Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market

Authored by SH Chen, CH Yeh

Date Published: 2001-03

DOI: 10.1016/s0165-1889(00)00030-0

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Pseudocode Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

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 to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of `school', and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders' search behavior. By simulated annealing, traders' search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. Our econometric study of the resultant artificial time series evidences 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 lid series was generated by traders, who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived. (C) 2001 Elsevier Science B.V. All rights reserved. JEL classification: G12; G14; D83.

Tags

Agent-Based Computational Economics Artificial stock markets genetic programming Social learning business school