IDEAS home Printed from https://ideas.repec.org/p/sce/scecf0/61.html
   My bibliography  Save this paper

Financial Time Series Forecasting By Neural Network Using Conjugate Gradient Learning Algorithm And Multiple Linear Regression Weight Initialization

Author

Listed:
  • Chi-Cheong Chris Wong

    (The Hong Kong Polytechnic University)

  • Man-Chung Chan

    (The Hong Kong Polytechnic University)

  • Chi-Chung Lam

    (The Hong Kong Polytechnic University)

Abstract

Multilayer neural network has been successfully applied to the time series forecasting. Backpropagation, a popular learning algorithm, converges slowly and has the difficulty in determining the network parameters. In this paper, conjugate gradient learning algorithm with restart procedure is introduced to overcome these problems. Also, the commonly used random weight initialization does not guarantee to generate a set of initial connection weights close to the optimal weights leading to slow convergence. Multiple linear regression (MLR) provides an alternative for weight initialization. The daily trade data of the listed companies from Shanghai Stock Exchange is collected for technical analysis with the means of neural networks. Two learning algorithms and two weight initializations are compared. The results find that neural networks can model the time series satisfactorily. The proposed conjugate gradient with MLR weight initialization requires a lower computation cost and learns better than backpropagation with random initialization.

Suggested Citation

  • Chi-Cheong Chris Wong & Man-Chung Chan & Chi-Chung Lam, 2000. "Financial Time Series Forecasting By Neural Network Using Conjugate Gradient Learning Algorithm And Multiple Linear Regression Weight Initialization," Computing in Economics and Finance 2000 61, Society for Computational Economics.
    • Handle: RePEc:sce:scecf0:61
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/cef00/papers/paper61.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David F. Shanno, 1978. "Conjugate Gradient Methods with Inexact Searches," Mathematics of Operations Research, INFORMS, vol. 3(3), pages 244-256, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fathi Abid & Bilel Kaffel, 2018. "The extent of virgin olive-oil prices’ distribution revealing the behavior of market speculators," Review of Quantitative Finance and Accounting, Springer, vol. 50(2), pages 561-590, February.
    2. Mohammad Arashi & Mohammad Mahdi Rounaghi, 2022. "Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model," Future Business Journal, Springer, vol. 8(1), pages 1-12, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yasushi Narushima & Shummin Nakayama & Masashi Takemura & Hiroshi Yabe, 2023. "Memoryless Quasi-Newton Methods Based on the Spectral-Scaling Broyden Family for Riemannian Optimization," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 639-664, May.
    2. Churlzu Lim & Hanif Sherali & Stan Uryasev, 2010. "Portfolio optimization by minimizing conditional value-at-risk via nondifferentiable optimization," Computational Optimization and Applications, Springer, vol. 46(3), pages 391-415, July.
    3. Fischer, Manfred M. & Staufer, Petra, 1998. "Optimization in an Error Backpropagation Neural Network Environment with a Performance Test on a Pattern Classification Problem," MPRA Paper 77810, University Library of Munich, Germany.
    4. B. Sellami & Y. Chaib, 2016. "A new family of globally convergent conjugate gradient methods," Annals of Operations Research, Springer, vol. 241(1), pages 497-513, June.
    5. Neculai Andrei, 2013. "Another Conjugate Gradient Algorithm with Guaranteed Descent and Conjugacy Conditions for Large-scale Unconstrained Optimization," Journal of Optimization Theory and Applications, Springer, vol. 159(1), pages 159-182, October.
    6. Ke-Lin Du & Chi-Sing Leung & Wai Ho Mow & M. N. S. Swamy, 2022. "Perceptron: Learning, Generalization, Model Selection, Fault Tolerance, and Role in the Deep Learning Era," Mathematics, MDPI, vol. 10(24), pages 1-46, December.
    7. Shummin Nakayama & Yasushi Narushima & Hiroshi Yabe, 2021. "Inexact proximal memoryless quasi-Newton methods based on the Broyden family for minimizing composite functions," Computational Optimization and Applications, Springer, vol. 79(1), pages 127-154, May.
    8. N. Mahdavi-Amiri & M. Shaeiri, 2020. "A conjugate gradient sampling method for nonsmooth optimization," 4OR, Springer, vol. 18(1), pages 73-90, March.
    9. Abubakar, Auwal Bala & Kumam, Poom & Malik, Maulana & Ibrahim, Abdulkarim Hassan, 2022. "A hybrid conjugate gradient based approach for solving unconstrained optimization and motion control problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 640-657.
    10. Andrei, Neculai, 2010. "Accelerated scaled memoryless BFGS preconditioned conjugate gradient algorithm for unconstrained optimization," European Journal of Operational Research, Elsevier, vol. 204(3), pages 410-420, August.
    11. Hanif D. Sherali & Churlzu Lim, 2007. "Enhancing Lagrangian Dual Optimization for Linear Programs by Obviating Nondifferentiability," INFORMS Journal on Computing, INFORMS, vol. 19(1), pages 3-13, February.
    12. David Ek & Anders Forsgren, 2021. "Exact linesearch limited-memory quasi-Newton methods for minimizing a quadratic function," Computational Optimization and Applications, Springer, vol. 79(3), pages 789-816, July.
    13. T. L. Jensen & M. Diehl, 2017. "An Approach for Analyzing the Global Rate of Convergence of Quasi-Newton and Truncated-Newton Methods," Journal of Optimization Theory and Applications, Springer, vol. 172(1), pages 206-221, January.
    14. Dongyi Liu & Genqi Xu, 2013. "Symmetric Perry conjugate gradient method," Computational Optimization and Applications, Springer, vol. 56(2), pages 317-341, October.
    15. Hiroyuki Sakai & Hideaki Iiduka, 2024. "Modified Memoryless Spectral-Scaling Broyden Family on Riemannian Manifolds," Journal of Optimization Theory and Applications, Springer, vol. 202(2), pages 834-853, August.
    16. Saman Babaie-Kafaki, 2012. "A note on the global convergence theorem of the scaled conjugate gradient algorithms proposed by Andrei," Computational Optimization and Applications, Springer, vol. 52(2), pages 409-414, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sce:scecf0:61. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.