ABSTRACT The recent financial network analysis approach reveals that the topologies of financial markets have an important influence on market dynamics. However, the majority of existing Finance Big Data networks are built as undirected networks without information on the influence directions among prices. Rather than understanding the correlations, this research applies the Granger causality test to build the Granger Causality Directed Network for 33 global major stock market indices. The paper further analyzes how the markets influence one another by investigating the directed edges in the different filtered networks. The network topology that evolves in different market periods is analyzed via a sliding window approach and Finance Big Data visualization. By quantifying the influences of market indices, 33 global major stock markets from the Granger causality network are ranked in comparison with the result based on PageRank centrality algorithm. Results reveal that the ranking lists are similar in both approaches where the U.S. indices dominate the top position followed by other American, European, and Asian indices. The lead-lag analysis reveals that there is lag effects among the global indices. The result sheds new insights on the influences among global stock markets with implications for trading strategy design, global portfolio management, risk management, and markets regulation.

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全球股市如何相互影响？来自金融大数据和格兰杰因果导向网络的证据

摘要 最近的金融网络分析方法表明，金融市场的拓扑结构对市场动态具有重要影响。然而，现有的大多数金融大数据网络都是无向网络，没有关于价格之间影响方向的信息。本研究并未了解相关性，而是应用格兰杰因果关系检验为 33 个全球主要股票市场指数构建格兰杰因果关系导向网络。本文通过调查不同过滤网络中的有向边进一步分析了市场如何相互影响。通过滑动窗口方法和金融大数据可视化分析不同市场时期演变的网络拓扑。通过量化市场指数的影响，将来自格兰杰因果网络的 33 个全球主要股票市场与基于 PageRank 中心性算法的结果进行比较。结果显示，两种方法的排名列表相似，其中美国指数占主导地位，其次是其他美国、欧洲和亚洲指数。超前滞后分析表明，全球指数之间存在滞后效应。结果对全球股票市场之间的影响提供了新的见解，并对交易策略设计、全球投资组合管理、风险管理和市场监管产生了影响。和亚洲指数。超前滞后分析表明，全球指数之间存在滞后效应。结果对全球股票市场之间的影响提供了新的见解，对交易策略设计、全球投资组合管理、风险管理和市场监管都有影响。和亚洲指数。超前滞后分析表明，全球指数之间存在滞后效应。结果对全球股票市场之间的影响提供了新的见解，并对交易策略设计、全球投资组合管理、风险管理和市场监管产生了影响。