Impacts of social networks in an agent-based artificial stock market

Abstract

We propose an agent-based articial stock market to investigate the inuences of social networks on the nancial market. The articial stock market contains four types of traders whose information sets and trading strategies are dierent. Genetic Programming is employed in informed and uninformed traders' learning behavior and heterogeneity with the application of articial intelligence. When information is exogenous, social networks result in higher market volatility and trading volume, and decrease price distortion and bid-ask spread. When information is endogenous, the inuences of social networks on the nancial market are reversed, which indicates that social networks harm market eciency, decreases the trading volume and increases bid-ask spread. The reason is that social networks harm information production after traders tend to rely on information from communication, instead of spending a cost on it.

Fleeting Extinction? Unraveling the persistence of Noise Traders in Financial Markets with Learning and Replacement

“…In a paper related to ours, Dai, Zhang, and Chang (2023) developed an artificial stock market with three types of agents that are similar to ours in some respects (informed, uninformed, and noise traders). While they find that noise traders cannot survive in the long run, the presence of noise traders increases market volatility, price distortion, noise trader risk, and trading volume.…”