Which stocks are profitable? A network method to investigate the effects of network structure on stock returns

Chen, Kun; Luo, Peng; Sun, Bianxia; Wang, Huaiqing

doi:10.1016/j.physa.2015.05.047

Cited by 35 publications

(19 citation statements)

References 37 publications

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“…In the rise of quantitative trading, the causality and lead/lag relationships revealed by financial network analysis can be particularly interesting for trading strategy design [41,42]. Many researches have revealed stylized evidence that the network structure has a profound influence on the asset returns [43]. Taking risks into consideration, it has been found that investing in peripheries of financial networks might generate better returns over risks [44].…”

Section: Literature Review Of Financial Network Analysismentioning

confidence: 99%

Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets

et al. 2018

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Policy makings and regulations of financial markets rely on a good understanding of the complexity of financial markets. There have been recent advances in applying data-driven science and network theory into the studies of social and financial systems. Financial assets and institutions are strongly connected and influence each other. It is essential to study how the topological structures of financial networks could potentially influence market behaviors. Network analysis is an innovative method to enhance data mining and knowledge discovery in financial data. 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. 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. Results suggest that the network properties and hierarchical structures are fundamentally different for the two stock markets. The patterns embedded in the price movements are revealed and shed light on the market dynamics. Financial policymakers and regulators can gain inspiration from these findings for applications in policy making, regulations design, portfolio management, risk management, and trading.

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Section: Literature Review Of Financial Network Analysismentioning

confidence: 99%

Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets

et al. 2018

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“…Generally, two ways of segmentation are popular: one is the fixed time window [9,13,17], and the other is on the basis of the local extremum [16,27]. Unfortunately, both of them are regardless of the change of investors' strategies, which effects the movements of stocks dramatically.…”

Section: Cutting the Stock Datamentioning

confidence: 99%

“…To analyze Shanghai stock index, Zhang et al build several scale-free (or small world) networks, suggesting that the existence of hubs and the segments correlated with a given one appear in a Poisson process [16]. Chen et al suggest that centrality and modularity of a complex network based on correlation are used to detect the effect of interconnection on stock returns and industries [17]. See also [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

The Relation between Degree and Strength in the Complex Network Derived from an Individual Stock

Zhang

Luo

Deng

et al. 2016

Mathematical Problems in Engineering

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A method based on coarse-graining to construct a directed weighted complex network which models the transformation of the trading data of an individual stock is introduced. The degree (strength) distribution of derived network follows a power-law. A moderated regression equation with interaction effects of average return and out-degree (in-degree) on out-strength (in-strength) is established. Moreover, we found that the differences of nodes affect the network’s structure and average return level impacts nodes’ eigenvector centrality and pagerank, significantly.

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“…Song, Tumminello, Zhou, and Mantegna (2011) constructed a network using the Pearson correlation coefficient as a global information on the stock market and introduced a method of measuring the mutual information of the links in the network to detect the continuous dynamic changes. Chen, Luo, Sun, and Wang (2015) constructed the network using the Pearson correlation coefficient as the edge information on the SSE stock market in China and found that there is a correlation between the industry's proximity and industry return, as well as between stock centre and stock returns. Nie, Zhang, Chen, and Lv (2015) used the Pearson correlation coefficient as a network of information on the Chinese stock market and found that the stock network shows small-world characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Nie, Zhang, Chen, and Lv (2015) used the Pearson correlation coefficient as a network of information on the Chinese stock market and found that the stock network shows small-world characteristics. Chen, Luo, Sun, and Wang (2015) constructed the network using the Pearson correlation coefficient as the edge information on the SSE stock market in China and found that there is a correlation between the industry's proximity and industry return, as well as between stock centre and stock returns. Li et al (2015) used the Pearson correlation coefficient as a trading channel for Baosteel's equity information and found that there are strong simultaneous correlations between the topological metrics of trading networks that characterize the patterns of order execution and the financial variables for the stock and its warrant.…”

Section: Introductionmentioning

confidence: 99%

Tail dependence networks of global stock markets

2018

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The Pearson correlation coefficient is used by many researchers to construct complex financial networks. However, it is difficult to capture the structural characteristics of financial markets that have extreme fluctuations. To solve this problem, we resort to tail dependence networks. We first build the edge information of the stock network by adopting Pearson's correlation coefficient and the symmetrized Joe–Clayton copula model, respectively. By using the planar maximally filtered graph method, we filter the edge information, obtain Pearson's correlation coefficient and tail dependence network, and compare their efficiencies. The community structure of the constructed networks is investigated. We find that the global efficiency of tail‐dependent networks is higher than that of the Pearson correlation networks. Further analysis of the nodes in the upper‐ and lower‐tail dependence networks reveals that the European markets are more influential than Asian and African markets during a booming market and a recession market. In addition, different cliques are found in the two tail dependence networks. The finding indicates that financial risks will impact geographically adjacent markets.

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Which stocks are profitable? A network method to investigate the effects of network structure on stock returns

Cited by 35 publications

References 37 publications

Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets

Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets

The Relation between Degree and Strength in the Complex Network Derived from an Individual Stock

Tail dependence networks of global stock markets

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