Agent-based Modeling and Investors’ Behavior Explanation of Asset Price Dynamics on Artificial Financial Markets

Standard asset pricing models based on rational expectations and homogeneity have problems explaining the complex and volatile nature of financial markets. The heterogeneity in expectations can lead to market instability and complicate dynamics of prices, which are driven by endogenous market forces. In this sense, we use Agent-based computational approach and more specifically artificial Stock Market modeling to explore the market dynamics from a behavioral perspective. Our aim is to point out that the investors’ irrationality explains various numbers of financial anomalies, especially the phenomena that traditional financials models have never been able to explain. We built a virtual financial market that contains three types of investors: fundamentalists, non-fundamentalist and loss adverse investors. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the investor psychology. The Artificial Neural Networks learning mechanism take on the role of traders, who from their futures return expectations and place orders based on their expectations. The results of intensive analysis indicate that the existence of agents having heterogeneous beliefs and preferences has provided a better understanding of price dynamics in the financial market.