IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v73y2021icp249-256.html
   My bibliography  Save this article

Forecasting stock prices

Author

Listed:
  • Harel, Arie
  • Harpaz, Giora

Abstract

We apply concepts form machine learning to forecast stock prices. First, we introduce the general (3 by 3) forecasting model, in which the financial markets are populated by three types of stocks: Overpriced stocks, underpriced stocks and fairly priced stocks. The objective of the financial analyst or a potential investor is to identify which stock belongs to which classification, and to take the relevant investment decision. Second, we present two different numerical examples to illustrate our forecasting models, and estimate all the relevant statistics, as well as the forecasting accuracy. Third, we introduce the Receiver Operator Curve (ROC), and demonstrate the trade-off between the sensitivity and specificity of the prediction. We also discuss the performance evaluation of forecasting stock prices.

Suggested Citation

  • Harel, Arie & Harpaz, Giora, 2021. "Forecasting stock prices," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 249-256.
    • Handle: RePEc:eee:reveco:v:73:y:2021:i:c:p:249-256
    DOI: 10.1016/j.iref.2020.12.033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056020303191
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2020.12.033?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Aslam, Faheem & Hunjra, Ahmed Imran & Ftiti, Zied & Louhichi, Wael & Shams, Tahira, 2022. "Insurance fraud detection: Evidence from artificial intelligence and machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).

    More about this item

    Keywords

    Forecasting; Stock prices; Accuracy; Machine learning; Receiver operator curve;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    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:eee:reveco:v:73:y:2021:i:c:p:249-256. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620165 .

    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.