Regularization of Portfolio Allocation

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