的信息流关联网络具有小世界效应，同时存在影响范围和影响力极大的关键节点股票，是潜在的风险窗口期。蓄意攻击和随机故障下的小世界性分析表明，关键风险节点股票在控制全局风险的同时可能会导致局部风险加剧。
- 转移熵/
- 信息流/
- 股票市场/
- 关联网络/
- 小世界性
Abstract:Information flow network, which was constructed on information flows between stocks traded in China's stock market over the period from 2005 to 2016, was studied. Transfer entropy was employed to quantify asymmetric and non-linear information flows between stocks. Then directed-weighted information flow networks were constructed by employing a threshold value and sliding window method. Average path length and clustering coefficient borrowed from complex theory and methods such as intentional attack and random failure were used to analyze the characteristics of networks, such as network topologies, key nodes and small-worldness. Studies showed that there was intense information interactions between stocks during both volatile and relative stable periods in question, and structural differences are observed among time-varying information flow networks. Changing characteristics of network topologies show that information flow networks at certain calendar dates showing effects of small-world and with presence of nodes exhibiting extreme influences at the same time. Furthermore, small-worldness analysis based on intentional attack and random failure imply that risk control based on key stocks will reduce global risk but may intensify local risk contagion.
- transfer entropy/
- information flow/
- stock market/
- correlation network/
- small-worldness

[1]	LOH L. Co-movement of Asia-Pacific with European and US stock market returns:a cross-time-frequency analysis[J]. Research in International Business and Finance,2013,29:1-13.
[2]	宁建楠,易文德. 金融危机对中国股市各行业板块间相依结构的影响[J]. 系统工程,2015,33(11):10-17.
[3]	BEKAERT G,EHRMANN M,FRATZSCHER M,et al. The global crisis and equity market contagion[J]. The Journal of Finance,2014,69(6):2597-2649.
[4]	ROLL R. R²[J]. The Journal of Finance,1988,43:541-566.
[5]	GRANGER C W J. Investigating causal relations by econometric models and cross-spectral methods[J]. Econometrica:Journal of the Econometric Society,1969:424-438.
[6]	MARSCHINSKI R,KANTZ H. Analysing the information flow between financial time series[J]. The European Physical Journal B,2002,30(2):275-281.
[7]	KWON O,YANG J S. Information flow between stock indices[J]. Europhysics Letters,2008,82(6):124-130.
[8]	AGHABOZORGI S,TEH Y W. Stock market co-movement assessment using a three-phase clustering method[J]. Expert Systems with Applications,2014,41(4):1301-1314.
[9]	OH G,OH T,KIM H,et al. An information flow among industry sectors in the Korean stock market[J]. Journal of the Korean Physical Society,2014,65(12):2140-2146.
[10]	BEKIROS S,NGUYEN D K,JUNIOR L S,et al. Information diffusion,cluster formation and entropy-based network dynamics in equity and commodity markets[J]. European Journal of Operational Research,2017,256(3):945-961.
[11]	JUNIOR L S,MULLOKANDOV A,KENETT D Y. Dependency relations among international stock market indices[J]. Journal of Risk and Financial Management,2015,8(2):227-265.
[12]	KWON O,OH G. Asymmetric information flow between market index and individual stocks in several stock markets[J]. Europhysics Letters,2012,97(2):28007.
[13]	WATTS D J,STROGATZ S H. Collective dynamics of ‘small-world’ networks[J]. Nature,1998,393(6684):440-442.
[14]	BARABáSI A L,ALBERT R. Emergence of scaling in random networks[J]. Science,1999,286(5439):509-512.
[15]	乔海曙,杨蕾. 沪深300指数成分股系统性风险贡献分析——基于股票指标关联网络的研究[J]. 中南大学学报:社会科学版,2016,22(3):114-123.
[16]	黄玮强,庄新田,姚爽. 我国股票关联网络拓扑结构与网络弹性关系研究[J]. 系统管理学报,2015,24(1):71-77.
[17]	黄玮强,庄新田,姚爽. 中国股票关联网络拓扑性质与聚类结构分析[J]. 管理科学,2008,21(3):94-103.
[18]	刘井建,焦怀东,南晓莉. 危机冲击背景下股票市场风险联动非线性[J]. 系统工程,2015,33(12):16-22.
[19]	SCHREIBER T. Measuring information transfer[J]. Physical Review Letters,2000,85(2):461-464.
[20]	KIM Y,KIM J,YOOK S H. Information transfer network of global market indices[J]. Physica A:Statistical Mechanics and Its Applications,2015,430:39-45.
[21]	徐梅,黄超. 基于符号时间序列方法的金融收益分析与预测[J]. 中国管理科学,2011,19(5):1-9.
[22]	SHI W,SHANG P. Cross-sample entropy statistic as a measure of synchronism and cross-correlation of stock markets[J]. Nonlinear Dynamics,2013,71(3):539-554.
[23]	SANDOVAL L. Structure of a global network of financial companies based on transfer entropy[J]. Entropy,2014,16(8):4443-4482.
[24]	DIMPFL T,PETER F J. Using transfer entropy to measure information flows between financial markets[J]. Studies in Nonlinear Dynamics and Econometrics,2013,17(1):85-102.
[25]	JALILI M. Error and attack tolerance of small-worldness in complex networks[J]. Journal of Informetrics,2011,5(3):422-430.
[26]	庄新田,张鼎,苑莹,等. 中国股市复杂网络中的分形特征[J]. 系统工程理论与实践,2015(2):273-282.
[27]	张来军,杨治辉,路飞飞. 基于复杂网络理论的股票指标关联性实证分析[J]. 中国管理科学,2014,22(12):85-92.
[28]	CLAUSET A,SHALIZI C R,NEWMAN M E J. Power-law distributions in empirical data[J]. Siam Review,2009,51(4):661-703.

