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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: Lipson, H, (ed.) Proceedings of the 9th annual conference on Genetic and evolutionary computation. (pp. pp. 2234-2241). ACM: Association Computing Machinery: London, England, United Kingdom. Green open access

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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 (GECCO '07)
Location: London, ENGLAND
Dates: 07 July 2007 - 11 July 2007
ISBN-13: 978-1-59593-697-4
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/1276958.1277384
Publisher version: http://dx.doi.org/10.1145/1276958.1277384
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Genetic Programming, Diversity, Phenotype, Finance, Adaptation, Dynamic Environment
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10087084
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