Abstract
This paper aims to investigate the influence of investors’ confidence in their portfolio holding relative to their social group and of various social network topologies on the dynamics of an artificial stock exchange. An investor’s confidence depends on the growth rate of his or her wealth relative to his or her social group’s average wealth. If the investor’s confidence is low, the agent will change his or her asset allocation; otherwise, he or she will maintain it. We consider three types of social networks: Barabási, small-world, and random. The actual stock markets’ properties are recovered by this model: high excess kurtosis, skewness, volatility clustering, random walk prices, and stationary return rates. The networks’ topologies are found to impact both the structuration of investors in the space of strategies and their performance. Among other characteristics, we find that (i) the small-world networks show the highest degree of homophily; (ii) as investors can switch to more profitable strategies, the best approach to make profitable investments is the chartist one in Barabási and small-world topologies; and (iii) an unequal distribution and more significant relative wealth gains occur in the Barabási network.
Highlights
Agent-based models study phenomena that emerge through individual interactions [1]
Excess kurtosis of simulated returns with networks indicates heavier tails, consistent with actual data. e observed exception refers to simulated data with no network effects whose kurtosis is very close to 3. e Jarque–Bera test rejected the hypothesis of normality for all simulated returns with networks and for the actual data. e normality hypothesis could not be rejected for simulated returns with no network effect, in line with the kurtosis result presented above
We have investigated this model’s properties on three types of social networks. e combination of a psychological variable with social networks produced results consistent with empirical facts. e incorporation of networks in conjunction with the behavioral variable sheds light on several properties that are little explored in this literature, such as the degree of homophily and the level of wealth inequality within social network topologies of a virtual stock exchange
Summary
Agent-based models study phenomena that emerge through individual interactions [1]. One branch of this computational economics refers to simulated economic systems on which economic theory can be tested. Such economic “laboratories” occupy a niche between analytical models, theoretical models, and empirical research. Us, they present the opportunity to verify more realistic theories than analytical models while maintaining the possibility of examining and understanding the resulting behavior. Another branch of agent-based computational economics aims at understanding the emergence of global behaviors based on local interactions. If the same behaviors exist in simulated models, it can be stated that the inclusion of certain actors in the simulation may be sufficient to induce certain observed behaviors. ese branches of agent-based computational economics are not mutually exclusive
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