An Empirical Study of Multi-Objective Algorithms for Stock Ranking

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Becker:2007:GPTP,
  author =       "Ying L. Becker and Harold Fox and Peng Fei",
  title =        "An Empirical Study of Multi-Objective Algorithms for
                 Stock Ranking",
  booktitle =    "Genetic Programming Theory and Practice {V}",
  year =         "2007",
  editor =       "Rick L. Riolo and Terence Soule and Bill Worzel",
  series =       "Genetic and Evolutionary Computation",
  chapter =      "14",
  pages =        "239--259",
  address =      "Ann Arbor",
  month =        "17-19" # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-387-76308-8",
  DOI =          "doi:10.1007/978-0-387-76308-8_14",
  size =         "21 pages",
  abstract =     "Quantitative models for stock selection and portfolio
                 management face the challenge of determining the most
                 efficacious factors, and how they interact, from large
                 amounts of financial data. Genetic programming using
                 simple objective fitness functions has been shown to be
                 an effective technique for selecting factors and
                 constructing multi-factor models for ranking stocks,
                 but the resulting models can be somewhat unbalanced in
                 satisfying the multiple objectives that portfolio
                 managers seek: large excess returns that are consistent
                 across time and the cross-sectional dimensions of the
                 investment universe. In this study, we implement and
                 evaluate three multi-objective algorithms to
                 simultaneously optimise the information ratio,
                 information coefficient, and intra-fractile hit rate of
                 a portfolio. These algorithms the constrained fitness
                 function, sequential algorithm, and parallel algorithm
                 take widely different approaches to combine these
                 different portfolio metrics. The results show that the
                 multi-objective algorithms do produce well-balanced
                 portfolio performance, with the constrained fitness
                 function performing much better than the sequential and
                 parallel multi-objective algorithms. Moreover, this
                 algorithm generalises to the held-out test data set
                 much better than any of the single fitness
                 algorithms.",
  affiliation =  "Advanced Research Center, State Street Global Advisors
                 Boston MA 02111",
  notes =        "part of \cite{Riolo:2007:GPTP} published Jan 2008",
}

Genetic Programming entries for Ying Becker Harold Fox Peng Fei

Citations