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Similarities between stock price correlation networks and co-main product networks: Threshold scenarios

Yanli Wang, Huajiao Li, Jianhe Guan and Nairong Liu

Physica A: Statistical Mechanics and its Applications, 2019, vol. 516, issue C, 66-77

Abstract: Because of the high yields and high risks associated with the stock market, investors can hold diversified portfolios with low relativity of stocks to reduce unsystematic risk. The current literature analyzes single factors affecting the relativity of stocks, but in this paper, we analyze the correlations between different factors to provide multiple perspectives of and about investment portfolios. This study analyzes the relationships between the similarities of the main products of listed companies and the varying degrees of correlations of stock price by examining different threshold scenarios of the energy industry between 2012 and 2016 and then constructing stock price correlation threshold networks and co-main product networks to analyze the similarities in their structures. The results indicate that two factors are significantly correlated in 97.5% of the scenarios and that these factors are the most strongly correlated when the threshold is between 0.5 and 0.7. The two networks exhibit a high degree of similarity in degree, weighted degree and community division. Main product similarity, used as supplementary information for stock relativity research, plays a role similar to stock price correlations in certain scenarios. Furthermore, compared with stock price correlations, the similarity of main products is simpler and more intuitive. This paper proposes a new method to study stock relativity based on different threshold scenarios; thus, it could serve as a reference for investors when developing portfolio strategies from multiple perspectives.

Keywords: Structural correlations; Threshold scenarios; Co-main product network; Stock price correlation threshold network; Investment portfolio (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:516:y:2019:i:c:p:66-77

DOI: 10.1016/j.physa.2018.09.154

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