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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Mostafa, M.M.: Forecasting Stock Exchange Movements Using Neural Networks: Empirical Evidence from Kuwait. Expert Systems with Applications 37(9), 6302–6309 (2010)
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)
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)
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)
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)
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)
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)
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)
Montana, D.: A Weighted Probabilistic Neural Network. In: Advances in Neural Information Processing Systems, pp. 1110–1117 (1992)
Canning, A., Gardner, E.: Partially Connected Models of Neural Networks. Journal of Physics A 21, 3275–3284 (1998)
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)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Adeli, H., Hung, S.: Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems. Wiley, New York (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, D., Chang, PC., Wu, JL., Zhou, C. (2012). A Partially Connected Neural Evolutionary Network for Stock Price Index Forecasting. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-24553-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24552-7
Online ISBN: 978-3-642-24553-4
eBook Packages: Computer ScienceComputer Science (R0)