Does more intelligent trading strategy win? Interacting trading strategies: an agent-based approach

Abstract

An artificial financial market is built on top of the Genoa Artificial Stock Market. The market is populated with agents having different trading strategies and they are let to interact with each other. Agents differ in the trading method they use to trade, and they are grouped as noise, technical, statistical analysis, and machine learning traders. The model is validated by the replication of stylized facts in financial asset returns. We were able to replicate the leptokurtic shape of the probability density function, volatility clustering, and the absence of autocorrelation in asset returns. The wealth dynamics for each agent group are analyzed throughout the trading period. Agents with a higher time complexity trading strategy outperform those with a strategy comparing their final wealth.