Artificial Financial Markets: An Agent Based Approach to Reproduce Stylized Facts and to study the Red Queen Effect

Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world’s population. Investors, governing bodies and the society as a whole could benefit from better understanding of the behaviour of stock markets. The traditional approach to analyze such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of analytical results. This motivates the use of alternative methods. For those reasons, the study of such markets is a fertile field to use the agent-based methodology. In this work, we developed an artificial financial market and used it to study the behaviour of stock markets. In this market, we model technical, fundamental and noise traders. The technical traders are non-simple genetic programming based agents that co-evolve (by means of their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. Such traders are equipped with an investment strategy that we consider to be realistic and we avoid any kind of strong assumptions about the agents’ rationality, utility function or risk aversion. Changes in some parameters and in the agents behaviour produce different properties of the stock price series that we analyze. In this paper we investigate the different conditions under which the statistical properties of an artificial stock market resemble those of the real financial markets. Additionally, we modelled the pressure to beat the market by a behavioural constraint imposed on the agents related to the Red Queen principle in evolution. The Red Queen principle is a metaphor of a co-evolutionary arms race between species. We investigate the effect of such constraint on the price dynamics and the wealth distribution of the agents after several periods of trading in the different simulation cases. We have demonstrated how evolutionary computation plays a key role in studying stock markets.