IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v34y2021i1p108-148..html
   My bibliography  Save this article

What Do Fund Flows Reveal about Asset Pricing Models and Investor Sophistication?
[Alpha or beta in the eye of the beholder: What drives hedge fund flows?]

Author

Listed:
  • Narasimhan Jegadeesh
  • Chandra Sekhar Mangipudi

Abstract

Recent evidence indicates that market model alphas are stronger predictors of mutual fund flows than alphas with other models. Some recent papers have interpreted this evidence to mean that CAPM is the best asset pricing model, but some others have interpreted it as evidence against investor sophistication. We evaluate the merits of these mutually exclusive interpretations. We show that no tenable inference about the validity of any asset pricing model can be drawn from this evidence. Rejecting the investor sophistication hypothesis is tenable, but the appropriate benchmark to judge sophistication is different from that used in this literature.

Suggested Citation

  • Narasimhan Jegadeesh & Chandra Sekhar Mangipudi, 2021. "What Do Fund Flows Reveal about Asset Pricing Models and Investor Sophistication? [Alpha or beta in the eye of the beholder: What drives hedge fund flows?]," The Review of Financial Studies, Society for Financial Studies, vol. 34(1), pages 108-148.
    • Handle: RePEc:oup:rfinst:v:34:y:2021:i:1:p:108-148.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhaa045
    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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.

    More about this item

    JEL classification:

    • G4 - Financial Economics - - Behavioral Finance
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

    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:1:p:108-148.. 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.