Robustness of multiple objective GP stock-picking in unstable financial markets: real-world applications track

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{DBLP:conf/gecco/HassanC09,
  author =       "Ghada Hassan and Christopher D. Clack",
  title =        "Robustness of multiple objective {GP} stock-picking in
                 unstable financial markets: real-world applications
                 track",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1513--1520",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  doi =          "doi:10.1145/1569901.1570104",
  abstract =     "Multiple Objective Genetic Programming (MOGP) is a
                 promising stock-picking technique for fund managers,
                 because the Pareto front approximates the risk/reward
                 Efficient Frontier and simplifies the choice of
                 investment model for a given client's attitude to
                 risk.

                 Unfortunately GP solutions don't work well if used in
                 an environment that is different from the training
                 environment, and the financial markets are notoriously
                 unstable, often lurching from one market context to
                 another (e.g. {"}bull{"} to {"}bear{"}). This turns out
                 to be a hard problem -- simple dynamic adaptation
                 methods are insufficient and robust behaviour of
                 solutions becomes extremely important.

                 In this paper we provide the first known empirical
                 results on the robustness of MOGP solutions in an
                 unseen environment consisting of real-world financial
                 data. We focus on two well-known mechanisms to
                 determine which leads to the more robust solutions:
                 Mating Restriction, and Diversity Preservation. We
                 introduce novel metrics for Pareto front robustness,
                 and a novel variation on Mating Restriction, both based
                 on phenotypic cluster analysis.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
}

Genetic Programming entries for Ghada Hassan Christopher D Clack