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Speculative Betas

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  • 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.
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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
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    File URL: http://hdl.handle.net/10.1111/jofi.2016.71.issue-5
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Huang, Shiyang & Lou, Dong & Polk, Christopher, 2016. "The Booms and Busts of Beta Arbitrage," CEPR Discussion Papers 11531, C.E.P.R. Discussion Papers.
    2. repec:eee:jbfina:v:96:y:2018:i:c:p:56-72 is not listed on IDEAS
    3. Savor, Pavel & Wilson, Mungo, 2014. "Asset pricing: A tale of two days," Journal of Financial Economics, Elsevier, vol. 113(2), pages 171-201.
    4. 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.
    5. Shen, Junyan & Yu, Jianfeng & Zhao, Shen, 2017. "Investor sentiment and economic forces," Journal of Monetary Economics, Elsevier, vol. 86(C), pages 1-21.
    6. 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.
    7. Blitz, David & Pang, Juan & van Vliet, Pim, 2013. "The volatility effect in emerging markets," Emerging Markets Review, Elsevier, vol. 16(C), pages 31-45.
    8. 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.
    9. 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

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