IDEAS home Printed from https://ideas.repec.org/h/wsi/wschap/9789811270277_0062.html
   My bibliography  Save this book chapter

Research on the Price of Stock on AR, MA, ARMA, and ARMA-GARCH Models

In: Economic Management and Big Data Application Proceedings of the 3rd International Conference

Author

Listed:
  • Tianlei Zhu
  • Yingke Yang
  • Zheng Tao

Abstract

This paper aims at issues of Goldman Sachs’ stock forecasting in a short time by using the time series analysis based on four models: AR, MA, ARMA, and ARMA-GARCH models and chooses the optimal model. In this paper, after selecting the sample data and preprocessing data, the regression evaluation index is used to analyze the preliminary models. After that, use the Sequence Stationarity Test and ADF Test to test the series’ stationarity, and analyze the solution of the ARMA model to conclude the formula. The regression evaluation parameters are then compared to the initial models. Later, by selecting from Gaussian distribution, student t distribution, and biased student t distribution, the solution of the AGMA-GARCH model is analyzed. By constructing ARMA and GARCH models, the short-term forecast stock price results are valid and feasible. It concludes that the Arma-GARCH model greatly improves the accuracy of stock forecasting.

Suggested Citation

  • Tianlei Zhu & Yingke Yang & Zheng Tao, 2024. "Research on the Price of Stock on AR, MA, ARMA, and ARMA-GARCH Models," World Scientific Book Chapters, in: Sikandar Ali Qalati (ed.), Economic Management and Big Data Application Proceedings of the 3rd International Conference, chapter 62, pages 710-718, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789811270277_0062
    as

    Download full text from publisher

    File URL: https://www.worldscientific.com/doi/pdf/10.1142/9789811270277_0062
    Download Restriction: Ebook Access is available upon purchase.
    File URL: https://www.worldscientific.com/doi/abs/10.1142/9789811270277_0062
    Download Restriction: Ebook Access is available upon purchase.
    ---><---

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

    More about this item

    Keywords

    Big Data; Information Management; Economic; Data Applications; Blockchain; E-commerce;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology

    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:wsi:wschap:9789811270277_0062. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscientific.com/page/worldscibooks .

    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.