[go: up one dir, main page]

Skip to main content

Diagonal Log-Normal Generalized RBF Neural Network for Stock Price Prediction

  • Conference paper
  • First Online:
Advances in Neural Networks – ISNN 2014 (ISNN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8866))

Included in the following conference series:

Abstract

Stock price prediction is one of the most important topics in financial engineering. In this paper, for stock closing price prediction, we propose a diagonal log-normal generalized RBF neural network in which the diagonal log-normal density functions serve as the RBFs. Specifically, it utilizes the dynamic split-and-merge EM algorithm to select the number of hidden units (or RBFs) as well as the initial values of the parameters, and implements a synchronous LMS learning algorithm for parameter learning. It is demonstrated by the experiments that the diagonal log-normal generalized RBF neural network has a competitive performance on stock closing price prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Jiang, Y., Lin, Y.: The Application of RBF Neural Networks to Stock Price Forecasting. Mind and Computation 1(4), 413–419 (2007) (in Chinese)

    Google Scholar 

  2. Liu, H., Bai, Y.: Analysis of AR Model and Neural Network for Forecasting Stock Price. Mathmatics in Practice and Theory 41(4), 14–19 (2011) (in Chinese)

    Google Scholar 

  3. Liu, S., Ma, J.: The Application of Diagonal Generalized RBF Neural Network to Stock Price Prediction. China Sciencepaper Online (2014) (in Chinese)

    Google Scholar 

  4. Tsang, P.M., Kwok, P., Choy, S.O., et al.: Design and Implementation of NN5 for Hong Kong Stock Price Forcasting. Enginerring Applications of Artificial Intelligence 20(4), 453–461 (2007)

    Google Scholar 

  5. Fu, C., Fu, M., Que, J.: Prediction of Stock Price Base on Radial Basic Function Neural Networks. Technological Development of Enterprise 23(4), 14–15 (2004) (in Chinese)

    Google Scholar 

  6. Zheng, P., Ma, Y.: RBF Neural Network Based Sock Market Modeling and Forecasting. Journal of Tianjin University 33(4), 483–486 (2000) (in Chinese)

    Google Scholar 

  7. Lee, R.S.: iJADE Stock Advisor: An Intelligent Agent Based Stock Prediction System Using Hybrid RBF Recurrent Network. IEEE Transctions on Systems, Man, and Cybernetics, Part A: Systems and Humans 34(3), 421–428 (2004)

    Article  Google Scholar 

  8. Broomhead, D.S., Lowe, D.: Mutivariable Functional Interpolation and Adaptive Networks. Complex Systems 2, 321–355 (1988)

    Google Scholar 

  9. Huang, K., Wang, L., Ma, J.: Efficient Training of RBF Networks Via the BYY Automated Model Selection Learning Algorithms. In: Liu, D., Fei, S., Hou, Z.-G., Zhang, H., Sun, C. (eds.) ISNN 2007, Part I. LNCS, vol. 4491, pp. 1183–1192. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Ma, J., Qing, C.: Diagonal Generalized RBF Neural Network and Nonelinear Time Series Prediction. Journal of Signal Processing 29(12), 1609–1614 (2013) (in Chinese)

    Google Scholar 

  11. Yu, Y.: The Application of the Lognormal Distribution in the Stock Price Models. Journal of Langfang Teachers College (Natural Science Edition) 12(5), 69–72 (2012) (in Chinese)

    Google Scholar 

  12. Long, S., Xiang, L.: Emirical Analysis of Stock Price’s Lognormal Distribution. Journal of Huanggang Normal University 33(3), 9–11 (2013) (in Chinese)

    Google Scholar 

  13. Wang, L., Ma, J.: A Kurtosis and Skewness Based Criterion for Model Selection on Gaussian Mixture. In: The 2nd International Conference on Biomedical Engineering and Information, pp. 1–5 (2009)

    Google Scholar 

  14. Wang, L., Ma, J.: Efficient Training of RBF Networks via the Kurtosis and Skewness Minimization Learning Alogrithm. Journal of Theoretical and Applied Information Technology 48(1), 496–504 (2013)

    Google Scholar 

  15. Kim, T.H., Mizen, P., Thanaset, A.: Forecasting Changes in UK Interest Rates. Journal of Forecasting 27(1), 53–74 (2008)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jinwen Ma .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Zheng, W., Ma, J. (2014). Diagonal Log-Normal Generalized RBF Neural Network for Stock Price Prediction. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_64

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12436-0_64

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12435-3

  • Online ISBN: 978-3-319-12436-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics