Financial market opinion dynamics in complex networks - Xia, Yuanhao (Nanyang Technological University, 2024)
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.