Evolving robust GP solutions for hedge fund stock selection in emerging markets

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...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2011, Vol.15 (1), p.37-50
Main Authors: Yan, Wei, Clack, Christopher D.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Intelligence
Control
Datasets
Engineering
Evolution
Extreme environments
Focus
Genetic algorithms
Hedge funds
Mathematical Logic and Foundations
Mechatronics
Robotics
Robustness
Securities markets
Volatility
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Summary: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 two new approaches. The first approach uses subsets of extreme environments during training and the second approach uses a voting committee of GP individuals with differing phenotypic behaviour.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-009-0511-4