Mohamed, Ismail and Otero, Fernando E.B. (2021) Building Market Timing Strategies Using Trend Representative Testing and Computational Intelligence Metaheuristics. In: Merelo, J and Garibaldi, J and Linares-Barranco, A and Warwick, K and Madani, K, eds. Computational Intelligence (IJCCI 2019). Studies in Computational Intelligence . Springer, pp. 29-54. ISBN 978-3-030-70593-0. E-ISBN 978-3-030-70594-7. (doi:10.1007/978-3-030-70594-7_2) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:92027)
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Official URL: https://doi.org/10.1007/978-3-030-70594-7_2 |
Abstract
Market timing, one of the core deciding when to buy or sell an asset of interest on a financial market. Market timing strategies can be built by using a collection of components or functions that process market context and return a recommendation on the course of action to take. In this chapter, we revisit the work presented in [20] on the application of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to the issue of market timing while using a novel approach for training and testing called Trend Representative Testing. We provide more details on the process of building trend representative datasets, as well as, introduce a new PSO variant with a different approach to pruning. Results show that the new pruning procedure is capable of reducing solution length while not adversely affecting the quality of the solutions in a statistically significant manner.
Item Type: | Book section |
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DOI/Identification number: | 10.1007/978-3-030-70594-7_2 |
Uncontrolled keywords: | Particle swarm optimization, Genetic algorithms, Market timing, Technical analysis |
Subjects: | Q Science > Q Science (General) > Q335 Artificial intelligence |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Fernando Otero |
Date Deposited: | 05 Dec 2021 00:13 UTC |
Last Modified: | 09 Dec 2021 10:35 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/92027 (The current URI for this page, for reference purposes) |
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