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Evolving Robust GP Solutions for Hedge Fund Stock Selection in Emerging Markets

Yan, W; Clack, CD; (2007) Evolving Robust GP Solutions for Hedge Fund Stock Selection in Emerging Markets. In: GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2. (pp. 2234 - 2241). ASSOC COMPUTING MACHINERY

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Abstract

Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment in which the GP solution must survive) are dynamic, unpredictable and unforgiving. How can GP be improved so that solutions are produced that are robust to non-trivial changes in the environment? We explore an approach that uses subsets of extreme environments during training.

Type:Proceedings paper
Title:Evolving Robust GP Solutions for Hedge Fund Stock Selection in Emerging Markets
Event:Annual Conference of Genetic and Evolutionary Computation Conference
Location:London, ENGLAND
Dates:2007-07-07 - 2007-07-11
ISBN-13:978-1-59593-697-4
Keywords:Genetic Programming, Diversity, Phenotype, Finance, Adaptation, Dynamic Environment
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

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