Piecewise nonlinear goal-directed CPPI strategy

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

  author =       "J. S. Chen and Benjamin Penyang Liao",
  title =        "Piecewise nonlinear goal-directed CPPI strategy",
  journal =      "Expert Systems with Applications",
  year =         "2007",
  volume =       "33",
  number =       "4",
  pages =        "857--869",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, Portfolio
                 insurance strategy, Goal-directed strategy, Piecewise
                 linear GDCPPI strategy, Piecewise nonlinear GDCPPI
  DOI =          "doi:10.1016/j.eswa.2006.07.001",
  abstract =     "Traditional portfolio insurance (PI) strategy, such as
                 constant proportion portfolio insurance (CPPI), only
                 considers the floor constraint but not the goal aspect.
                 This paper proposes a goal-directed (GD) strategy to
                 express an investor's goal-directed trading behaviour
                 and combines this floor-less GD strategy with the
                 goal-less CPPI strategy to form a piecewise linear
                 goal-directed CPPI (GDCPPI) strategy. The piecewise
                 linear GDCPPI strategy shows that there is a wealth
                 position M at the intersection of the GD and CPPI
                 strategies. This M position guides investors to apply
                 the CPPI strategy or the GD strategy depending on
                 whether current wealth is less than or greater than M,
                 respectively. In addition, we extend the piecewise
                 linear GDCPPI strategy to a piecewise nonlinear GDCPPI
                 strategy. This paper applies genetic algorithm (GA)
                 technique to find better piecewise linear GDCPPI
                 strategy parameters than those under the Brownian
                 motion assumption. This paper also applies forest
                 genetic programming (GP) technique to generate the
                 piecewise nonlinear GDCPPI strategy. The statistical
                 tests show that the GP strategy outperforms the GA
                 strategy which in turn outperforms the Brownian

Genetic Programming entries for Jiah-Shing Chen Benjamin Penyang Liao