A social network approach to change detection in the interdependence structure of global stock markets

A novel method is proposed to rank the stock indices from across the globe to capture changes in the dominance of an index with respect to other indices. A correlation-based network structure is formulated and centrality measures are used to track these changes. Temporal evolution of the minimum spanning tree derived from the network of 93 stock indices worldwide has been analyzed with data from Bloomberg for the 5-year period from year 2006 through 2010. Measures are suggested for identifying dominant stock indices in the global stock market. It is investigated how the stock market turbulence can be detected by measuring the relative change in the ranks of the stock indices and in the network centralization of the emergent network structure. The study reveals how inclusion of abstract non-living entities such as stock indices in the social network analysis framework can capture the latent interdependence as manifested in the stock market. The chosen period of study encompassed the behavioral change in the stock market network before and after the collapse of Lehman Brothers in the USA, revealing interesting counter-intuitive findings that the turbulence following the collapse of Lehman Brothers had a structure-loosening impact on the global stock market.