Evolving dynamic trade execution strategies using grammatical evolution

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Title: Evolving dynamic trade execution strategies using grammatical evolution
Authors: Cui, Wei
Brabazon, Anthony
O'Neill, Michael
Permanent link: http://hdl.handle.net/10197/2161
Date: 2010
Abstract: Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to potential use of these methods for efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. In this paper we use a GE algorithm to discover dynamic, efficient, trade execution strategies which adapt to changing market conditions. The strategies are tested in an artificial limit order market. GE was found to be able to evolve quality trade execution strategies which are highly competitive with two benchmark trade execution strategies.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): 2010 Springer Verlag
Keywords: Finance;Grammatical evolution;Trade;Risk management
Subject LCSH: Evolutionary computation
International finance
Financial risk
Financial instruments
DOI: 10.1007/978-3-642-12242-2_20
Language: en
Status of Item: Peer reviewed
Is part of: Di Chio, C. ... et al. (eds.). Applications of Evolutionary Computation : EvoApplications 2010: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoMUSART, and EvoTRANSLOG Istanbul, Turkey, April 7-9, 2010 Proceedings, Part II
Conference Details: Paper presented at EvoFin 2010, 4th European Event on Evolutionary and Natural Computation in Finance and Economics, as part of EvoStar 2010, 7-9 April 2010, Istanbul
Appears in Collections:FMC² Research Collection
Computer Science Research Collection
Business Research Collection
CASL Research Collection

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