IDEAS home Printed from https://ideas.repec.org/a/axf/feiaaa/v1y2022i1p1-18.html
   My bibliography  Save this article

Quantitative Analysis and Forecasting Techniques in Financial Markets: A 2022 Review

Author

Listed:
  • Huang, Zibin

Abstract

This paper provides a comprehensive review of the advancements in quantitative analysis and forecasting techniques in financial markets, focusing on key developments in 2022. It explores various statistical models, machine learning methodologies, and high-frequency trading algorithms, highlighting their applications and effectiveness in predicting market trends. Additionally, the paper discusses forecasting techniques such as time series analysis, predictive analytics, and sentiment analysis, while examining their role in shaping investment strategies and risk management. The analysis further delves into the practical applications of these techniques in areas like risk management, asset pricing, and market efficiency. Finally, the paper outlines future trends in quantitative finance, including the integration of artificial intelligence, quantum computing, and the growing emphasis on ESG factors. This review serves as a valuable resource for practitioners and researchers looking to understand the evolving landscape of quantitative finance.

Suggested Citation

  • Huang, Zibin, 2022. "Quantitative Analysis and Forecasting Techniques in Financial Markets: A 2022 Review," Financial Economics Insights, Scientific Open Access Publishing, vol. 1(1), pages 1-18.
    • Handle: RePEc:axf:feiaaa:v:1:y:2022:i:1:p:1-18
    as

    Download full text from publisher

    File URL: https://soapubs.com/index.php/FEI/article/view/58/48
    Download Restriction: no
    ---><---

    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:axf:feiaaa:v:1:y:2022:i:1:p:1-18. 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: Yuchi Liu (email available below). General contact details of provider: https://soapubs.com/index.php/FEI .

    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.