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Soft Computing
Erschienen in: Soft Computing 1/2011

01.01.2011 | Focus

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

verfasst von: Wei Yan, Christopher D. Clack

Erschienen in: Soft Computing | Ausgabe 1/2011

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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 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.
Vorheriger Artikel Multi-objective evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints
Nächster Artikel A mixed integer genetic algorithm used in biological and chemical defense applications

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Fußnoten
1
An alternative approach is to look for an adaptive solution, i.e. one that detects changes in the environment and responds by modifying its internal structure and the way that it operates. However, a similar question arises: in an unforgiving environment, would it have time to adapt and survive without prior exposure to extreme environments?
 
2
Connected networks of RNA sequences with identical structure.
 
3
A contrarian strategy might do the opposite—sell the high stocks and buy the low stocks, on the expectation that mean-reversion will occur and the high stocks will fall while the low stocks will rise.
 
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Metadaten
Titel
Evolving robust GP solutions for hedge fund stock selection in emerging markets
verfasst von
Wei Yan
Christopher D. Clack
Publikationsdatum
01.01.2011
Verlag
Springer-Verlag
Erschienen in
Soft Computing / Ausgabe 1/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-009-0511-4

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