Varying portfolio construction of stocks using genetic network programming with control nodes

Authors:
Etsushi Ohkawa;Yan Chen;Shingo Mabu;Kaoru Shimada;Kotaro Hirasawa
Affiliations:
Waseda University, Kitakyushu, Fukuoka, Japan;Waseda University, Kitakyushu, Fukuoka, Japan;Waseda University, Kitakyushu, Fukuoka, Japan;Waseda University, Kitakyushu, Fukuoka, Japan;Waseda University, Kitakyushu, Fukuoka, Japan
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

Citing 1
Cited 0

A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning

Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new evolutionary method named "Genetic Network Programming with Control Nodes, GNPcn" has been proposed and applied to determine the timing of buying and selling stocks. GNPcn represents its solution as a directed graph structure which has some useful features inherently. For example, GNPcn has the implicit memory function which memorizes the past action sequences of agents and GNPcn can re-use nodes repeatedly in the network flow, so highly compact graph structures can be made. GNPcn can improve the strategy of buying and selling stocks of multi issues. Its effectiveness is confirmed by some simulations.