However, the majority of existing Finance Big Data networks are built as undirected networks without information on the influence directions among prices. The recent financial network analysis approach reveals that the topologies of financial markets have an important influence on market dynamics. We observe that our solutions help unravel complex behaviours and resonate well with study participants in addressing their needs in the context of correlation analysis in finance. We evaluate our approach by checking the validity of the layouts produced, presenting a number of analysis stories, and through a user study. As part of our contribution, we also present behaviour profiles to help guide future users of our approach. We devise interaction techniques coupled with context‐sensitive auxiliary views to support the analysis of subsets of correlation networks. Our solution combines concepts from multi‐dimensional scaling, weighted complete graphs and threshold networks to present interactive, animated displays which use proximity as a visual metaphor for correlation and animation stability to encode correlation stability. We conduct a series of interviews and review the financial correlation analysis literature to guide our design. In this paper, we propose a visual analytics framework for the interactive analysis of relations and structures in dynamic, high‐dimensional correlation data. In order to manage investment risk, in‐depth analysis of changing correlations is needed, with both high and low correlations between financial assets (and groups thereof) important to identify. The analysis of financial assets’ correlations is fundamental to many aspects of finance theory and practice, especially modern portfolio theory and the study of risk. Financial policymakers and regulators can gain inspiration from these findings for applications in policy making, regulations design, portfolio management, risk management, and trading. The patterns embedded in the price movements are revealed and shed light on the market dynamics. Results suggest that the network properties and hierarchical structures are fundamentally different for the two stock markets. In this study, two major markets of the most influential economies, China and the United States, are systematically studied from the perspective of financial network analysis. With the help of complex network theory, the topological network structures of a market can be extracted to reveal hidden information and relationships among stocks. Network analysis is an innovative method to enhance data mining and knowledge discovery in financial data. It is essential to study how the topological structures of financial networks could potentially influence market behaviors. Financial assets and institutions are strongly connected and influence each other. There have been recent advances in applying data-driven science and network theory into the studies of social and financial systems. Policy makings and regulations of financial markets rely on a good understanding of the complexity of financial markets.
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. Furthermore, compared with stock price correlations, the similarity of main products is simpler and more intuitive. Main product similarity, used as supplementary information for stock relativity research, plays a role similar to stock price correlations in certain scenarios. The two networks exhibit a high degree of similarity in degree, weighted degree and community division. 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. 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 20 and then constructing stock price correlation threshold networks and co-main product networks to analyze the similarities in their structures. 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. 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.
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