Constructing portfolio investment strategy based on time adapting genetic network programming

Authors:
Yan Chen;Shingo Mabu;Etsushi Ohkawa;Kotaro Hirasawa
Affiliations:
Graduate school of Information, Production, and Systems, Waseda University, Kitakyushu, Fukuoka;Graduate school of Information, Production, and Systems, Waseda University, Kitakyushu, Fukuoka;Graduate school of Information, Production, and Systems, Waseda University, Kitakyushu, Fukuoka;Graduate school of Information, Production, and Systems, Waseda University, Kitakyushu, Fukuoka
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

The classical portfolio problem is a problem of distributing capital to a set of stocks. By adapting to the change of stock prices, this study proposes an portfolio investment strategy based on an evolutionary method named "Genetic Network Programming" (GNP). This method makes use of the information from Technical Indices and Candlestick Chart. The proposed portfolio model, consisting of technical analysis rules, are trained to generate investment advice. Experimental results on the Japanese stock market show that the proposed investment strategy using Time Adapting GNP (TA-GNP) method outperforms other traditional models in terms of both accuracy and efficiency. We also compared the experimental results of the proposed model with the conventional GNP based methods, GA and Buy & Hold method to confirm its effectiveness, and it is clarified that the proposed investment strategy is effective on the portfolio optimization problem.