Analysis of the Dynamics of Market Graph Characteristics

Faizliev, Alexey; Balash, Vladimir; Vlasov, Andrey; Tryapkina, Tatiana; Миронов, С. В.; Androsov, Ivan; Petrov, Vladimir

doi:10.2991/cmdm-18.2019.3

Cited by 2 publications

(3 citation statements)

References 37 publications

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“…Indeed, graphs, mathematical models of networks, have been shown to be useful tools to model complex relationships between assets. Early work by Mantegna [26][27][28] analyze stock markets using graphs built using Pearson, Spearman rank and Kendall correlation, respectively. Millington and Niranjan's (2021) [29] work compares networks built using these different correlations.…”

Section: Literature Reviewmentioning

confidence: 99%

Distance Correlation Market Graph: The Case of S&P500 Stocks

Ugwu,

Miasnikof,

Lawryshyn

2023

Mathematics

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This study investigates the use of a novel market graph model for equity markets. Our graph model is built on distance correlation instead of the traditional Pearson correlation. We apply it to the study of S&P500 stocks from January 2015 to December 2022. We also compare our market graphs to the traditional market graphs in the literature, those built using Pearson correlation. To further the comparison, we also build graphs using Spearman rank correlation. Our comparisons reveal that non-linear relationships in stock returns are not captured by either Pearson correlation or Spearman rank correlation. We observe that distance correlation is a robust measure for detecting complex relationships in S&P500 stock returns. Networks built on distance correlation networks, are shown to be more responsive to market conditions during turbulent periods such as the COVID crash period.

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Section: Literature Reviewmentioning

confidence: 99%

Distance Correlation Market Graph: The Case of S&P500 Stocks

Ugwu,

Miasnikof,

Lawryshyn

2023

Mathematics

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“…The market graph constructed with use of the measure linearly dependent on the sign correlation was studied in [23,32]. The paper showed that the measure is capable for the analysis of the market graphs.…”

Section: Market Network Constructionmentioning

confidence: 99%

“…In accordance with the efficient market hypothesis, it is assumed that stock prices of companies and their mutual behavior reflect all publicly available information about companies. Thus, economic and financial connections between companies may be reflected by the correlation of the log returns of company assets [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. • Some researchers believed that a connection between companies arises, if both companies have common members of the board of directors [33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Company Co-Mention Network and Market Graph Over Time Using Graph Similarity Measures

Faizliev

Balash

Petrov

et al. 2019

Journal of Open Innovation: Technology, Market, and Complexity

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The aim of the paper is to provide an analysis of news and financial data using their network representation. The formation of network structures from data sources is carried out using two different approaches: by building the so-called market graph in which nodes represent financial assets (e.g., stocks) and the edges between nodes stand for the correlation between the corresponding assets, by constructing a company co-mention network in which any two companies are connected by an edge if a news item mentioning both companies has been published in a certain period of time. Topological changes of the networks over the period 2005–2010 are investigated using the sliding window of six-month duration. We study the stability of the market graph and the company co-mention network over time and establish which of the two networks was more stable during the period. In addition, we examine the impact of the crisis of 2008 on the stability of the market graph as well as the company co-mention network. The networks that are considered in this paper and that are the objects of our study (the market graph and the company co-mention network) have a non-changing set of nodes (companies), and can change over time by adding/removing links between these nodes. Different graph similarity measures are used to evaluate these changes. If a network is stable over time, a measure of similarity between two graphs constructed for two different time windows should be close to zero. If there was a sharp change between the graphs constructed for two adjacent periods, then this should lead to a sharp increase in the value of the similarity measure between these two graphs. This paper uses the graph similarity measures which were proposed relatively recently. In addition, to estimate how the networks evolve over time we exploit QAP (Quadratic Assignment Procedure). While there is a sufficient amount of works studying the dynamics of graphs (including the use of graph similarity metrics), in this paper the company co-mention network dynamics is examined both individually and in comparison with the dynamics of market graphs for the first time.

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Analysis of the Dynamics of Market Graph Characteristics

Cited by 2 publications

References 37 publications

Distance Correlation Market Graph: The Case of S&P500 Stocks

Distance Correlation Market Graph: The Case of S&P500 Stocks

Stability Analysis of Company Co-Mention Network and Market Graph Over Time Using Graph Similarity Measures

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