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Building Market Timing Strategies Using Trend Representative Testing and Computational Intelligence Metaheuristics

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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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
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
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