A network analysis of the value migration process on the financial market. The effect of value migration network structure on stock returns

Siudak, Dariusz

doi:10.1016/j.eswa.2021.116129

Cited by 12 publications

(12 citation statements)

References 79 publications

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“…The resulting classification using the Kohonen network and the four centrality measures does not determine whether the analyzed assets are central or peripheral. However, previous study [ 59 ] has revealed the diversifying impact of the centrality level in VMN on the rate of return on shares. This is due to the different nature of the individual centrality measures used, for which a positive effect was demonstrated for in-degree centrality, and a negative one for out-degree and entropy centrality.…”

Section: Discussionmentioning

confidence: 99%

“…The following steps based on the procedure presented in Ref. [ 59 ] are performed to build the value migration network:…”

Section: Methodsmentioning

confidence: 99%

“…This implies that a relatively few hub-like assets strongly influence the fluctuations of the rest stock prices and corporate values in the entire stock market. As pointed out by Siudak [ 59 ], the VMN shows a disassortative behavior. In this light, only a few highly connected assets synchronize flows of value on the stock market in two directions, the inflow and outflow of value.…”

Section: Introductionmentioning

confidence: 93%

“…In the VMN, companies (stocks) are represented as nodes, and the value flows between stocks are modeled as edges. It has been demonstrated that in- and out-degree distribution in the VMN obeys a power-law [ 59 ]. This implies that a relatively few hub-like assets strongly influence the fluctuations of the rest stock prices and corporate values in the entire stock market.…”

Section: Introductionmentioning

confidence: 99%

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The effect of self-organizing map architecture based on the value migration network centrality measures on stock return. Evidence from the US market

Siudak

2022

PLoS ONE

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Complex financial systems are the subject of current research interest. The notion of complex network is used for understanding the value migration process. Based on the stock data of 498 companies listed in the S&P500, the value migration network has been constructed using the MST-Pathfinder filtering network approach. The analysis covered 471 companies included in the largest component of VMN. Three methods: (i) complex networks; (ii) artificial neural networks and (iii) MARS regression, are developed to determine the effect of network centrality measures and rate of return on shares. A network-based data mining analysis has revealed that the topological position in the value migration network has a pronounced impact on the stock’s returns.

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Section: Discussionmentioning

confidence: 99%

“…The following steps based on the procedure presented in Ref. [ 59 ] are performed to build the value migration network:…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The effect of self-organizing map architecture based on the value migration network centrality measures on stock return. Evidence from the US market

Siudak

2022

PLoS ONE

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“…One can identify the existence of this linkage using numerous indicators. Some of them include the well-known Pearson's correlation coefficient [16][17][18][19][20][21], partial correlation coefficient [22][23][24], and tail dependence coefficient [25][26][27][28]. However, since these coefficients only measure pairwise dependencies, they cannot accommodate directional causality relationships or measure the magnitude of the impacts [15].…”

Section: Introductionmentioning

confidence: 99%

Modifying (M)CoVaR and constructing tail risk networks through analytic higher-order moments: Evidence from the global forex markets

et al. 2022

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In a financial system, entities (e.g., companies or markets) face systemic risk that could lead to financial instability. To prevent this impact, we require quantitative systemic risk management we can carry out using conditional value-at-risk (CoVaR) and a network model. The former measures any targeted entity’s tail risk conditional on another entity being financially distressed; the latter represents the financial system through a set of nodes and a set of edges. In this study, we modify CoVaR along with its multivariate extension (MCoVaR) considering the joint conditioning events of multiple entities. We accomplish this by first employing a multivariate Johnson’s SU risk model to capture the asymmetry and leptokurticity of the entities’ asset returns. We then adopt the Cornish–Fisher expansion to account for the analytic higher-order conditional moments in modifying (M)CoVaR. In addition, we attempt to construct a conditional tail risk network. We identify its edges using a corresponding Delta (M)CoVaR reflecting the systemic risk contribution and further compute the strength and clustering coefficient of its nodes. When applying the financial system to global foreign exchange (forex) markets before and during COVID-19, we revealed that the resulting expanded (M)CoVaR forecast exhibited a better conditional coverage performance than its unexpanded version. Its superior performance appeared to be more evident over the COVID-19 period. Furthermore, our network analysis shows that advanced and emerging forex markets generally play roles as net transmitters and net receivers of systemic risk, respectively. The former (respectively, the latter) also possessed a high tendency to cluster with their neighbors in the network during (respectively, before) COVID-19. Overall, the interconnectedness and clustering tendency of the examined global forex markets substantially increased as the pandemic progressed.

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Cohesion and segregation in the value migration network: Evidence from network partitioning based on sector classification and clustering

Siudak

2023

Soc. Netw. Anal. Min.

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Cluster structure detection of the network is a basic problem of complex network analysis. This study investigates the structure of the value migration network using data from 499 stocks listed in the S&P500 as of the end of 2021. An examination is carried out whether the process of value migration creates a cluster structure in the network of companies according to economic activity. Specifically, the cohesion and segregation of the extracted modules in the network division according to (i) sector classification, (ii) community division, and (iii) network clustering decomposition are assessed. The results of this study show that the sector classification of the value migration network has a non-cohesive structure, which means that the flow of value in the financial market occurs between companies from various industries. Moreover, the divisions of the value migration network based on community detection and clustering algorithm are characterized by intra-cluster similarity between the vertices and have a strong community structure. The structure of the network division into modules corresponding to the classification of economic sectors differs significantly from the partition based on the algorithms applied.

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A network analysis of the value migration process on the financial market. The effect of value migration network structure on stock returns

Cited by 12 publications

References 79 publications

The effect of self-organizing map architecture based on the value migration network centrality measures on stock return. Evidence from the US market

The effect of self-organizing map architecture based on the value migration network centrality measures on stock return. Evidence from the US market

Modifying (M)CoVaR and constructing tail risk networks through analytic higher-order moments: Evidence from the global forex markets

Cohesion and segregation in the value migration network: Evidence from network partitioning based on sector classification and clustering

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