Abstract

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

Citations