Construction of portfolio optimization system using genetic network programming with control nodes
Yan Chen, Shingo Mabu, Kaoru Shimada, Kotaro Hirasawa · 2008
Many evolutionary computation methods applied to the financial field have been reported. A new evolutionary method named "Genetic Network Programming" (GNP) has been developed and applied to the stock market recently. In this paper a portfolio optimization system based on Genetic Network Programming with control nodes is presented, which makes use of the information from Technical Indices and Candlestick Chart. The proposed optimization system, consisting of technical analysis rules, are trained to generate trading advice. The experimental results on the Japanese stock market show that the proposed optimization system using GNP with control nodes outperforms other traditional models and Buy&Hold method in terms of both accuracy and efficiency, and its effectiveness has been confirmed.