Genetic programming for quantitative stock selection

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Ying L. Becker and Una-May O'Reilly",
  title =        "Genetic programming for quantitative stock selection",
  booktitle =    "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
                 Genetic and Evolutionary Computation",
  year =         "2009",
  editor =       "Lihong Xu and Erik D. Goodman and Guoliang Chen and 
                 Darrell Whitley and Yongsheng Ding",
  bibsource =    "DBLP,",
  pages =        "9--16",
  address =      "Shanghai, China",
  organisation = "SigEvo",
  DOI =          "doi:10.1145/1543834.1543837",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        jun # " 12-14",
  isbn13 =       "978-1-60558-326-6",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We provide an overview of using genetic programming
                 (GP) to model stock returns. Our models employ GP
                 terminals (model decision variables) that are financial
                 factors identified by experts. We describe the
                 multi-stage training, testing and validation process
                 that we have integrated with GP selection to be
                 appropriate for financial panel data and how the GP
                 solutions are situated within a portfolio selection
                 strategy. We share our experience with the pros and
                 cons of evolved linear and non-linear models, and
                 outline how we have used GP extensions to balance
                 different objectives of portfolio managers and control
                 the complexity of evolved models.",
  notes =        "Also known as \cite{DBLP:conf/gecco/BeckerO09} part of

Genetic Programming entries for Ying Becker Una-May O'Reilly