IDEAS home Printed from https://ideas.repec.org/a/eme/jespps/jes-07-2023-0379.html
   My bibliography  Save this article

US efficient factors in a Bayesian model scan framework

Author

Listed:
  • Michael O'Connell

Abstract

Purpose - The author examines the impact these efficient factors have on factor model comparison tests in US returns using the Bayesian model scan approach of Chibet al. (2020), and Chibet al.(2022). Design/methodology/approach - Ehsani and Linnainmaa (2022) show that time-series efficient investment factors in US stock returns span and earn 40% higher Sharpe ratios than the original factors. Findings - The author shows that the optimal asset pricing model is an eight-factor model which contains efficient versions of the market factor, value factor (HML) and long-horizon behavioral factor (FIN). The findings show that efficient factors enhance the performance of US factor model performance. The top performing asset pricing model does not change in recent data. Originality/value - The author is the only one to examine if the efficient factors developed by Ehsani and Linnainmaa (2022) have an impact on model comparison tests in US stock returns.

Suggested Citation

  • Michael O'Connell, 2024. "US efficient factors in a Bayesian model scan framework," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 51(5), pages 1077-1092, January.
    • Handle: RePEc:eme:jespps:jes-07-2023-0379
    DOI: 10.1108/JES-07-2023-0379
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/JES-07-2023-0379/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/JES-07-2023-0379/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/JES-07-2023-0379?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:eme:jespps:jes-07-2023-0379. 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: Emerald Support (email available below). General contact details of provider: .

    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.