Piecewise nonlinear goal-directed CPPI strategy
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Chen:2007:ESA,
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author = "J. S. Chen and Benjamin Penyang Liao",
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title = "Piecewise nonlinear goal-directed CPPI strategy",
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journal = "Expert Systems with Applications",
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year = "2007",
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volume = "33",
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number = "4",
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pages = "857--869",
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month = nov,
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keywords = "genetic algorithms, genetic programming, Portfolio
insurance strategy, Goal-directed strategy, Piecewise
linear GDCPPI strategy, Piecewise nonlinear GDCPPI
strategy",
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DOI = "doi:10.1016/j.eswa.2006.07.001",
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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
strategy.",
- }
Genetic Programming entries for
Jiah-Shing Chen
Benjamin Penyang Liao
Citations