IDEAS home Printed from https://ideas.repec.org/a/wly/jfutmk/v13y1993i6p631-643.html
   My bibliography  Save this article

Forecasting S&P and gold futures prices: An application of neural networks

Author

Listed:
  • Gary Grudnitski
  • Larry Osburn

Abstract

No abstract is available for this item.

Suggested Citation

  • Gary Grudnitski & Larry Osburn, 1993. "Forecasting S&P and gold futures prices: An application of neural networks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 13(6), pages 631-643, September.
    • Handle: RePEc:wly:jfutmk:v:13:y:1993:i:6:p:631-643
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
    2. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2020. "Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine," Resources Policy, Elsevier, vol. 69(C).
    3. Berislav Žmuk & Hrvoje Jošiæ, 2020. "Forecasting stock market indices using machine learning algorithms," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 18(4), pages 471-489.
    4. Lonnie Hamm & B. Wade Brorsen, 2000. "Trading futures markets based on signals from a neural network," Applied Economics Letters, Taylor & Francis Journals, vol. 7(2), pages 137-140.
    5. Shuofen Hsu & Chaohsin Lin & Yaling Yang, 2008. "Integrating Neural Networks for Risk‐Adjustment Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 617-642, September.
    6. Wu, Yih-Jiuan, 1998. "Exchange rate forecasting: an application of radial basis function neural networks," ISU General Staff Papers 1998010108000013540, Iowa State University, Department of Economics.
    7. Zhang, Ningning & Lin, Aijing & Shang, Pengjian, 2017. "Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 477(C), pages 161-173.
    8. Qifeng Qiao & Peter A. Beling, 2016. "Decision analytics and machine learning in economic and financial systems," Environment Systems and Decisions, Springer, vol. 36(2), pages 109-113, 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:wly:jfutmk:v:13:y:1993:i:6:p:631-643. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0270-7314/ .

    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.