Construction and robustness of directed-weighted financial stock networks via meso-scales

Su, Qingqing; Tu, Lilan; Wang, Xianjia; Rong, Hang

doi:10.1016/j.physa.2022.127955

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…The economic relationships of financial institutes can form a network, and analyzing such networks can facilitate the understanding of certain economic contexts as shown in several studies [49][50][51][52][53][54][55][56][57]. The use of network theory in the context of financial systems can, thus, ease one's understanding of complex systems.…”

Section: Directed Correlation Networkmentioning

confidence: 99%

Network-Induced Soft Sets and Stock Market Applications

2022

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The intricacy of the financial systems reflected in bilateral ties has piqued the interest of many specialists. In this research, we introduce network-induced soft sets, a novel mathematical model for studying the dynamics of a financial stock market with several orders of interaction. To achieve its intelligent parameterization, this model relies on the bilateral connections between economic actors, who are agents in a financial network, rather than relying on any other single feature of the network itself. Our study also introduces recently developed statistical measures for network-induced soft sets and provides an analysis of their application to the study of financial markets. Findings validate the efficacy of this novel method in assessing the effects of various economic stress periods registered in Borsa Istanbul.

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Section: Directed Correlation Networkmentioning

confidence: 99%

Network-Induced Soft Sets and Stock Market Applications

2022

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“…Hence, complex network analysis is often used to study stock markets. Networks (graphs) are ideally suited to capture interactions in large systems with interacting components [14]. In a market graph, each node represents an asset (typically a stock) and the edges between nodes indicate the correlations between their returns [3].…”

Section: Introductionmentioning

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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Climate policy uncertainty and the Chinese sectoral stock market: A multilayer network analysis

Chen,

Wang

2024

Economic Systems

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Construction and robustness of directed-weighted financial stock networks via meso-scales

Cited by 5 publications

References 23 publications

Network-Induced Soft Sets and Stock Market Applications

Network-Induced Soft Sets and Stock Market Applications

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

Climate policy uncertainty and the Chinese sectoral stock market: A multilayer network analysis

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