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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #81041 A Multiobjective Optimization Approach for Market Timing
sameAs	A Multiobjective Optimization Approach for Market Timing
http://eprints.org/ontology/hasAccepted	A Multiobjective Optimization Approach for Market Timing (PDF)
http://eprints.org/ontology/hasDocument	A Multiobjective Optimization Approach for Market Timing (PDF) A Multiobjective Optimization Approach for Market Timing (Other) A Multiobjective Optimization Approach for Market Timing (Other) A Multiobjective Optimization Approach for Market Timing (Other) A Multiobjective Optimization Approach for Market Timing (Other) A Multiobjective Optimization Approach for Market Timing (Other)
dc:hasVersion	A Multiobjective Optimization Approach for Market Timing (PDF)
Title	A Multiobjective Optimization Approach for Market Timing
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2020-06
Creator	Fernando E.B. Otero Ismail Mohamed
status	peer reviewed published
Publisher	ACM
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.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/81041#authors
presented at	Genetic and Evolutionary Computation Conference (GECCO ’20)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #81041 A Multiobjective Optimization Approach for Market Timing

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