Correlation network analysis for multi-dimensional data in stocks market

• Stocks network based on opening, highest, lowest, and closing prices is introduced.
• Escoufier’s RV coefficient is used to measure the similarity among stocks.
• Information in multi-dimensional network is filtered using minimal spanning tree.
• The topological properties of stocks are analyzed by using centrality measures.
• 30 Dow-Jones stocks are used to illustrate the advantage of RV coefficients network.

This paper shows how the concept of vector correlation can appropriately measure the similarity among multivariate time series in stocks network. The motivation of this paper is (i) to apply the RV   coefficient to define the network among stocks where each of them is represented by a multivariate time series; (ii) to analyze that network in terms of topological structure of the stocks of all minimum spanning trees, and (iii) to compare the network topology between univariate correlation based on rr and multivariate correlation network based on RV coefficient.