- title = "Multiobjective genetic programming for financial portfolio management in dynamic environments",
- author = "Ghada Nasr Aly Hassan",
- school = "Department of Computer Science, University College London",
- year = "2010",
- type = "Doctoral",
- address = "UK",
- keywords = "genetic algorithms, genetic programming, MOGP",
- bibsource = "OAI-PMH server at eprints.ucl.ac.uk",
- language = "eng",
- oai = "oai:eprints.ucl.ac.uk.OAI2:20456",
-
URL = "
http://discovery.ucl.ac.uk/20456/1/20456.pdf",
-
URL = "
https://discovery.ucl.ac.uk/id/eprint/20456/",
-
URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.565018",
- 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 clients 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 solutions.",
- notes = "Two supervisors, Christopher Clack and Philip Treleaven. uk.bl.ethos.565018 ISNI: 0000 0004 2728 0858 UCL internal:001466727",
