IDEAS home Printed from https://ideas.repec.org/a/bla/jfinan/v71y2016i5p2095-2144.html

Speculative Betas

Author

Listed:
  • HARRISON HONG
  • DAVID A. SRAER

Abstract

We provide a model for why high beta assets are more prone to speculative overpricing than low beta ones. When investors disagree about the common factor of cash-flows, high beta assets are more sensitive to this macro-disagreement and experience a greater divergence-of-opinion about their payoffs. Short-sales constraints for some investors such as retail mutual funds result in high beta assets being over-priced. When aggregate disagreement is low, expected return increases with beta due to risk-sharing. But when it is large, expected return initially increases but then decreases with beta. High beta assets have greater shorting from unconstrained arbitrageurs and more share turnover. Using measures of disagreement about stock earnings and economic uncertainty, we verify these predictions. A calibration exercise yields reasonable parameter values.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Harrison Hong & David A. Sraer, 2016. "Speculative Betas," Journal of Finance, American Finance Association, vol. 71(5), pages 2095-2144, October.
    • Handle: RePEc:bla:jfinan:v:71:y:2016:i:5:p:2095-2144
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/jofi.2016.71.issue-5
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Shiyang Huang & Xin Liu & Dong Lou & Christopher Polk, 2024. "The Booms and Busts of Beta Arbitrage," Management Science, INFORMS, vol. 70(8), pages 5367-5385, August.
    2. Wei, Zhihua & Wu, Deqian & Zeng, Aimin & Li, Bo, 2024. "Spillover effects of MSCI inclusion announcement: Evidence and implications from China," Pacific-Basin Finance Journal, Elsevier, vol. 88(C).
    3. Ma, Tian & Sheng, Haoyun & Wang, Yuejie, 2024. "Noisy market, machine learning and fundamental momentum," Pacific-Basin Finance Journal, Elsevier, vol. 86(C).
    4. Savor, Pavel & Wilson, Mungo, 2014. "Asset pricing: A tale of two days," Journal of Financial Economics, Elsevier, vol. 113(2), pages 171-201.
    5. Brandao-Marques, Luis & Gelos, Gaston & Melgar, Natalia, 2018. "Country transparency and the global transmission of financial shocks," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 56-72.
    6. Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Market Timing with Moving Averages," Sustainability, MDPI, vol. 10(7), pages 1-25, June.
    7. Shen, Junyan & Yu, Jianfeng & Zhao, Shen, 2017. "Investor sentiment and economic forces," Journal of Monetary Economics, Elsevier, vol. 86(C), pages 1-21.
    8. Blitz, David & Pang, Juan & van Vliet, Pim, 2013. "The volatility effect in emerging markets," Emerging Markets Review, Elsevier, vol. 16(C), pages 31-45.
    9. Maik Dierkes & Sebastian Schroen, 2023. "Betting against sentiment? Seemingly unrelated anomalies and the low‐risk effect," Review of Financial Economics, John Wiley & Sons, vol. 41(2), pages 152-176, April.
    10. Fischer, Thomas & Krauss, Christopher, 2017. "Deep learning with long short-term memory networks for financial market predictions," FAU Discussion Papers in Economics 11/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    11. Kim, Jun Sik & Ryu, Doojin & Seo, Sung Won, 2014. "Investor sentiment and return predictability of disagreement," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 166-178.
    12. Paul Schneider & Christian Wagner & Josef Zechner, 2020. "Low‐Risk Anomalies?," Journal of Finance, American Finance Association, vol. 75(5), pages 2673-2718, October.
    13. Kim, Woo Chang & Kim, Jang Ho & Mulvey, John M. & Fabozzi, Frank J., 2015. "Focusing on the worst state for robust investing," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 19-31.

    More about this item

    JEL classification:

    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:bla:jfinan:v:71:y:2016:i:5:p:2095-2144. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/afaaaea.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.