Clustering of Japanese stock returns by recursive modularity optimization for efficient portfolio diversification

Isogai, Takashi

doi:10.1093/comnet/cnu023

Cited by 13 publications

(16 citation statements)

References 23 publications

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“…7 a basically comprises regional banks, which seem to form a tightly connected subnetwork. This observation is consistent with the findings of our previous research (Isogai 2014). …”

Section: Analysis Of Dynamic Network Changessupporting

confidence: 94%

“…Specifically, these volatility shocks can distort the correlation structure when a market-wide shock occurs. In our previous research (Isogai 2014), we applied a multivariate volatility model to control for volatility fluctuations in order to avoid such a distortion problem when clustering a static correlation network. In this study, we use a more advanced type of volatility model with dynamically changing correlation (DCC–GARCH) to control for such volatility fluctuations.…”

Section: Literature Reviewmentioning

confidence: 99%

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Building a dynamic correlation network for fat-tailed financial asset returns

Isogai

2016

Appl Netw Sci

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In this paper, a novel approach to building a dynamic correlation network of highly volatile financial asset returns is presented. Our method avoids the spurious correlation problem when estimating the dynamic correlation matrix of financial asset returns by using a filtering approach. A multivariate volatility model, DCC–GARCH, is employed to filter the fat-tailed returns. The method is proven to be more reliable for detecting dynamic changes in the correlation matrix compared with the widely used method of calculating time-dependent correlation matrices over a fixed size moving window, which can have fundamental problems when applied to fat-tailed returns. We apply the method to selected Japanese stock returns to observe the dynamic network changes as a case study. The estimated time-dependent correlation matrices are then compared with those calculated by using the traditional method to highlight the advantages of the proposed method. Two types of indicators, namely the largest eigenvalue and cosine distance measures, are introduced to identify significant changes in the correlation matrix for an initial screening of remarkable stress events. A more detailed network-based analysis is then conducted by examining topological measures calculated from the network adjacency matrices. The higher density and lower heterogeneity of the correlation network during stress periods are clearly observed, while the correlation network of stock returns is shown to be robust with regard to time. The method discussed in this paper is not limited to stock returns; it can also be applied to build a dynamic correlation network of other financial and non-financial time series with high volatility.

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“…7 a basically comprises regional banks, which seem to form a tightly connected subnetwork. This observation is consistent with the findings of our previous research (Isogai 2014). …”

Section: Analysis Of Dynamic Network Changessupporting

confidence: 94%

Section: Literature Reviewmentioning

confidence: 99%

Building a dynamic correlation network for fat-tailed financial asset returns

Isogai

2016

Appl Netw Sci

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“…a Optimal Portfolio vs Degree Centrality b Sum of precision matrix diagonal Louvain algorithm (Blondel et al 2008). These methods have been applied previously to detect communities in financial networks constructed from stock data (PICCARDI et al 2011;Isogai 2014).…”

Section: Community Detectionmentioning

confidence: 99%

Partial correlation financial networks

Millington

Niranjan

2020

Appl Netw Sci

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Correlation based networks have been a popular way of inferring a financial network due to the simplicity of construction and the ease of interpretability. However two variables which share a common cause can be correlated, leading to the inference of spurious relationships. To solve this we can use partial correlation. In this paper we construct both correlation and partial correlation networks from S&P500 returns and compare and contrast the two. Firstly we show that the partial correlation networks have a smaller and much less variable intensity than the correlation networks, but in fact are less stable. We look at the centrality of the various sectors in the graph using degree centrality and eigenvector centrality, finding that sector centralities move together during the 2009 market crash and that the financial sector generally has a higher mean centrality over most of the dataset. Exploring the use of these centrality measures for portfolio construction, we shown there is mild correlation between the in-sample centrality and the out of sample Sharpe ratio but there is negative correlation between the in-sample centrality and out of sample risk. Finally we use a community detection method to study how the networks reflect the underlying sector structure and study how stable these communities are over time.

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“…This promising approach has also been applied to analyse time series data [22,23,24,25] where the goal is to identify clusters of components with a similar dynamics. The attempts made so far have basically replaced network data with cross-correlation matrices as the input.…”

Section: Community Structurementioning

confidence: 99%

Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations

et al. 2015

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The mesoscopic organization of complex systems, from financial markets to the brain, is an intermediate between the microscopic dynamics of individual units (stocks or neurons, in the mentioned cases), and the macroscopic dynamics of the system as a whole. The organization is determined by “communities” of units whose dynamics, represented by time series of activity, is more strongly correlated internally than with the rest of the system. Recent studies have shown that the binary projections of various financial and neural time series exhibit nontrivial dynamical features that resemble those of the original data. This implies that a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. Here, we explore whether the binary signatures of multiple time series can replicate the same complex community organization of the financial market, as the original weighted time series. We adopt a method that has been specifically designed to detect communities from cross-correlation matrices of time series data. Our analysis shows that the simpler binary representation leads to a community structure that is almost identical with that obtained using the full weighted representation. These results confirm that binary projections of financial time series contain significant structural information.

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Clustering of Japanese stock returns by recursive modularity optimization for efficient portfolio diversification

Cited by 13 publications

References 23 publications

Building a dynamic correlation network for fat-tailed financial asset returns

Building a dynamic correlation network for fat-tailed financial asset returns

Partial correlation financial networks

Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations

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