IDEAS home Printed from https://ideas.repec.org/a/ids/ijepee/v20y2024i3-4p237-245.html
   My bibliography  Save this article

Predicting next trading day closing price of ASEAN+6 stock indices using artificial neural networks: evidence from Lunar New Year effect

Author

Listed:
  • Surachai Chancharat
  • Sutida Tukaew

Abstract

Many stakeholders place a premium on accurate stock market price forecasting. Artificial neural networks (ANNs) have demonstrated high accuracy in predicting stock price returns, future stock prices, and stock market direction. The main objective of this study is to predict the effect of the Lunar New Year on ASEAN+6 stock markets using historical data from September 8, 1999, to December 31, 2021. The experimental results show that ANNs are an effective modelling tool for accurately predicting the ASEAN+6 stock prices. This is the first attempt, to our knowledge, to utilise ANNs to predict the ASEAN+6 stock markets, and the results are comparable to, if not better than, many stock market predictions recorded in the literature. The ANN model also demonstrated the most important technical indicators in predicting the ASEAN+6 stock markets. The investigation results also showed that ANNs are resistant to stock market volatility.

Suggested Citation

  • Surachai Chancharat & Sutida Tukaew, 2024. "Predicting next trading day closing price of ASEAN+6 stock indices using artificial neural networks: evidence from Lunar New Year effect," International Journal of Economic Policy in Emerging Economies, Inderscience Enterprises Ltd, vol. 20(3/4), pages 237-245.
    • Handle: RePEc:ids:ijepee:v:20:y:2024:i:3/4:p:237-245
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=142493
    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.

    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:ijepee:v:20:y:2024:i:3/4:p:237-245. 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=219 .

    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.