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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, easily modified and extended, 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. Our approach also provides a new test of the portfolio choice implications of equilibrium asset pricing models. We present an empirical implementation for the universe of all stocks in the CRSP-Compustat dataset, exploiting the size, value, and momentum anomalies.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 10996.

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Date of creation: Dec 2004
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Publication status: published as Michael W. Brandt & Pedro Santa-Clara & Rossen Valkanov, 2009. "Parametric Portfolio Policies: Exploiting Characteristics in the Cross-Section of Equity Returns," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 22(9), pages 3411-3447, September.
Handle: RePEc:nbr:nberwo:10996

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Citations

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Cited by:
  1. 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, vol. 28(5), pages 880-901, September.
  2. 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.
  3. Hjalmarsson, Erik & Manchev, Petar, 2012. "Characteristic-based mean-variance portfolio choice," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1392-1401.
  4. Mohammed Bouaddi & Abderrahim Taamouti, 2012. "Portfolio risk management in a data-rich environment," Financial Markets and Portfolio Management, Springer, vol. 26(4), pages 469-494, December.
  5. 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 124813, Agricultural and Applied Economics Association.
  6. Maio, Paulo, 2013. "Return decomposition and the Intertemporal CAPM," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4958-4972.
  7. Tu, Jun & Zhou, Guofu, 2011. "Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies," Journal of Financial Economics, Elsevier, vol. 99(1), pages 204-215, January.
  8. 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.
  9. Miguel Anton & Christopher Polk, 2010. "Connected stocks," LSE Research Online Documents on Economics 43098, London School of Economics and Political Science, LSE Library.
  10. Olivier Ledoit & Michael Wolf, 2014. "Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks," ECON - Working Papers 137, Department of Economics - University of Zurich.
  11. 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.

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