Goal-Directed Portfolio Insurance Strategies

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Benjamin Penyang Liao",
  title =        "Goal-Directed Portfolio Insurance Strategies",
  school =       "Department of Information Management, National Central
  year =         "2006",
  address =      "ROC",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, forest
                 genetic programming, GDPI, implicit piecewise linear
                 GDPI strategy, piecewise nonlinear GDPI strategy,
                 piecewise linear GDPI strategy, goal-directed strategy,
                 Portfolio insurance strategy",
  URL =          "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search/view_etd?URN=87443004",
  URL =          "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search-c/getfile?URN=87443004&filename=87443004.pdf",
  size =         "120 pages",
  abstract =     "Traditional portfolio insurance (PI) strategy such as
                 constant proportion portfolio insurance (CPPI) only
                 considers the floor constraint but not the goal aspect.
                 There seems to be two contradictory risk-attitudes
                 according to different studies: low wealth risk
                 aversion and high wealth risk aversion. Although low
                 wealth risk aversion can be explained by the CPPI
                 strategy, high wealth risk aversion can not be
                 explained by CPPI. We argue that these contradictions
                 can be explained from two perspectives: the portfolio
                 insurance perspective and the goal-directed
                 perspective. This study 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 strategy and
                 CPPI strategy. This M position guides investors to
                 apply CPPI strategy or GD strategy depending on whether
                 the 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. Moreover, we extend the piecewise GDCPPI
                 strategy to the piecewise GDTIPP strategy by applying
                 the time invariant portfolio protection (TIPP) idea,
                 which allows variable floor and goal comparing to the
                 constant floor and goal for piecewise GDCPPI strategy.
                 Therefore, piecewise GDCPPI strategy and piecewise
                 GDTIPP strategy are two special cases of piecewise
                 goal-directed portfolio insurance (GDPI) strategies.
                 When building the piecewise nonlinear GDPI strategies,
                 it is difficult to preassign an explicit $M$ value when
                 the structures of nonlinear PI strategies and nonlinear
                 GD strategies are uncertain. To solve this problem, we
                 then apply the minimum function to build the piecewise
                 nonlinear GDPI strategies, which these strategies still
                 apply the $M$ concept but operate it in an implicit
                 way. Also, the piecewise linear GDPI strategies can
                 attain the same effect by applying the minimum function
                 to form implicit piecewise linear GDPI strategies. This
                 study performs some experiments to justify our
                 propositions for piecewise GDPI strategies: there are
                 nonlinear GDPI strategies that can outperform the
                 linear GDPI strategies and there are some data-driven
                 techniques that can find better linear GDPI strategies
                 than the solutions found by Brownian technique. The GA
                 and forest genetic programming (GP) are two data-drive
                 techniques applied in this study. This study applies
                 genetic algorithm (GA) technique to find better
                 piecewise linear GDPI strategy parameters than those
                 under Brownian motion assumption. This study adapts
                 traditional GP to a forest GP in order to generate
                 piecewise nonlinear GDPI strategies. The statistical
                 tests show that the GP strategy outperforms the GA
                 strategy which in turn outperforms the Brownian
                 strategy. These statistical tests therefore justify our

Genetic Programming entries for Benjamin Penyang Liao