[1]	严翔, 黄永春, 柏建成, 邹晨.长江经济带绿色创新效率的空间关联网络结构及驱动因素. bob手机在线登陆学报（社会科学版）, 2021, 23(6): 72-83.doi:10.15918/j.jbitss1009-3370.2021.4309
[2]	张彪, 李捷.董事会组成与温室气体排放信息披露的关联性——基于2011—2016年CDP中国报告的实证研究. bob手机在线登陆学报（社会科学版）, 2019, (2): 38-47.doi:10.15918/j.jbitss1009-3370.2019.1354
[3]	王耀君, 高扬, 王耀青.基于网络搜索指数的股票市场微观结构特征. bob手机在线登陆学报（社会科学版）, 2018, (5): 54-62.doi:10.15918/j.jbitss1009-3370.2018.2030
[4]	高扬, 王超, 刘超.境外投资者持股对中国股票市场信息不对称的影响. bob手机在线登陆学报（社会科学版）, 2017, (5): 104-112.doi:10.15918/j.jbitss1009-3370.2017.1872
[5]	方胜, 卢新生, 刘笑.国际原油价格变化对中美能源股影响的断点研究. bob手机在线登陆学报（社会科学版）, 2017, (4): 36-42.doi:10.15918/j.jbitss1009-3370.2017.1922
[6]	李成, 郭哲宇, 王瑞君.中国货币政策与股票市场溢出效应研究——基于VAR-GARCH-BEKK模型. bob手机在线登陆学报（社会科学版）, 2014, (4): 83-91.
[7]	尹秋菊, 黄洁萍, 冯丽.电动汽车技术转移网络结构及其社会资本. bob手机在线登陆学报（社会科学版）, 2013, (2): 43-47.
[8]	卞曰瑭, 张岳锋, 许露.股市交易网络模型构建及其稳定性研究. bob手机在线登陆学报（社会科学版）, 2013, (2): 66-70,76.
[9]	陈洁, 张定胜.国际股市对上证综指的影响. bob手机在线登陆学报（社会科学版）, 2012, (1): 32-37.
[10]	胡秋灵, 张苏凤, 王宁.可转债市场与股票市场间的溢出效应研究. bob手机在线登陆学报（社会科学版）, 2011, (3): 55-59.
[11]	彭兴庭.我国房地产市场与股票市场的波动相关性研究. bob手机在线登陆学报（社会科学版）, 2011, (5): 39-43.
[12]	姚宁.损益价差：基于中国股票市场的实证研究. bob手机在线登陆学报（社会科学版）, 2010, (3): 10-12.
[13]	张秀梅, 杨艳.关联方转移利益与审计意见尧审计独立性的关系研究. bob手机在线登陆学报（社会科学版）, 2009, (5): 72-76.
[14]	姚绍文.模糊环境下基于信息熵风险度量的投资组合模型. bob手机在线登陆学报（社会科学版）, 2008, (6): 59-61.
[15]	张秀梅.关联方转移利益与盈余管理、大股东控制力关. bob手机在线登陆学报（社会科学版）, 2008, (4): 3-6.
[16]	王春峰, 孙永亮, 房振明, 李晔.订单流不平衡对股票价格的冲击效应实证研究. bob手机在线登陆学报（社会科学版）, 2007, (2): 71-75.
[17]	房振明, 王春峰, 李晔.股票与权证市场信息非对称特征比较研究. bob手机在线登陆学报（社会科学版）, 2007, (1): 82-85.
[18]	王树钰, 田华.中国股票市场的多重分形实证分析. bob手机在线登陆学报（社会科学版）, 2006, (2): 71-73.
[19]	房振明, 王春峰.上海股票市场收益日内效应的研究. bob手机在线登陆学报（社会科学版）, 2004, (3): 38-41.
[20]	郭明院.互联网与知识转移. bob手机在线登陆学报（社会科学版）, 2000, (2): 48-49.

点击查看大图

计量

文章访问数:498
HTML全文浏览量:1
PDF下载量:197
被引次数:0

留言板

中国股票市场信息流关联网络——基于转移熵的实证研究

doi:10.15918/j.jbitss1009-3370.2018.2124

Information Flow Correlation Network to China's Stock Market—Empirical Study based on Transfer Entropy

计量

中国股票市场信息流关联网络——基于转移熵的实证研究

doi:10.15918/j.jbitss1009-3370.2018.2124

1. 东南大学经济管理学院, 江苏南京 211189

English Abstract

Information Flow Correlation Network to China's Stock Market—Empirical Study based on Transfer Entropy

1. School of Economics and Management, Southeast University, Nanjing Jiangsu 211189, China

目录

作者相关

写作规范

友情链接

留言板

中国股票市场信息流关联网络——基于转移熵的实证研究

doi:10.15918/j.jbitss1009-3370.2018.2124

Information Flow Correlation Network to China's Stock Market—Empirical Study based on Transfer Entropy

计量

出版历程

中国股票市场信息流关联网络——基于转移熵的实证研究

doi:10.15918/j.jbitss1009-3370.2018.2124

1. 东南大学 经济管理学院, 江苏 南京 211189

English Abstract

Information Flow Correlation Network to China's Stock Market—Empirical Study based on Transfer Entropy

1. School of Economics and Management, Southeast University, Nanjing Jiangsu 211189, China

目录

作者相关

写作规范

友情链接

1. 东南大学经济管理学院, 江苏南京 211189