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Conference papers

Financial Data Mining with Genetic Programming: A Survey and Look Forward

Nicolas Navet 1 Shu-Heng Chen 2
1 TRIO - Real time and interoperability
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Genetic Programming (GP) is an appealing machine-learning technique for tackling financial engineering problems: it belongs to the family of evolutionary algorithms that have proven to be remarkably successful at handling complex optimization problems, and possesses the unique feature of producing solutions under a symbolic form that can be understood and analyzed by humans. Over the last decade, GP has been applied to generate financial trading strategies, forecast stocks and options prices, or grasp some insight into the dynamics of the markets and the behavior of the agents. In this paper, we first provide a brief survey of the existing studies, then highlight fields of investigations that, we believe, should lead to enhance the applicability and efficiency of GP in the financial domain.
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Submitted on : Monday, August 27, 2007 - 4:16:45 PM
Last modification on : Friday, February 4, 2022 - 3:31:54 AM
Long-term archiving on: : Friday, April 9, 2010 - 1:11:56 AM


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  • HAL Id : inria-00168352, version 1



Nicolas Navet, Shu-Heng Chen. Financial Data Mining with Genetic Programming: A Survey and Look Forward. 56th Session of the International Statistical Institute (ISI 2007), Jul 2007, Lisboa, Portugal. ⟨inria-00168352⟩



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