Abstract
This paper employs a genetic algorithm to evolve an optimized stock market prediction system. The prediction based on a range of technical indicators generates signals to indicate the price movement. The performance of the system is analyzed and compared to market movements as represented by its index. Also investment funds run by professional traders are selected to establish a relative measure of success. The results show that the evolved system outperforms the index and funds in different market environments.
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References
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© 2009 Springer-Verlag Berlin Heidelberg
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Jiang, H., Kang, L. (2009). Building Trade System by Genetic Algorithm. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_3
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DOI: https://doi.org/10.1007/978-3-642-04843-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04842-5
Online ISBN: 978-3-642-04843-2
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