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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chang, P.C., Liu, C.H., Lin, J.L., Fan, C.Y., Ng, C.S.P.: A Neural Network with a Case Based Dynamic Window for Stock Trading Prediction. Expert Systems with Applications 36(3 PART 2), 6889–6898 (2009)CrossRefGoogle Scholar
  2. 2.
    Mostafa, M.M.: Forecasting Stock Exchange Movements Using Neural Networks: Empirical Evidence from Kuwait. Expert Systems with Applications 37(9), 6302–6309 (2010)CrossRefGoogle Scholar
  3. 3.
    Chang, P.-C., Liu, C.-H., Fan, C.-Y., Lin, J.-L., Lai, C.-M.: An ensemble of neural networks for stock trading decision making. In: Huang, D.-S., Jo, K.-H., Lee, H.-H., Kang, H.-J., Bevilacqua, V. (eds.) ICIC 2009. LNCS, vol. 5755, pp. 1–10. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Li, F., Liu, C.: Application Study of BP Neural Network on Stock Market Prediction. In: Ninth International Conference on Hybrid Intelligent Systems, Shgenyang, China, pp. 174–178 (2009)Google Scholar
  5. 5.
    Kim, K.J., Han, I.: Genetic Algorithms Approach to Feature Discretization in Artificial Neural Networks for the Prediction of Stock Price Index. Expert Systems with Applications 19, 125–132 (2000)CrossRefGoogle Scholar
  6. 6.
    Mandziuk, J.: Jaruszewicz, m.: Neuro-evolutionary approach to stock market prediction. In: Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, pp. 12–17 (August 2007)Google Scholar
  7. 7.
    Kim, S.H., Chun, H.S.: Graded Forecasting Using an Array of Bipolar Predictions: Application of Probabilistic Neural Networks to a Stock Market Index. International Journal of Forecasting 14, 323–337 (1998)CrossRefGoogle Scholar
  8. 8.
    Chang, P.C., Fan, C.Y., Liu, C.H.: Integrating a Piecewise Linear Presentation Method and a Neural Network Model for Stock Trading Points Prediction. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 39(1), 80–92 (2009)CrossRefGoogle Scholar
  9. 9.
    Chang, P.C., Liu, C.H.: A TSK type Fuzzy Rule Based System for Stock Price Prediction. Expert Systems with Applications 34(1), 135–144 (2008)CrossRefGoogle Scholar
  10. 10.
    Montana, D.: A Weighted Probabilistic Neural Network. In: Advances in Neural Information Processing Systems, pp. 1110–1117 (1992)Google Scholar
  11. 11.
    Canning, A., Gardner, E.: Partially Connected Models of Neural Networks. Journal of Physics A 21, 3275–3284 (1998)CrossRefzbMATHGoogle Scholar
  12. 12.
    Hubert, C.: Design of Fully and Partially Connected Random Neural Networks for Pattern Completion. In: Mira, J., Cabestany, J., Prieto, A.G. (eds.) IWANN 1993. LNCS, vol. 686, pp. 137–142. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  13. 13.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  14. 14.
    Adeli, H., Hung, S.: Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems. Wiley, New York (1995)zbMATHGoogle Scholar

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

Personalised recommendations