Soft Computing

, Volume 15, Issue 1, pp 37–50 | Cite as

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

  • Wei Yan
  • Christopher D. ClackEmail author


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.


Genetic Programming Hedge Fund Vote System Sharpe Ratio Bear Market 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank Dr Gerard Vila and Prospect Wealth Management for suggestions and discussions, SIAM Capital for financial support, and Reuters for access to financial data. We also thank the anonymous referees for their helpful comments.


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity College LondonLondonUK
  2. 2.Financial Computing, Department of Computer ScienceUniversity College LondonLondonUK

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