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The Cross-Section of Risk and Return

Author

Listed:
  • Kent Daniel
  • Lira Mota
  • Simon Rottke
  • Tano Santos

Abstract

In the finance literature, a common practice is to create characteristic portfolios by sorting on characteristics associated with average returns. We show that the resulting portfolios are likely to capture not only the priced risk associated with the characteristic, but also unpriced risk. We develop a procedure to remove this unpriced risk using covariance information estimated from past returns. We apply our methodology to the five Fama and French (2015) characteristic portfolios. The squared Sharpe ratio of the optimal combination of the resulting characteristic efficient portfolios is 2.16, compared with 1.16 for the original characteristic portfolios.

Suggested Citation

  • Kent Daniel & Lira Mota & Simon Rottke & Tano Santos, 2017. "The Cross-Section of Risk and Return," NBER Working Papers 24164, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24164
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    References listed on IDEAS

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    Cited by:

    1. Cujean, Julien & Andrei, Daniel & Fournier, Mathieu, 2019. "The Low-Minus-High Portfolio and the Factor Zoo," CEPR Discussion Papers 14153, C.E.P.R. Discussion Papers.
    2. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
    3. Armin Varmaz & Christian Fieberg & Thorsten Poddig, 2024. "Portfolio optimization for sustainable investments," Annals of Operations Research, Springer, vol. 341(2), pages 1151-1176, October.
    4. Tengfei Zhang, 2020. "Manager Uncertainty and Cross-Sectional Stock Returns," 2020 Papers pzh934, Job Market Papers.
    5. Mikhail Chernov & Lars A Lochstoer & Stig R H Lundeby, 2022. "Conditional Dynamics and the Multihorizon Risk-Return Trade-Off," The Review of Financial Studies, Society for Financial Studies, vol. 35(3), pages 1310-1347.
    6. I-Cheng Yeh & Yi-Cheng Liu, 2020. "Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-28, December.
    7. G Andrew Karolyi & Stijn Van Nieuwerburgh, 2020. "New Methods for the Cross-Section of Returns," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1879-1890.
    8. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2019. "Common Risk Factors in Cryptocurrency," NBER Working Papers 25882, National Bureau of Economic Research, Inc.
    9. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    10. Baba-Yara, Fahiz & Boons, Martijn & Tamoni, Andrea, 2024. "Persistent and transitory components of firm characteristics: Implications for asset pricing," Journal of Financial Economics, Elsevier, vol. 154(C).
    11. Favero, Carlo A. & Melone, Alessandro, 2020. "Asset Pricing vs Asset Expected Returning in Factor-Portfolio Models," CEPR Discussion Papers 14417, C.E.P.R. Discussion Papers.
    12. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
    13. Lambert, Marie & Fays, Boris & Hübner, Georges, 2020. "Factoring characteristics into returns: A clinical study on the SMB and HML portfolio construction methods," Journal of Banking & Finance, Elsevier, vol. 114(C).

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    More about this item

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G1 - Financial Economics - - General Financial Markets
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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