- author = "Serafin Martinez-Jaramillo and Edward P. K. Tsang",
- title = "An Heterogeneous, Endogenous and Coevolutionary {GP}-Based Financial Market",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2009",
- month = feb,
- volume = "13",
- number = "1",
- pages = "33--55",
- keywords = "genetic algorithms, genetic programming, economics, multi-agent systems, pricing, series (mathematics), statistical analysis, stock markets, Red Queen principle, agent-based simulation, analytical models, behavioral constraint, coevolutionary GP-based financial market, economic learning, endogenous artificial market, evolutionary computation, fitness function, genetic programming based agents, homogeneous investors, investment opportunity, noise traders, perfect rationality, price generation, price series, real financial markets, statistical property, stock markets, technical traders",
- ISSN = "1089-778X",
-
DOI = "
doi:10.1109/TEVC.2008.2011401",
- size = "23 pages",
-
abstract = "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 co- evolve (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.",
- notes = "also known as \cite{4769014}",
