A complex adaptive agent modeling to predict the stock market prices

Abstract

Understanding the behaviors of financial markets and their participants remains a challenging problem to resolve. Adaptive agents, which switch from fundamentalist to chartist behavior, are examined in some recent work. In this paper, we propose an adaptive agent-based model that combines three forecasting behaviors of financial agents: fundamentalist, chartist, and mimetic. The weighting of each type of behavior in the final forecasting changes according to the market cycle. Our model adapts to the different cycles of the market.We assess the ability of the proposed model to predict and explain the dynamics of stock market price formation. First, on the microscopic level, we consider four agent-based models: fundamentalist, chartist, mimetic and adaptive. We compare the prices generated by the different prediction models to the real prices generated by the US market (S&P 500 index) over the period (1990–2021). Second, at a macroscopic level, we set up a multi-agent system to simulate an artificial stock market composed of the four types of agents with different market fractions. We compare the prices generated by the artificial market with the real data generated by the US market.The series of statistical analyses that we have carried out allows us to conclude that: the proposed adaptive agent exists in the stock market, he offers a better accuracy of price predictions compared to fundamentalist, chartist and mimetic models and that he can explain the dynamics of the stock market prices formation when he dominates.