Information Asymmetry in the Artificial Financial Market Represented by Scale-Free Network

Financial markets are complex systems in which the market price of the financial instruments reflects the distribution of information and investors’ expectations. If we regard investors as nodes (vertices) and interacting relations between investors as links (edges), than financial markets can be viewed as complex networks, whose structure corresponds to scale-free networks. Since the classic mathematical models have not yielded satisfactory results in market analysis, in recent years, the complex system analysis is carried out using agent-based models. In this paper we analyze the diffusion of information in the financial market presented by means of a scale-free network. We hypothesize that large investors are better informed than the smaller ones and explain reasons for appearance of information asymmetry between the traders. Considering the assumption that financial market is scale-free network, we present a simple agent-based computational model of the financial market, formed in Netlog and R programming environment, and investigate the effects of distribution of information through network. Simulations of artificial stock market (ASM) model with different number of heterogeneous agents and different priority in orders’ realization shows that initial wealth of agents is reallocated in favor of better informed agents. Small and “uninformed” agents can reduce the information gap by imitating the wealthier neighboring agent and increase the wealth. However, favoring realization of large orders can paradoxically improve the position of small agents in the market by reducing the effect of inaccurate anticipation of market movements due to lack of information and efficacy to make precise anticipation.