Regularization of Portfolio Allocation

35 Pages Posted: 21 Apr 2016

See all articles by Benjamin Bruder

Benjamin Bruder

Lyxor Asset Management

Nicolas Gaussel

Metori Capital Management

Jean-Charles Richard

Eisler Capital

Thierry Roncalli

Amundi Asset Management; University of Evry

Date Written: June 2013

Abstract

The mean-variance optimization (MVO) theory of Markowitz (1952) for portfolio selection is one of the most important methods used in quantitative finance. This portfolio allocation needs two input parameters, the vector of expected returns and the covariance matrix of asset returns. This process leads to estimation errors, which may have a large impact on portfolio weights. In this paper we review different methods which aim to stabilize the mean-variance allocation. In particular, we consider recent results from machine learning theory to obtain more robust allocation.

Keywords: Portfolio optimization, active management, estimation error, shrinkage estimator, resampling methods, eigendecomposition, norm constraints, Lasso regression, ridge regression, information matrix, hedging portfolio, sparsity

JEL Classification: G11, C60

Suggested Citation

Bruder, Benjamin and Gaussel, Nicolas and Richard, Jean-Charles and Roncalli, Thierry, Regularization of Portfolio Allocation (June 2013). Available at SSRN: https://ssrn.com/abstract=2767358 or http://dx.doi.org/10.2139/ssrn.2767358

Benjamin Bruder

Lyxor Asset Management ( email )

Paris
France

Nicolas Gaussel

Metori Capital Management ( email )

9 rue de la Paix
Paris, 75002
France

HOME PAGE: http://www.metori.com

Jean-Charles Richard

Eisler Capital ( email )

16 St. James's Street
London, SW1A1ER

Thierry Roncalli (Contact Author)

Amundi Asset Management ( email )

90 Boulevard Pasteur
Paris, 75015
France

University of Evry ( email )

Boulevard Francois Mitterrand
F-91025 Evry Cedex
France

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