Some improved sparse and stable portfolio optimization problems

Abstract

Parameter uncertainty and estimation errors often cause the presence of unstable asset weights and the poor performance of portfolio model. In addition, in the real world, most investors prefer to choose a small number of stocks to invest. In this paper, we propose some improved sparse and stable portfolio models by combining the shrinkage method and objective function L1 regularization method. An ‘optimal’ shrinkage constant is obtained by minimizes the expected distance between the shrinkage estimator and the true covariance matrix. Moreover, we investigate the combination of the constant correlation and objective function L1 regularization method. Empirical studies show that the proposed strategies have better out-of-sample performance than many other strategies for tested datasets.