IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v346y2025i1d10.1007_s10479-024-06337-2.html
   My bibliography  Save this article

Mean-variance and mean-ETL optimizations in portfolio selection: an update

Author

Listed:
  • Barret Pengyuan Shao

    (Tudor Investment Corporation)

  • John B. Guerard

    (Independent Financial Researcher)

  • Ganlin Xu

    (GuidedChoice.com, Inc.)

Abstract

In this research update, we apply the Mean-Variance (MV) and Mean-Expected Tail Loss (ETL) portfolio optimization techniques on earnings forecasting and robust regression-based composite models. A time series model with multivariate normal tempered stable (MNTS) innovations is applied to generate the out-of-sample scenarios for the portfolio optimization. We report that (1) a composite variable of analysts’ forecasts, revisions, and direction of analysts’ revisions continues to produce value in portfolio construction; (2) robust regression-based models continue to produce meaningful active returns; and (3) the Mean-Variance and Mean-ETL portfolio optimizations produce statistically significant active returns, passing the Markowitz and Xu (Journal of Portfolio Management 21:1–60, 1994) data mining corrections test.

Suggested Citation

  • Barret Pengyuan Shao & John B. Guerard & Ganlin Xu, 2025. "Mean-variance and mean-ETL optimizations in portfolio selection: an update," Annals of Operations Research, Springer, vol. 346(1), pages 657-671, March.
    • Handle: RePEc:spr:annopr:v:346:y:2025:i:1:d:10.1007_s10479-024-06337-2
    DOI: 10.1007/s10479-024-06337-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-024-06337-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-024-06337-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:spr:annopr:v:346:y:2025:i:1:d:10.1007_s10479-024-06337-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.