Lessons Learned Using Genetic Programming in a Stock Picking Context

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

  author =       "Michael Caplan and Ying Becker",
  title =        "Lessons Learned Using Genetic Programming in a Stock
                 Picking Context",
  booktitle =    "Genetic Programming Theory and Practice {II}",
  year =         "2004",
  editor =       "Una-May O'Reilly and Tina Yu and Rick L. Riolo and 
                 Bill Worzel",
  chapter =      "6",
  pages =        "87--102",
  address =      "Ann Arbor",
  month =        "13-15 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, stock
                 selection, data mining, fitness functions, quantitative
                 portfolio management",
  ISBN =         "0-387-23253-2",
  DOI =          "doi:10.1007/0-387-23254-0_6",
  abstract =     "This is a narrative describing the implementation of a
                 genetic programming technique for stock picking in a
                 quantitatively driven, risk-controlled, US equity
                 portfolio. It describes, in general, the problems that
                 the authors faced in their portfolio context when using
                 genetic programming techniques and in gaining
                 acceptance of the technique by a skeptical audience. We
                 discuss in some detail the construction of the fitness
                 function, the genetic programming system's
                 parametrisation (including data selection and internal
                 function choice), and the interpretation and
                 modification of the generated programs for eventual
  notes =        "part of \cite{oreilly:2004:GPTP2}",

Genetic Programming entries for Michael Caplan Ying Becker