IDEAS home Printed from https://ideas.repec.org/a/igg/jban00/v3y2016i1p1-21.html
   My bibliography  Save this article

A Prescriptive Stock Market Investment Strategy for the Restaurant Industry using an Artificial Neural Network Methodology

Author

Listed:
  • Gary R. Weckman

    (Department of Industrial and Systems Engineering, Ohio University, Athens, OH, USA)

  • Ronald W. Dravenstott

    (Department of Industrial and Systems Engineering, Ohio University, Athens, OH, USA)

  • William A. Young II

    (Department of Management, Ohio University, Athens, OH, USA)

  • Ehsan Ardjmand

    (Department of Industrial and Systems Engineering, Ohio University, Athens, OH, USA)

  • David F. Millie

    (Palm Island Enviro-Informatics LLC, Sarasota, FL, USA)

  • Andy P. Snow

    (The J. W. McClure School of Information and Telecommunication Systems, Ohio University, Athens, OH, USA)

Abstract

Stock price forecasting is a classic problem facing analysts. Forecasting models have been developed for predicting individual stocks and stock indices around the world and in numerous industries. According to a literature review, these models have yet to be applied to the restaurant industry. Strategies for forecasting typically include fundamental and technical variables. In this research, fundamental and technical inputs were combined into an artificial neural network (ANN) stock prediction model for the restaurant industry. Models were designed to forecast 1 week, 4 weeks, and 13 weeks into the future. The model performed better than the benchmark methods, which included, an analyst prediction, multiple linear regression, trading, and Buy and Hold trading strategies. The prediction accuracy of the ANN methodology presented reached accuracy performance measures as high as 60%. The model also shown resiliency over the housing crisis in 2008.

Suggested Citation

  • Gary R. Weckman & Ronald W. Dravenstott & William A. Young II & Ehsan Ardjmand & David F. Millie & Andy P. Snow, 2016. "A Prescriptive Stock Market Investment Strategy for the Restaurant Industry using an Artificial Neural Network Methodology," International Journal of Business Analytics (IJBAN), IGI Global, vol. 3(1), pages 1-21, January.
    • Handle: RePEc:igg:jban00:v:3:y:2016:i:1:p:1-21
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBAN.2016010101
    Download Restriction: no
    ---><---

    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:igg:jban00:v:3:y:2016:i:1:p:1-21. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.