IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v34y2021i2p1090-1103..html
   My bibliography  Save this article

Corrigendum: Bond Risk Premiums with Machine Learning
[Bond risk premiums with machine learning]

Author

Listed:
  • Daniele Bianchi
  • Matthias Büchner
  • Tobias Hoogteijling
  • Andrea Tamoni

Abstract

In this note we revisit the empirical results in Bianchi, Büchner, and Tamoni (2020) after correcting for using information not available at the time the forecast was made. Although we note a decrease in out-of-sample , the revised analysis confirms that bond excess return predictability from neural networks remains statistically and economically significant.

Suggested Citation

  • Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021. "Corrigendum: Bond Risk Premiums with Machine Learning [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1090-1103.
    • Handle: RePEc:oup:rfinst:v:34:y:2021:i:2:p:1090-1103.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhaa098
    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.

    More about this item

    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:oup:rfinst:v:34:y:2021:i:2:p:1090-1103.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sfsssea.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.