A comparison of GA and PSO for excess return evaluation in stock markets

Authors:
Ju-sang Lee;Sangook Lee;Seokcheol Chang;Byung-Ha Ahn
Affiliations:
Mechatronics Dept., Gwangju Institute of Science and Technology, Gwangju, Republic of Korea;Mechatronics Dept., Gwangju Institute of Science and Technology, Gwangju, Republic of Korea;Mechatronics Dept., Gwangju Institute of Science and Technology, Gwangju, Republic of Korea;Mechatronics Dept., Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
Venue:
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Year:
2005

Citing 2
Cited 3

The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best

Proceedings of the third international conference on Genetic algorithms
Generating trading rules on the stock markets with genetic programming

Computers and Operations Research

Particle swarm optimization of Bollinger bands

ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Construct a personal asset allocation models with particle swarm optimization: an example for wealth management

ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Identification of factors characterising volatility and firm-specific risk using ensemble classifiers

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV

Quantified Score

Hi-index	0.00

Visualization

Abstract

One of the important problems in financial markets is making the profitable stocks trading rules using historical stocks market data. This paper implemented Particle Swarm Optimization (PSO) which is a new robust stochastic evolutionary computation Algorithm based on the movement and intelligence of swarms, and compared it to a Genetic Algorithm (GA) for generating trading rules. The results showed that PSO shares the ability of genetic algorithm to handle arbitrary nonlinear functions, but with a much simpler implementation clearly demonstrates good possibilities for use in Finance.