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Using Population-based Metaheuristics and Trend Representative Testing to Compose Strategies for Market Timing

Mohamed, Ismail, Otero, Fernando E.B. (2019) Using Population-based Metaheuristics and Trend Representative Testing to Compose Strategies for Market Timing. In: Proceedings of the 11th International Joint Conference on Computational Intelligence. . pp. 59-69. SciTePress ISBN 978-989-758-384-1. (doi:10.5220/0008066100590069) (KAR id:77371)

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Market Timing is the capacity of deciding when to buy or sell a given asset on a financial market. Market Timing strategies are usually composed of components that process market context and return a recommendation whether to buy or sell. The main issues with composing market timing strategies are twofold: (i) selecting the signal generating components; and (ii) tuning their parameters. In previous work, researchers usually attempt to either tune the parameters of a set of components or select amongst a number of components with predetermined parameter values. In this paper, we approach market timing as one integrated problem and propose to solve it with two variants of Particle Swarm Optimization (PSO). We compare the performance of PSO against a Genetic Algorithm (GA), the most widely used metaheuristic in the domain of market timing. We also propose the use of trend representative testing to circumvent the issue of overfitting commonly associated with step-forward testing. Results show PSO to be competitive with GA, and that trend representative testing is an effective method of exposing strategies to various market conditions during training and testing.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.5220/0008066100590069
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 13 Oct 2019 00:09 UTC
Last Modified: 14 Oct 2019 11:18 UTC
Resource URI: (The current URI for this page, for reference purposes)
Otero, Fernando E.B.:
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