Financial market opinion dynamics in complex networks
Complex network theory describes real-world systems through networks consisting of nodes and edges between nodes at the topological level by efficient abstract representations. Complex network-based modeling of financial market trader systems evolved from the majority-vote model can effectively char...
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2024
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sg-ntu-dr.10356-1757932024-05-10T15:49:35Z Financial market opinion dynamics in complex networks Xia, Yuanhao Xiao Gaoxi School of Electrical and Electronic Engineering EGXXiao@ntu.edu.sg Computer and Information Science Physics Complex networks Complex network theory describes real-world systems through networks consisting of nodes and edges between nodes at the topological level by efficient abstract representations. Complex network-based modeling of financial market trader systems evolved from the majority-vote model can effectively characterize multiple properties of real financial markets. Previous research has used Erdős-Rényi random networks to characterize trader networks. Due to the scale-free feature of Barabási-Albert scale-free networks, in order to make the model more valuable for research on human social networks, this dissertation proposes and completes this financial market modeling using Barabási-Albert scale-free networks. This dissertation finds that in financial market modeling, the critical noise value of the ordered-disordered phase transition in the standard majority-vote model corresponding to the Barabási-Albert scale-free networks is higher than that of the Erdős-Rényi random networks under the same average nodal degree. This dissertation analyzes the characteristics of the topologies of the Erdős-Rényi random networks and the Barabási-Albert scale-free networks, compares the degrees of instability tolerance of the system under the two networks, finds the reason why the critical noise value of the ordered-disordered phase transition in the standard majority-vote model corresponding to the Barabási-Albert scale-free networks is higher than that of the Erdős-Rényi random networks under the same average nodal degree from the perspective of the efficiency of the information diffusion with related proofs provided. Master's degree 2024-05-08T01:33:35Z 2024-05-08T01:33:35Z 2024 Thesis-Master by Coursework Xia, Y. (2024). Financial market opinion dynamics in complex networks. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175793 https://hdl.handle.net/10356/175793 en application/pdf Nanyang Technological University |
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Computer and Information Science Physics Complex networks Xia, Yuanhao Financial market opinion dynamics in complex networks |
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Complex network theory describes real-world systems through networks consisting of nodes and edges between nodes at the topological level by efficient abstract representations. Complex network-based modeling of financial market trader systems evolved from the majority-vote model can effectively characterize multiple properties of real financial markets. Previous research has used Erdős-Rényi random networks to characterize trader networks. Due to the scale-free feature of Barabási-Albert scale-free networks, in order to make the model more valuable for research on human social networks, this dissertation proposes and completes this financial market modeling using Barabási-Albert scale-free networks.
This dissertation finds that in financial market modeling, the critical noise value of the ordered-disordered phase transition in the standard majority-vote model corresponding to the Barabási-Albert scale-free networks is higher than that of the Erdős-Rényi random networks under the same average nodal degree. This dissertation analyzes the characteristics of the topologies of the Erdős-Rényi random networks and the Barabási-Albert scale-free networks, compares the degrees of instability tolerance of the system under the two networks, finds the reason why the critical noise value of the ordered-disordered phase transition in the standard majority-vote model corresponding to the Barabási-Albert scale-free networks is higher than that of the Erdős-Rényi random networks under the same average nodal degree from the perspective of the efficiency of the information diffusion with related proofs provided. |
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Xiao Gaoxi |
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Xiao Gaoxi Xia, Yuanhao |
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Thesis-Master by Coursework |
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Xia, Yuanhao |
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Xia, Yuanhao |
title |
Financial market opinion dynamics in complex networks |
title_short |
Financial market opinion dynamics in complex networks |
title_full |
Financial market opinion dynamics in complex networks |
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Financial market opinion dynamics in complex networks |
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Financial market opinion dynamics in complex networks |
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financial market opinion dynamics in complex networks |
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Nanyang Technological University |
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2024 |
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https://hdl.handle.net/10356/175793 |
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