A symmetric Gauss-Seidel based method for a class of multi-period mean-variance portfolio selection problems

Abstract

It is commonly accepted that the estimation error of asset returns' sample mean is much larger than that of sample covariance. In order to hedge the risk raised by the estimation error of the sample mean, we propose a sparse and robust multi-period mean-variance portfolio selection model and show how this proposed model can be equivalently reformulated as a multi-block nonsmooth convex optimization problem. In order to get an optimal strategy, a symmetric Gauss-Seidel based method is implemented. Moreover, we show that the algorithm is globally linearly convergent. The effectiveness of our portfolio selection model and the efficiency of its solution method are demonstrated by empirical experiments on both the synthetic and real datasets.