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文章基本信息

  • 标题:Genetic Algorithm Based Backpropagation Neural Network Performs better than Backpropagation Neural Network in Stock Rates Prediction
  • 作者:Asif Ullah Khan ; T. K. Bandopadhyaya ; Sudhir Sharma
  • 期刊名称:International Journal of Computer Science and Network Security
  • 印刷版ISSN:1738-7906
  • 出版年度:2008
  • 卷号:8
  • 期号:7
  • 页码:162-166
  • 出版社:International Journal of Computer Science and Network Security
  • 摘要:The prevailing notion in society is that wealth brings comfort and luxury, so it is a challenging and daunting task to find out which is more effective and accurate method for stock rate prediction so that a buy or sell signal can be generated for given stocks. This paper presents a Back Propagation Neural Network and Genetic Based Backpropagation Neural Network to predict the stock price of the day. Stock rate prediction accuracy of backpropagation neural network and genetic algorithm based backpropagation neural network has been compared. The results showed that the genetic algorithm based backpropagation neural network predict stock price more accurately as compared to backpropagation neural network.
  • 关键词:Backpropagation Neural Network; Genetic Algorithm Based Backpropagation Neural Network; Technical Analysis.
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