Multiobjective genetic programming for financial portfolio management in dynamic environments

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  title =        "Multiobjective genetic programming for financial
                 portfolio management in dynamic environments",
  author =       "Ghada Nasr Aly Hassan",
  school =       "Department of Computer Science, University College
  year =         "2010",
  bibsource =    "OAI-PMH server at",
  language =     "eng",
  oai =          "",
  type =         "Doctoral",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "160 pages",
  abstract =     "Multiobjective (MO) optimisation is a useful technique
                 for evolving portfolio optimisation solutions that span
                 a range from high-return/high-risk to
                 low-return/low-risk. The resulting Pareto front would
                 approximate the risk/reward Efficient Frontier [Mar52],
                 and simplifies the choice of investment model for a
                 given client{'}s attitude to risk.

                 However, the financial market is continuously changing
                 and it is essential to ensure that MO solutions are
                 capturing true relationships between financial factors
                 and not merely over fitting the training data. Research
                 on evolutionary algorithms in dynamic environments has
                 been directed towards adapting the algorithm to improve
                 its suitability for retraining whenever a change is
                 detected. Little research focused on how to assess and
                 quantify the success of multiobjective solutions in
                 unseen environments. The multiobjective nature of the
                 problem adds a unique feature to be satisfied to judge
                 robustness of solutions. That is, in addition to
                 examining whether solutions remain optimal in the new
                 environment, we need to ensure that the solutions
                 relative positions previously identified on the Pareto
                 front are not altered.

                 This thesis investigates the performance of
                 Multiobjective Genetic Programming (MOGP) in the
                 dynamic real world problem of portfolio optimisation.
                 The thesis provides new definitions and statistical
                 metrics based on phenotypic cluster analysis to
                 quantify robustness of both the solutions and the
                 Pareto front. Focusing on the critical period between
                 an environment change and when retraining occurs, four
                 techniques to improve the robustness of solutions are
                 examined. Namely, the use of a validation data set;
                 diversity preservation; a novel variation on mating
                 restriction; and a combination of both diversity
                 enhancement and mating restriction. In addition,
                 preliminary investigation of using the robustness
                 metrics to quantify the severity of change for optimum
                 tracking in a dynamic portfolio optimisation problem is
                 carried out.

                 Results show that the techniques used offer
                 statistically significant improvement on the solutions'
                 robustness, although not on all the robustness criteria
                 simultaneously. Combining the mating restriction with
                 diversity enhancement provided the best robustness
                 results while also greatly enhancing the quality of
  notes =        "Two supervisors, Christopher Clack and Philip

                 UCL internal:001466727",

Genetic Programming entries for Ghada Hassan