Evolution and co-evolution of computer programs to control independently-acting agents
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming and emergent intelligence
Advances in genetic programming
Artificial economic life: a simple model of a stockmarket
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Competitively evolving decision trees against fixed training cases for natural language processing
Advances in genetic programming
Software—Practice & Experience
Genetic Algorithms and Investment Strategies
Genetic Algorithms and Investment Strategies
Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations
Proceedings of the Third European Conference on Advances in Artificial Life
Modelling Bounded Rationality Using Evolutionary Techniques
Selected Papers from AISB Workshop on Evolutionary Computing
EDDIE-automation, a decision support tool for financial forecasting
Decision Support Systems - Special issue: Data mining for financial decision making
Development of an artificial market model based on a field study
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
An automated FX trading system using adaptive reinforcement learning
Expert Systems with Applications: An International Journal
A simple but theoretically-motivated method to control bloat in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Guest editorial agent-based modeling of evolutionary economic systems
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Computational learning techniques for intraday FX trading using popular technical indicators
IEEE Transactions on Neural Networks
Complex Task Allocation in Mobile Surveillance Systems
Journal of Intelligent and Robotic Systems
Market-based framework for mobile surveillance systems
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
| Hi-index | 0.00 |
Stock markets are very important in modern societies and their behavior has serious implications for a wide spectrum of the world's population. Investors, governing bodies, and society as a whole could benefit from better understanding of the behavior of stock markets. The traditional approach to analyzing 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 their results. This motivates alternative methods. In this paper, we report an artificial financial market and its use in studying the behavior of stock markets. This is an endogenous market, with which we model technical, fundamental, and noise traders. Nevertheless, our primary focus is on the technical traders, which are sophisticated genetic programming based agents that coevolve (by learning based on their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. With this endogenous artificial market, we identify the conditions under which the statistical properties of price series in the artificial market resemble some of the properties of real financial markets. By performing a careful exploration of the most important aspects of our simulation model, we determine the way in which the factors of such a model affect the endogenously generated price. Additionally, we model the pressure to beat the market by a behavioral constraint imposed on the agents reflecting the Red Queen principle in evolution. We have demonstrated how evolutionary computation could play a key role in studying stock markets, mainly as a suitable model for economic learning on an agent-based simulation.