Sectoral Analysis of the US Stock Market through Complex Networks
Purpose: This study was carried out to analyze the structure of the aggregated network at the level of economic sectors and to reveal the central/peripheral sectors. Design/Methodology/Approach: The study uses the method of complex networks, with the two-step procedure employed to construct the network of economic sectors. First, the MST approach is utilized based on the cross-correlation of 496 stock price returns of the S&P500 Index. Then, the network is aggregated at the level of economic sectors. In addition, to analyze the graph, the network theory, multi-dimensional scaling (MDS), and relative importance approaches are employed. Findings: The results indicate that the sectoral network has a core/periphery structure. Based on the centrality measures, the ranking of sectors is provided. Of the 11 sectors, 3 are classified as central nodes, 4 as peripheral nodes, and the remaining 4 are classified as intermediate. In addition, the network configuration analysis demonstrates that the graph consists of two parts with a star-like structure, connected through the industrials sector. Practical Implications: An analysis of the cross-correlation network of aggregated assets at the level of economic sectors can be applied to ascertain the direction of stock price movements in the stock market. The division of sectors in the network into central and peripheral nodes has important implications for the management of an optimal portfolio of stocks. Originality/value: This study contributes to complex network theory and portfolio strategy design. A unique procedure is proposed to construct the network of economic sectors using the MST-based approach. Detection of the stock market network structure is vital for investors and regulators alike.