Identifying states of global financial market based on information flow network motifs

Xie, Wen-Jie; Yong, Yang; Wei, Na; Yue, Peng; Zhou, Wei‐Xing

doi:10.1016/j.najef.2021.101459

Cited by 10 publications

(7 citation statements)

References 53 publications

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“…As each morphology of network motifs unveils a high-order correlation pattern of a financial network [24], their synchronous change represents the dramatic shift in the financial system's functionality and may indicate a financial crisis [25]. In recent studies, researchers have found predictive signals by investigating network motifs distributions [21] and flickering behaviors [25]. This finding enlightens us to propose a better early warning indicator by describing more network motifs' synchronous change in more detail.…”

Section: Introductionmentioning

confidence: 95%

“…It shows intrinsic correlations with network resilience and robustness and can influence network functionality [16,19]; thus is identified as a determinant of critical transition [20]. Considering that a financial crisis is rooted in a lack of system resilience and robustness, analyzing the evolution of the financial network motif deepens the understanding of financial stability and helps predict financial crises [15,21]. Empirical studies have found that network motifs, in different morphologies, may change abruptly ahead of financial crises [22,23].…”

Section: Introductionmentioning

confidence: 99%

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Motif Transition Intensity: A Novel Network-Based Early Warning Indicator for Financial Crises

et al. 2022

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Financial crisis, rooted in a lack of system resilience and robustness, is a particular type of critical transition that may cause grievous economic and social losses and should be warned against as early as possible. Regarding the financial system as a time-varying network, researchers have identified early warning signals from the changing dynamics of network motifs. In addition, network motifs have many different morphologies that unveil high-order correlation patterns of a financial system, whose synchronous change represents the dramatic shift in the financial system’s functionality and may indicate a financial crisis; however, it is less studied. This paper proposes motif transition intensity as a novel method that quantifies the synchronous change of network motifs in detail. Applying this method to stock networks, we developed three early warning indicators. Empirically, we conducted a horse race to predict ten global crises during 1991–2020. The results show evidence that the proposed indicators are more efficient than the VIX and the other 39 network-based indicators. In a detailed analysis, the proposed indicators send sensitive and comprehensible warning signals, especially for the U.S. subprime mortgage crisis and the European sovereign debt crisis. Furthermore, the proposed method provides a new perspective to detect critical signals and may be extended to predict other crisis events in natural and social systems.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Motif Transition Intensity: A Novel Network-Based Early Warning Indicator for Financial Crises

et al. 2022

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“…network motifs are a key variable in the dynamic flow equations between macroscopic and microscopic layers in complex networks. By constructing a global stock index transfer entropy network and analyzing the ternary motifs in the network, Xie et al (2021) classified the global stock market into different market states according to the distribution of the motifs and found that the information flow in the global stock market increases significantly during major financial events, and the global stock indexes influence each other and are closely related. Based on the motif theory of complex networks.…”

Section: Research Related To Network Motifsmentioning

confidence: 99%

Analysis of the structure and time-series evolution of knowledge label network from a complex perspective

Wang

Feng

Guo

2022

AJIM

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PurposeThe research on social media-based academic communication has made great progress with the development of the mobile Internet era, and while a large number of research results have emerged, clarifying the topology of the knowledge label network (KLN) in this field and showing the development of its knowledge labels and related concepts is one of the issues that must be faced. This study aims to discuss the aforementioned issue.Design/methodology/approachFrom a bibliometric perspective, 5,217 research papers in this field from CNKI from 2011 to 2021 are selected, and the title and abstract of each paper are subjected to subword processing and topic model analysis, and the extended labels are obtained by taking the merged set with the original keywords, so as to construct a conceptually expanded KLN. At the same time, appropriate time window slicing is performed to observe the temporal evolution of the network topology. Specifically, the basic network topological parameters and the complex modal structure are analyzed empirically to explore the evolution pattern and inner mechanism of the KLN in this domain. In addition, the ARIMA time series prediction model is used to further predict and compare the changing trend of network structure among different disciplines, so as to compare the differences among different disciplines.FindingsThe results show that the degree sequence distribution of the KLN is power-law distributed during the growth process, and it performs better in the mature stage of network development, and the network shows more stable scale-free characteristics. At the same time, the network has the characteristics of “short path and high clustering” throughout the time series, which is a typical small-world network. The KLN consists of a small number of hub nodes occupying the core position of the network, while a large number of label nodes are distributed at the periphery of the network and formed around these hub nodes, and its knowledge expansion pattern has a certain retrospective nature. More knowledge label nodes expand from the center to the periphery and have a gradual and stable trend. In addition, there are certain differences between different disciplines, and the research direction or topic of library and information science (LIS) is more refined and deeper than that of journalism and media and computer science. The LIS discipline has shown better development momentum in this field.Originality/valueKLN is constructed by using extended labels and empirically analyzed by using network frontier conceptual motifs, which reflects the innovation of the study to a certain extent. In future research, the influence of larger-scale network motifs on the structural features and evolutionary mechanisms of KLNs will be further explored.

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“…The concept of motifs in networks (e.g. [14,15]) has allowed several interesting results in network science, with ample applications in biochemistry, neurobiology, ecology, engineering, economy [16], transportation and infrastructure [17]. Because of the intrinsic small topological variations expected to be found in city networks, the identification of possible motifs needs to be done statistically (e.g.…”

Section: Introductionmentioning

confidence: 99%

City Motifs as Revealed by Similarity Between Hierarchical Features

Domingues¹,

Tokuda²,

Costa³

2022

Preprint

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Several natural and theoretical networks can be broken down into smaller portions, or subgraphs corresponding to neighborhoods. The more frequent of these neighborhoods can then be understood as motifs of the network, being therefore important for better characterizing and understanding of the overall structure. Several developments in network science have relied on this interesting concept, with ample applications in areas including systems biology, computational neuroscience, economy and ecology. The present work aims at reporting an unsupervised methodology capable of identifying motifs respective to streets networks, the latter corresponding to graphs obtained from city plans by considering street junctions and terminations as nodes while the links are defined by the streets. Remarkable results are described, including the identification of nine stable and informative motifs, which have been allowed by three critically important factors: (i) adoption of five hierarchical measurements to locally characterize the neighborhoods of nodes in the streets networks; (ii) adoption of an effective coincidence methodology for translating datasets into networks; and (iii) definition of the motifs in statistical terms by using community finding methodology. The nine identified motifs are characterized and discussed from several perspective, including their mutual similarity, visualization, histograms of measurements, and geographical adjacency in the original cities. Also presented is the analysis of the effect of the adopted features on the obtained networks as well as a simple supervised learning method capable of assigning reference motifs to cities.

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Identifying states of global financial market based on information flow network motifs

Cited by 10 publications

References 53 publications

Motif Transition Intensity: A Novel Network-Based Early Warning Indicator for Financial Crises

Motif Transition Intensity: A Novel Network-Based Early Warning Indicator for Financial Crises

Analysis of the structure and time-series evolution of knowledge label network from a complex perspective

City Motifs as Revealed by Similarity Between Hierarchical Features

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