On Intelligent-Agent Based Analysis of Financial Markets

Agent-based computational economics acknowledges the distributed nature of trading in financial markets by modeling the markets as evolving systems of autonomous, interacting agents that correspond to the trading parties. Conventionally, the behavior of traders has been described mathematically, and the market system is analyzed at equilibrium conditions. The dynamics of price formation, however, is influenced by the large diversity in the cognitive structures of the traders (e.g. differences in decision making methods, interpretation of available information and learning capacity), their specific circumstances (e.g. attitude to risk, time horizon) and the organization of the specific market in which the traders operate (e.g. market microstructure). Therefore, we propose to study financial markets by using intelligent agents that have rich cognitive structures borrowed from artificial intelligence research for modeling their decision making behavior. This representation allows us to model the decision making behavior of agents in terms of algorithms, that can represent a more diverse set of behaviors than mathematical formulae only. We discuss the role of intelligent-agents in the analysis of financial markets and speculate on the type of agents that can be expected to be suitable for the analysis and simulation of financial markets. We elucidate our thoughts by exposing the outline of a research project that has started recently at our university. As a first step of our research project, we discuss a classification of adaptation that we proposed recently for agents in agent-based computational economics.