Evolving Robust GP Solutions for Hedge Fund Stock Selection in Emerging Markets.
GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2.
(pp. 2234 - 2241).
ASSOC COMPUTING MACHINERY
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.
|Title:||Evolving Robust GP Solutions for Hedge Fund Stock Selection in Emerging Markets|
|Event:||Annual Conference of Genetic and Evolutionary Computation Conference|
|Dates:||2007-07-07 - 2007-07-11|
|Keywords:||Genetic Programming, Diversity, Phenotype, Finance, Adaptation, Dynamic Environment|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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