Abstract
Considerable research has been done on the complex stock market, however, there is very little systematic work on the impact of crisis on global stock markets. For filling in these gaps, we propose a complex network method, which analyzes the effects of the 2008 global financial crisis on global main stock index from 2005 to 2010. Firstly, we construct three weighted networks. The physics-derived technique of minimum spanning tree is utilized to investigate the networks of three stages. Regional clustering is found in each network. Secondly, we construct three average threshold networks and find the small-world property in the network before and during the crisis. Finally, the dynamical change of the network community structure is deeply analyzed with different threshold. The result indicates that for large thresholds, the network before and after the crisis has a significant community structure. Though this analysis, it would be helpful to investors for making decisions regarding their portfolios or to regulators for monitoring the key nodes to ensure the overall stability of the global stock market.
Highlights
The global stock market is a typical complex system
There is little research on national stock data from a large system perspective. For filling in these gaps, we propose a complex network method, which analyzes the effects of the 2008 global financial crisis on global main stock index from 2005 to 2010
This paper focuses on the small world characteristics of the global stock index network from the view of clustering coefficient and characteristic path length comparing with the random network with the same scale
Summary
The global stock market is a typical complex system. The complex network is a powerful tool to deal with complex system [1,2,3,4]. It is found that there are many rules and characteristics in the financial network, such as the topological properties, the faction structure and scale-free characteristic The discovery of these rules is of great value to the healthy development of stock market and the risk control of investors. Nobi A et al [21] studied the change of the network structure in 2000–2012 based on the correlation between the global index and the Korean local index. There is little research on national stock data from a large system perspective For filling in these gaps, we propose a complex network method, which analyzes the effects of the 2008 global financial crisis on global main stock index from 2005 to 2010. These research will give a helpful exploration to uncover the mechanism of effect of global financial risks on the global stock market.
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