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Parametric Portfolio Policies: Exploiting Characteristics in the Cross-Section of Equity Returns

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  • Michael W. Brandt
  • Pedro Santa-Clara
  • Rossen Valkanov

Abstract

We propose a novel approach to optimizing portfolios with large numbers of assets. We model directly the portfolio weight in each asset as a function of the asset's characteristics. The coefficients of this function are found by optimizing the investor's average utility of the portfolio's return over the sample period. Our approach is computationally simple and easily modified and extended to capture the effect of transaction costs, for example, produces sensible portfolio weights, and offers robust performance in and out of sample. In contrast, the traditional approach of first modeling the joint distribution of returns and then solving for the corresponding optimal portfolio weights is not only difficult to implement for a large number of assets but also yields notoriously noisy and unstable results. We present an empirical implementation for the universe of all stocks in the CRSP--Compustat data set, exploiting the size, value, and momentum anomalies. The Author 2009. 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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Bibliographic Info

Article provided by Society for Financial Studies in its journal The Review of Financial Studies.

Volume (Year): 22 (2009)
Issue (Month): 9 (September)
Pages: 3411-3447

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Handle: RePEc:oup:rfinst:v:22:y:2009:i:9:p:3411-3447

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Citations

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Cited by:
  1. Vilkovz, Grigory & Xiaox, Yan, 2013. "Option-implied information and predictability of extreme returns," SAFE Working Paper Series 5, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
  2. Miguel Anton & Christopher Polk, 2010. "Connected stocks," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library 43098, London School of Economics and Political Science, LSE Library.
  3. Füss, Roland & Miebs, Felix & Trübenbach, Fabian, 2014. "A jackknife-type estimator for portfolio revision," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 14-28.
  4. Erik Hjalmarsson & Peter Manchev, 2009. "Characteristic-based mean-variance portfolio choice," International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.) 981, Board of Governors of the Federal Reserve System (U.S.).
  5. Behr, Patrick & Guettler, Andre & Truebenbach, Fabian, 2012. "Using industry momentum to improve portfolio performance," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1414-1423.
  6. Mohammed Bouaddi & Abderrahim Taamouti, 2012. "Portfolio risk management in a data-rich environment," Financial Markets and Portfolio Management, Springer, Springer, vol. 26(4), pages 469-494, December.
  7. Tu, Jun & Zhou, Guofu, 2011. "Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies," Journal of Financial Economics, Elsevier, Elsevier, vol. 99(1), pages 204-215, January.
  8. Maio, Paulo, 2013. "Return decomposition and the Intertemporal CAPM," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4958-4972.
  9. Olivier Ledoit & Michael Wolf, 2014. "Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks," ECON - Working Papers, Department of Economics - University of Zurich 137, Department of Economics - University of Zurich.
  10. De Santis, Roberto A. & Lührmann, Melanie, 2009. "On the determinants of net international portfolio flows: A global perspective," Journal of International Money and Finance, Elsevier, Elsevier, vol. 28(5), pages 880-901, September.
  11. Juarez-Torres, Miriam & Sanchez, Leonardo & Vedenov, Dmitry V., 2012. "Effectiveness of Weather Derivatives as Cross-Hedging Instrument against Climate Change: The Cases of Reservoir Water Allocation Management in Guanajuato, Mexico," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington, Agricultural and Applied Economics Association 124813, Agricultural and Applied Economics Association.

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