IDEAS home Printed from https://ideas.repec.org/a/aes/infoec/v27y2023i3p61-72.html
   My bibliography  Save this article

Exploring the Effectiveness of ARIMA and GARCH Models in Stock Price Forecasting: An Application in the IT Industry

Author

Listed:
  • Lavinia Roxana TOMA

Abstract

his study aims to develop a predictive model for stock prices using time-series analysis. The primary objective is to identify volatility patterns through the implementation of the GARCH model and forecast future stock prices for Microsoft company utilizing the ARIMA model based on historical data. The findings of this study contribute to the literature on stock price forecasting and provide insights for investors in making informed investment decisions. Moreover, the effectiveness of the proposed methodology is assessed through a comprehensive set of tests, indicating highly positive results when compared to other similar approaches.

Suggested Citation

  • Lavinia Roxana TOMA, 2023. "Exploring the Effectiveness of ARIMA and GARCH Models in Stock Price Forecasting: An Application in the IT Industry," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 27(3), pages 61-72.
    • Handle: RePEc:aes:infoec:v:27:y:2023:i:3:p:61-72
    as

    Download full text from publisher

    File URL: https://revistaie.ase.ro/content/107/05%20-%20toma.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Estimating yield spreads volatility using GARCH-type models," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Budi Setiawan & Marwa Ben Abdallah & Maria Fekete-Farkas & Robert Jeyakumar Nathan & Zoltan Zeman, 2021. "GARCH (1,1) Models and Analysis of Stock Market Turmoil during COVID-19 Outbreak in an Emerging and Developed Economy," JRFM, MDPI, vol. 14(12), pages 1-19, December.
    2. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    3. Swarnil Roy & Sk. Riad Arefin & Avijit Mallik, 2023. "How Volatility and Herding of the Stock Markets in the Oceania Region Influence Investors and Policymakers: A Sector-Wise Exploration in Pre and Post-COVID Period," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 15(1), pages 1-24, January.
    4. Mhd Ruslan, Siti Marsila & Mokhtar, Kasypi, 2021. "Stock market volatility on shipping stock prices: GARCH models approach," The Journal of Economic Asymmetries, Elsevier, vol. 24(C).
    5. Orlando, Giuseppe & Bufalo, Michele, 2022. "Modelling bursts and chaos regularization in credit risk with a deterministic nonlinear model," Finance Research Letters, Elsevier, vol. 47(PA).

    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:aes:infoec:v:27:y:2023:i:3:p:61-72. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Paul Pocatilu (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    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.