Paper Info
Title
Investigating the effect of different GP algorithms on the non-stationary behavior of financial markets
Abstract
This paper extends a previous market microstructure model, where we used Genetic Programming (GP) as an inference engine for trading rules, and Self Organizing Maps as a clustering machine for those rules. Experiments in that work took place under a single financial market and investigated whether its behavior is non-stationary or cyclic. Results showed that the market's behavior was constantly changing and strategies that would not adapt to these changes, would become obsolete, and their performance would thus decrease over time. However, because experiments in that work were based on a specific GP algorithm, we are interested in this paper to prove that those results are independent of the choice of such algorithms. We thus repeat our previous tests under two more GP frameworks. In addition, while our previous work surveyed only a single market, in this paper we run tests under 10 markets, for generalization purposes. Finally, we deepen our analysis and investigate whether the performance of strategies, which have not co-evolved with the market, follows a continuous decrease, as it has been previously suggested in the agent-based artificial stock market literature. Results show that our previous results are not sensitive to the choice of GP. Strategies that do not co-evolve with the market, become ineffective. However, we do not find evidence for a continuous performance decrease of these strategies.
Year
DOI
Venue
2011
10.1109/CIFER.2011.5953568
Computational Intelligence for Financial Engineering and Economics
Keywords
Field
DocType
financial data processing,genetic algorithms,marketing data processing,multi-agent systems,self-organising feature maps,stock markets,agent-based artificial stock market literature,financial markets,genetic programming algorithm,market microstructure model,nonstationary behavior,self organizing maps,genetic programming,clustering algorithms,financial market,multi agent systems,radio frequency
Market microstructure,Economics,Algorithm,Single market,Multi-agent system,Genetic programming,Inference engine,Financial market,Stock market,Genetic algorithm
Conference
ISSN
ISBN
Citations 
pending
978-1-4244-9933-5
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Michael Kampouridis19416.60
Shu-heng Chen2589203.10
Edward P. K. Tsang389987.77