Regularization of Portfolio Allocation

35 Pages Posted: 21 Apr 2016  

Benjamin Bruder

Lyxor Asset Management

Nicolas Gaussel

Lyxor Asset Management; Université Paris I Panthéon-Sorbonne

Jean-Charles Richard

Lyxor Asset Management

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

Benjamin Bruder

Lyxor Asset Management ( email )

Paris
France

Nicolas Gaussel

Lyxor Asset Management ( email )

Paris
France

Université Paris I Panthéon-Sorbonne

12 place du Panthéon
Paris, IL
France

Jean-Charles Richard

Lyxor Asset Management ( email )

Paris
France

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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