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.
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Lee, Js., Lee, S., Chang, S., Ahn, BH. (2005). A Comparison of GA and PSO for Excess Return Evaluation in Stock Markets. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_23
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DOI: https://doi.org/10.1007/11499305_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26319-7
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