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The Market Portfolio May Be Mean/Variance Efficient After All

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Listed author(s):
  • Moshe Levy
  • Richard Roll

Abstract

Numerous studies have examined the mean/variance efficiency of various market proxies by employing sample parameters and have concluded that these proxies are inefficient. These findings cast doubt about the capital asset pricing model (CAPM), one of the cornerstones of modern finance. This study adopts a reverse-engineering approach: given a particular market proxy, we find the minimal variations in sample parameters required to ensure that the proxy is mean/variance efficient. Surprisingly, slight variations in parameters, well within estimation error bounds, suffice to make the proxy efficient. Thus, many conventional market proxies could be perfectly consistent with the CAPM and useful for estimating expected returns. The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.

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File URL: http://hdl.handle.net/10.1093/rfs/hhp119
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Article provided by Society for Financial Studies in its journal The Review of Financial Studies.

Volume (Year): 23 (2010)
Issue (Month): 6 (June)
Pages: 2464-2491

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Handle: RePEc:oup:rfinst:v:23:y:2010:i:6:p:2464-2491
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Cited by:

  1. Da, Zhi & Guo, Re-Jin & Jagannathan, Ravi, 2012. "CAPM for estimating the cost of equity capital: Interpreting the empirical evidence," Journal of Financial Economics, Elsevier, vol. 103(1), pages 204-220.
  2. Moshe Levy, 2012. "On the Spurious Correlation Between Sample Betas and Mean Returns," Applied Mathematical Finance, Taylor & Francis Journals, vol. 19(4), pages 341-360, September.
  3. Marie Brière & Bastien Drut & Valérie Mignon & Kim Oosterlinck & Ariane Szafarz, 2013. "Is the Market Portfolio Efficient? A New Test of Mean-Variance Efficiency when all Assets are Risky," Finance, Presses universitaires de Grenoble, vol. 34(1), pages 7-41.
  4. Clark, Ephraim & Kassimatis, Konstantinos, 2014. "Exploiting stochastic dominance to generate abnormal stock returns," Journal of Financial Markets, Elsevier, vol. 20(C), pages 20-38.
  5. Tobias Schlueter & Soenke Sievers, 2014. "Determinants of market beta: the impacts of firm-specific accounting figures and market conditions," Review of Quantitative Finance and Accounting, Springer, vol. 42(3), pages 535-570, April.
  6. Levy, Moshe & Levy, Haim, 2015. "Keeping up with the Joneses and optimal diversification," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 29-38.
  7. Hanene Ben Salah & Ali Gannoun & Christian De Peretti & Mathieu Ribatet & Abdelwahed Trabelsi, 2016. "Towards dynamical Portfolio allocation Selections," Working Papers hal-01299566, HAL.
  8. repec:dau:papers:123456789/9297 is not listed on IDEAS
  9. David Ardia & Kris Boudt, 2013. "Implied Expected Returns and the Choice of a Mean-Variance Efficient Portfolio Proxy," Cahiers de recherche 1328, CIRPEE.
  10. Levy, Haim & Levy, Moshe, 2014. "The benefits of differential variance-based constraints in portfolio optimization," European Journal of Operational Research, Elsevier, vol. 234(2), pages 372-381.
  11. : Constantinos Antonio & : John A. Doukas & : Avanidhar Subrahmanyam, 2013. "Investor Sentiment and Beta Pricing," Working Papers wpn13-05, Warwick Business School, Finance Group.
  12. Levy, Haim & Simaan, Yusif, 2016. "More possessions, more worry," European Journal of Operational Research, Elsevier, vol. 255(3), pages 893-902.
  13. Fang, Yi, 2012. "Aggregate investor preferences and beliefs in stock market: A stochastic dominance analysis," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 528-547.
  14. Clark, Ephraim & Kassimatis, Konstantinos, 2012. "An empirical analysis of marginal conditional stochastic dominance," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1144-1151.

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