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Construction, Management, and Performance of Sparse Markowitz Portfolios

26 Pages Posted: 14 Oct 2012 Last revised: 18 Sep 2014

Julie Henriques

Tea-Cegos Deployment

Juan-Pablo Ortega

Universität Sankt Gallen; Centre National de la Recherche Scientifique (CNRS)

Date Written: October 14, 2012

Abstract

We study different implementations of the sparse portfolio construction and rebalancing method introduced by Brodie et al. This technique is based on the use of a l1-norm (sum of the absolute values) type penalization on the portfolio weights vector that regularizes the Markowitz portfolio selection problem by automatically eliminating the dynamical redundancies present in the time evolution of asset prices. We make specific recommendations as to the different estimation techniques for the parameters needed in the use of the method and we prove its good performance in realistic situations involving different rebalancing frequencies and transaction costs. Our empirical findings show that the beneficial effects of the use of sparsity constraints are robust with respect to the choice of trend and covariance estimation methods used in its implementation.

Keywords: Markowitz portfolios, penalized regression, portfolio selection, portfolio management, sparsity, Sharpe ratio

JEL Classification: C5

Suggested Citation

Henriques, Julie and Ortega, Juan-Pablo, Construction, Management, and Performance of Sparse Markowitz Portfolios (October 14, 2012). Studies in Nonlinear Dynamics and Econometrics, 18(4), 383-402, 2014. Available at SSRN: https://ssrn.com/abstract=2161564 or http://dx.doi.org/10.2139/ssrn.2161564

Julie Henriques

Tea-Cegos Deployment ( email )

11 rue Denis Papin
Besancon, F-25000
France

Juan-Pablo Ortega (Contact Author)

Universität Sankt Gallen ( email )

Bodanstrasse 6
St. Gallen, St. Gallen CH-9000
Switzerland

HOME PAGE: http://juan-pablo-ortega.com

Centre National de la Recherche Scientifique (CNRS) ( email )

16 route de Gray
Besançon, 25030
France

HOME PAGE: http://juan-pablo-ortega.com

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