Multi-Agent Market Modeling based on Neural Networks

Abstract

One of the challenges of financial research is to develop models that are capable of explaining and forecasting market price movements and returns.Agent based models focus directly on the underlying structure of the market. The basic idea is, that the market price dynamics arises from the interaction of many individual agents. Approaching financial markets in this manner, one starts off with the modeling of the agents´ decision making schemes on the microeconomic level of the market. Thereafter, market price changes can be determined on the macroeconomic level by a superposition of the agents´ buying and selling decisions. The aim of a (micro-)economic model is to explain market prices by a detailed causal analysis of the agents´ decision making behavior. The market price results from an aggregation of the agents´ decisions. Remarkably, agent-based financial markets provide a new explanatory framework supplementing the traditional economic concepts of equilibrium theory and efficient markets. Such a supplementing framework is needed, because in real-world financial markets the underlying assumptions of equilibrium or efficient market theory are often violated.As we will show, neural networks allow the integration of the decision behavior of individual economic agents into a market model. Based on the perspective of interacting agents, the resulting market model allows us to capture the underlying dynamics of financial markets, to fit real-world financial data, and to forecast future market price movements.In addition, we point out that neural networks allow to set up a joint framework of econometric model building. Besides the learning from data, one may integrate prior knowledge about the underlying dynamical system and first principles into the modeling. These elements are incorporated into the neural networks in form of architectural enhancements. This way of model building helps to overcome the drawbacks of purely data driven approaches.