Stock Price Prediction using Neural Networks with RasID-GA

被引：2

作者：

Mabu, Shingo ^{[1
]}

Chen, Yan ^{[1
]}

Sohn, Dongkyu ^{[1
]}

Shimada, Kaoru ^{[2
]}

Hirasawa, Kotaro ^{[1
]}

机构：

[1] Waseda Univ, Grad Sch Informat Prod & Syst, Wakamatsu Ku, Kitakyushu, Fukuoka 8080135, Japan

[2] Waseda Univ, Informat Prod & Syst Res Ctr, Wakamatsu Ku, Kitakyushu, Fukuoka 8080135, Japan

来源：

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING | 2009年 / 4卷 / 03期

关键词：

optimization; random search; intensified; diversified search; RANDOM SEARCH;

D O I：

10.1002/tee.20423

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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. (C) 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

引用

页码：392 / 403

页数：12

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