Stability Analysis Of Financial Network Flows Based On Evolutionary Game Theory

Network flow problem is one of the most important network optimization issues and is how to effectively design,manage and control network systems to maximize the social and economic benefits.Network flow analysis is the forefront of intelligent information processing.A financial network is a model that describes financial market participants and their financial relationships.The occurrence of financial transactions promotes the flow of financial resources in the network,forming financial network flows.When the evolution of the financial network flows reaches a steady state,the financial market is running in a steady state.Based on the theory of evolutionary game theory,this paper puts forward the theory of stability analysis of financial network flow,applies this theory to three kinds of typical topological financial networks and gives the experimental method.Experimental results can help financial market regulators in-depth understanding of the operation of the market mechanism.The main work of this paper includes a theoretical study and three applied studies,summarized as follows.(1)The theory of stability analysis of financial network flow is studied.In this thesis,a network game model is established to describe the problem of resource allocation in financial markets.Then,the network game is expressed as the form of a potential game.According to the property of the potential game,this thesis give the equilibrium solution of the network game and prove the uniqueness of equilibrium solution.This research is based on the concept of potential capacity to analyze the resource allocation in financial markets,and can more intuitively understand the equilibrium state of financial network.This method provides a model to study the evolution of fund,risk and information flow in real financial markets.Furthermore,based on the framework of evolutionary game theory,this thesis uses dynamic models to describe the financial network flow.And then we prove that the dynamic models will converge to the equilibrium solution of the network game over time.The proof method is based on the Lyapunov stability theorem in differential equation theory,and the proof process has some theoretical difficulty.This study takes into account that participants in the financial market are bounded rationally and adjust the trading strategies at different times.These conditions are the prerequisites of evolutionary game theory,so as to analyze the stability of the financial network.The analysis method is to study the implementing process and conditions of financial market equilibrium from the perspective of evolution,and analyze the interaction between financial market equilibrium and network flow evolution.(2)The evolutionary nature and computational experiment of the funds flows in bank deposit and loan network are studied.In the three-tier network that represents the bank’s deposit and loan market,a network game is formed by the controller of the funds.The equilibrium state of the game is the equilibrium of the capitalization rate of each loan path.Borrowing transactions between market participants,resulting in the flow of funds in the financial network,and the flow of funds always flows to the path of high returns.In this thesis,an ant colony algorithm with fluctuating control is designed,and the heuristic pathfinding feature of ant colony is used to simulate the flow of funds in a bank deposit-loan network.Furthermore,in the computational experiment of funds flows evolution,the evolution rate and fluctuation range of capital are described by setting the degree of conservative transfer of funds.(3)The evolutionary nature and computational experiment of risk propagation in a financial safety net are studied.In the bipartite graph that represents the deposit insurance market,a network game is formed by the commercial banks,the deposit insurance institutions and the insurance relationship between them.The equilibrium state of the game is the equivalent returns of the risk assets of the commercial banks.As risky assets are transferred from insured banks to uninsured banks,risk spread is formed in financial safety net.In this thesis,we design an iterative reinforcement learning algorithm.In each iteration calculation,each commercial bank makes the next cycle of insurance decision based on the enhanced signal provided by the market.The decision is based on the increase of the probability of high returns,simulating the evolution of risky assets in financial safety net.Furthermore,in the computational experiment of risk propagation,the learning rate of commercial bank decision is set up to describe the evolution rate of risk assets.(4)The evolutionary nature and computational experiment of information transmission in a P2P loan network are studied.In the Mesh network that represents the P2P loan market,a network game is formed by the participants of the P2P platform and investment-financing relationships between them.The equilibrium state of the game is that the returns of the information disclosed by the participants are balanced.Because participants need to disclose their credit information to other participants in the P2P loan platform,the information transmission is formed in the P2P loan network.In this thesis,we design an iterative best response algorithm to realize the evolution of information disclosure in a P2P loan network.Participants update their decisions in each phase,simulating the evolution of the information disclosed in a P2P platform.Furthermore,in the computational experiment of information disclosure,the update probability of the participant and the influence coefficient of the neighbor are set to describe the heterogeneity of the individual.