Sector dominance ratio analysis of financial markets

Uechi, Lisa; Akutsu, Tatsuya; Stanley, H. Eugene; Marcus, Alan J.; Kenett, Dror Y.

doi:10.1016/j.physa.2014.11.055

Cited by 26 publications

(11 citation statements)

References 45 publications

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“…On the other hand, the partial correlation analysis, which is a powerful tools for investigating the intrinsic correlation between two time series effected by common factors [27], has been applied to financial markets [17][18][19][28][29][30][31][32]. An intriguing feature is that the partial correlation analysis is able to identify influences among different time series [30].…”

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confidence: 99%

Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

et al. 2017

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-We perform a comparative analysis of the Chinese stock market around the occurrence of the 2008 crisis based on the random matrix analysis of high-frequency stock returns of 1228 stocks listed on the Shanghai and Shenzhen stock exchanges. Both raw correlation matrix and partial correlation matrix with respect to the market index in two time periods of one year are investigated. We find that the Chinese stocks have stronger average correlation and partial correlation in 2008 than in 2007 and the average partial correlation is significantly weaker than the average correlation in each period. Accordingly, the largest eigenvalue of the correlation matrix is remarkably greater than that of the partial correlation matrix in each period. Moreover, each largest eigenvalue and its eigenvector reflect an evident market effect, while other deviating eigenvalues do not. We find no evidence that deviating eigenvalues contain industrial sectorial information. Surprisingly, the eigenvectors of the second largest eigenvalues in 2007 and of the third largest eigenvalues in 2008 are able to distinguish the stocks from the two exchanges. We also find that the component magnitudes of the some largest eigenvectors are proportional to the stocks' capitalizations.Introduction. -Financial markets evolve in a selforganized manner with the interacting elements forming complex networks at different levels, including international markets [1][2][3][4], individual markets [5][6][7][8], and security trading networks [9][10][11][12][13][14][15][16]. There are well-documented stylized facts of stock return time series within individual markets unveiled by the random matrix theory (RMT) analysis [6,17]: (1) The largest eigenvalue reflects the market effect such that its eigenportfolio returns are strongly correlated with the market returns; (2) Other largest eigenvalues contain information of industrial sectors; and (3) The smallest eigenvalues embed stock pairs with large correlations. However, for stock exchange index returns [1] and housing markets [18,19], the largest eigenvalues can be used to extract geographic traits. Moreover, the signs of eigenvector components contain information of local interactions [18,20,21].

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confidence: 99%

Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

et al. 2017

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“…However, the high dimensionality and time-varying nature of correlation matrices poses challenges for both estimation and interpretation. A large set of tools exists to address these issues (Kenett et al 2015;Uechi et al 2014;Musmeci et al 2015;Song et al 2012), but it remains difficult to answer simple intuitive questions about which groups of assets are highly correlated with each other, and how these groups change through time. For instance, Dynamic Conditional Correlation (DCC) models (Engle et al 1992) allow for estimation of changing correlation matrices, and principal components analysis reduces the dimensionality of return correlation matrices (Connor and Korajczyk 1993).…”

Section: Introductionmentioning

confidence: 99%

Uncovering dynamic stock return correlations with multilayer network analysis

2019

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We apply recent innovations in network science to analyze how correlations of stock returns evolve over time. To illustrate these techniques we study the returns of 30 industry stock portfolios from 1927 to 2014. We calculate Pearson correlation matrices for each year, and apply multilayer network tools to these correlation matrices to uncover mesoscale architecture in the form of communities. These communities are easily interpretable as groups of industries with highly correlated stock returns. We observe that the flexibility, or the likelihood of industries to switch communities, exhibits a statistically significant increase after 1970, and that the communities evolve in ways consistent with changes in the structure of the U.S. economy. We find that these patterns are not explained by changes in average pairwise correlations or industry market betas. These results therefore underscore the potential for using multilayer network tools to study time-varying correlations of financial assets.

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“…In addition, some scholars used other methods to calculate the edge information of the stock network (Junior, Mullokandov, & Kenett, 2015;Raddant & Kenett, 2016;Wang, Xie, Chen, Yang, & Yang, 2013;Xie, Zhou, Wang, & Yan, 2017), among which the partial correlation concept attracts much interest (Kenett et al, 2010;Kenett, Huang, Vodenska, Havlin, & Stanley, 2015;Uechi, Akutsu, Stanley, Marcus, & Kenett, 2015). Kenett et al (2010) constructed a network using the partial correlation coefficient method as a global equity information and found that the financial sector, particularly the investment services, is the most influential stock.…”

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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Sector dominance ratio analysis of financial markets

Cited by 26 publications

References 45 publications

Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

Uncovering dynamic stock return correlations with multilayer network analysis

Tail dependence networks of global stock markets

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