A Partially Connected Neural Evolutionary Network for Stock Price Index Forecasting

  • Didi Wang
  • Pei-Chann Chang
  • Jheng-Long Wu
  • Changle Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6840)

Abstract

This paper proposes a novel partially connected neural evolutionary model (Parcone) architecture to simulate the relationship of stock and technical indicators to predict the stock price index. Different from artificial neural networks, the architecture has corrected three drawbacks: (1) connection between neurons of is random; (2) there can be more than one hidden layer; (3) evolutionary algorithm is employed to improve the learning algorithm and train weights. The more hidden knowledge stored within the historic time series data are needed in order to improve expressive ability of network. The genetically evolved weights mitigate the well-known limitations of gradient descent algorithm. In addition, the activation function is not defined by sigmoid function but sin(x). The experimental results show that Parcone can make the progress concerning the stock price index and it’s very promising to calculate the predictive percentage by simulation results of proposed evolutionary system.

Keywords

Hide Layer Probabilistic Neural Network Generalize Regression Neural Network Gradient Descent Algorithm Technical Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Didi Wang
    • 2
  • Pei-Chann Chang
    • 1
  • Jheng-Long Wu
    • 1
  • Changle Zhou
    • 2
  1. 1.Department of Information ManagementYuan Ze UniversityTaoyuanTaiwan
  2. 2.Cognitive Science Department, Fujian Key Laboratory of the Brain-like Intelligent SystemsXiamen UniversityXiamenChina

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