The Syntax of Stock Selection: Grammatical Evolution of a Stock Picking Model

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@InProceedings{mcgee_etal:cec2010,
  author =       "Richard McGee and Michael O'Neill and 
                 Anthony Brabazon",
  title =        "The Syntax of Stock Selection: Grammatical Evolution
                 of a Stock Picking Model",
  booktitle =    "2010 IEEE World Congress on Computational
                 Intelligence",
  pages =        "4347--4354",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  isbn13 =       "978-1-4244-6910-9",
  DOI =          "doi:10.1109/CEC.2010.5586001",
  abstract =     "A significant problem in the area of stock selection
                 is that of identifying the factors that affect a
                 security's return. While modern portfolio theory
                 suggests a linear multi-factor model in the form of
                 Arbitrage Pricing Theory it does not suggest the
                 identity, or even the number, of risk factors in the
                 model. Candidate factors for inclusion in a fundamental
                 model can include hundreds of data points for each firm
                 and with thousands of firms in the fund manager's
                 selection universe the model specification problem
                 encompasses a large, computationally intense search
                 space. Grammatical Evolution (GE) is a form of
                 evolutionary computing that has been used successfully
                 in model induction problems involving large search
                 spaces. GE is applied to evolve a stock selection model
                 with a customised mapping process developed
                 specifically to enhance the performance of evolutionary
                 operators for this problem. Stock selection models are
                 rated using fitness functions commonly employed in
                 asset management; the information coefficient and the
                 inter-quantile return spread. The findings of the paper
                 indicate that evolutionary computing is an excellent
                 tool for the development of stock picking models.",
  notes =        "WCCI 2010. Also known as \cite{5586001}",
}

Genetic Programming entries for Richard McGee Michael O'Neill Anthony Brabazon

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