Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Introduction to genetic programming
Advances in genetic programming
Genetic programming for the acquisition of double auction market strategies
Advances in genetic programming
Computer
An introduction to genetic algorithms
An introduction to genetic algorithms
Survival and growth with a liability: optimal portfolio strategies in continuous time
Mathematics of Operations Research
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Genetic Algorithms and Investment Strategies
Genetic Algorithms and Investment Strategies
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Forecasting Stock Returns Using Genetic Programming in C++
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Parallel genetic programming: an application to trading models evolution
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Information Systems Frontiers
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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 behavior 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.