Abstract
In this paper we study some properties of the Russian stock market with the application of network approaches and data-driven science. Complex networks theory allows us to construct and analyse topological network structures of the market. Among the important information which is possible to acquire from it is the relationships between stocks returns with the analysis of hidden information and market dynamics. This paper is focused on the analysis of the market network dynamics over time. We construct market networks for 75 consecutive overlapping 250-day periods to analyze the dynamics of the structural properties of the market rank-correlation-based network. Degree distribution and maximum clique size are considered as the important structural characteristics of the market network. In our opinion these parameters are the essential graph attributes and give insight into Russian financial market structure.
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