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A Multiobjective Optimization Approach for Market Timing

Mohamed, Ismail, Otero, Fernando E.B. (2020) A Multiobjective Optimization Approach for Market Timing. In: GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. . pp. 22-30. ACM ISBN 978-1-4503-7128-5. (doi:10.1145/3377930.3390156) (KAR id:81041)

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https://doi.org/10.1145/3377930.3390156

Abstract

The introduction of electronic exchanges was a crucial point in history as it heralded the arrival of algorithmic trading. Designers of such systems face a number of issues, one of which is deciding when to buy or sell a given security on a financial market. Although Genetic Algorithms (GA) have been the most widely used to tackle this issue, Particle Swarm Optimization (PSO) has seen much lower adoption within the domain. In two previous works, the authors adapted PSO algorithms to tackle market timing and address the shortcomings of the previous approaches both with GA and PSO. The majority of work done to date on market timing tackled it as a single objective optimization problem, which limits its suitability to live trading as designers of such strategies will realistically pursue multiple objectives such as maximizing profits, minimizing exposure to risk and using the shortest strategies to improve execution speed. In this paper, we adapt both a GA and PSO to tackle market timing as a multiobjective optimization problem and provide an in depth discussion of our results and avenues of future research.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1145/3377930.3390156
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: 28 Apr 2020 16:28 UTC
Last Modified: 16 Feb 2021 14:12 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/81041 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
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