IDEAS home Printed from https://ideas.repec.org/a/ids/ijecbr/v25y2023i1p64-100.html
   My bibliography  Save this article

Technical analysis forecasting and evaluation of stock markets: the probabilistic recovery neural network approach

Author

Listed:
  • Andreas Maniatopoulos
  • Alexandros Gazis
  • Nikolaos Mitianoudis

Abstract

The market efficiency theory suggests that stock market pricing reflects all publicly available information regarding a given stock. To outperform the market, a shareholder must study the market's price volume patterns and predict human behaviour and tendencies. Except for conventional approaches based on fundamental or technical analysis, new tools are currently proposed using big data and artificial intelligence. This publication analyses and evaluates four commonly used deep-learning artificial neural network models. Then, it proposes a new method by adopting the 'probabilistic recovery' algorithmic approach. The dataset used consists of 501 unique company names based on real data derived from US Dow Jones. This method closely follows the market's behaviour, providing daily upwards-downwards data trends. The proposed system can be used as a tool for technical analysis regarding the prediction accuracy of trading strategies, providing approximately 60% future movements' accuracy, over 90% relative price prediction and annual investment return slightly over 60%.

Suggested Citation

  • Andreas Maniatopoulos & Alexandros Gazis & Nikolaos Mitianoudis, 2023. "Technical analysis forecasting and evaluation of stock markets: the probabilistic recovery neural network approach," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 25(1), pages 64-100.
    • Handle: RePEc:ids:ijecbr:v:25:y:2023:i:1:p:64-100
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=127271
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Syed Hasan Jafar & Shakeb Akhtar & Hani El-Chaarani & Parvez Alam Khan & Ruaa Binsaddig, 2023. "Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model," JRFM, MDPI, vol. 16(10), pages 1-23, September.

    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:ids:ijecbr:v:25:y:2023:i:1:p:64-100. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=310 .

    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.