Stock price prediction using neural networks with RasID-GA

Authors

  • Shingo Mabu,

    Member
    1. Graduate School of Information, Production and Systems, Waseda University 2-7, Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka, 808-0135, Japan
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  • Yan Chen,

    Non-Member
    1. Graduate School of Information, Production and Systems, Waseda University 2-7, Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka, 808-0135, Japan
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  • Dongkyu Sohn,

    Non-Member
    1. Graduate School of Information, Production and Systems, Waseda University 2-7, Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka, 808-0135, Japan
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  • Kaoru Shimada,

    Member
    1. Information, Production and Systems Research Center, Waseda University 2-2, Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka, 808-0135, Japan
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  • Kotaro Hirasawa

    Member, Corresponding author
    1. Graduate School of Information, Production and Systems, Waseda University 2-7, Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka, 808-0135, Japan
    • Graduate School of Information, Production and Systems, Waseda University 2-7, Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka, 808-0135, Japan
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Abstract

In general, neural networks are widely used in pattern recognition, system modeling and prediction, and can model complex nonlinear systems. In the previous work, we proposed a novel training algorithm, Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA), for training the multibranch recurrent neural networks recently developed. In this paper, RasID-GA has been applied to predict stock market prices using the multibranch feed forward neural networks. We predicted the next day's closing stock price with several past closing stock prices. We used the stock prices of 20 brands for 720 days in order to evaluate the generalization ability of the proposed method. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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