Evolving Traders and the Faculty of the Business School: A New Architecture of the Artificial Stock Market

In this paper, we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called "school" which is a procedure for mapping the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of school, considering it as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders' search behaviour. By simulated annealing, the traders' search densities can be connected to psychological factors such as peer pressure or to economic factors such as the standard of living. This market architecture is then implemented in a standard artificial stock market. Our econometric study of the resultant artificial time series gives evidence that the return series is independently and identically distributed (iid) and hence supports the efficient market hypothesis (EMH). What is interesting, though, is that this iid series is generated by traders who do not believe in the EMH at all. In fact, our study indicates that many of our traders are often able to find useful signals from business school, even though these signals are short-lived.