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      Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets

      1 , 2 , 3 , 4 , 5 , 3 , 6
      Complexity
      Hindawi Limited

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          Abstract

          Policy makings and regulations of financial markets rely on a good understanding of the complexity of financial markets. There have been recent advances in applying data-driven science and network theory into the studies of social and financial systems. Financial assets and institutions are strongly connected and influence each other. It is essential to study how the topological structures of financial networks could potentially influence market behaviors. Network analysis is an innovative method to enhance data mining and knowledge discovery in financial data. With the help of complex network theory, the topological network structures of a market can be extracted to reveal hidden information and relationships among stocks. In this study, two major markets of the most influential economies, China and the United States, are systematically studied from the perspective of financial network analysis. Results suggest that the network properties and hierarchical structures are fundamentally different for the two stock markets. The patterns embedded in the price movements are revealed and shed light on the market dynamics. Financial policymakers and regulators can gain inspiration from these findings for applications in policy making, regulations design, portfolio management, risk management, and trading.

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          Most cited references102

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          Emergence of scaling in random networks

          Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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            The structure and function of complex networks

            M. Newman (2003)
            Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
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              Error and attack tolerance of complex networks

              Many complex systems, such as communication networks, display a surprising degree of robustness: while key components regularly malfunction, local failures rarely lead to the loss of the global information-carrying ability of the network. The stability of these complex systems is often attributed to the redundant wiring of the functional web defined by the systems' components. In this paper we demonstrate that error tolerance is not shared by all redundant systems, but it is displayed only by a class of inhomogeneously wired networks, called scale-free networks. We find that scale-free networks, describing a number of systems, such as the World Wide Web, Internet, social networks or a cell, display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected by even unrealistically high failure rates. However, error tolerance comes at a high price: these networks are extremely vulnerable to attacks, i.e. to the selection and removal of a few nodes that play the most important role in assuring the network's connectivity.
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                Author and article information

                Journal
                Complexity
                Complexity
                Hindawi Limited
                1076-2787
                1099-0526
                November 01 2018
                November 01 2018
                : 2018
                : 1-31
                Affiliations
                [1 ]Center of Cyberspace and Security, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
                [2 ]School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 610054, China
                [3 ]Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland
                [4 ]Department of Computer Information Systems and Supply Chain Management, Walker College of Business, Appalachian State University, Boone, NC 28608, USA
                [5 ]Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA
                [6 ]Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
                Article
                10.1155/2018/4680140
                09d228a7-6bbd-47e6-8f31-9eba254e2959
                © 2018

                http://creativecommons.org/licenses/by/4.0/

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