Proximal Linearized Method for Sparse Equity Portfolio Optimization with Minimum Transaction Cost

TL;DR

This paper introduces a sparse equity portfolio optimization model that minimizes transaction costs by promoting sparsity in asset weights using an $-regularized mean-variance framework, solved efficiently with a convergent ADMM-like algorithm.

Contribution

It develops a novel sparse portfolio optimization model with $$-norm regularization and minimum return constraint, solved via an efficient proximal ADMM-like algorithm with proven convergence.

Findings

The proposed algorithm converges globally.

The model achieves higher expected returns with fewer assets.

It effectively reduces transaction costs in real data applications.

Abstract

In this paper, we propose a sparse equity portfolio optimization (SEPO) based on the mean-variance portfolio selection model. Aimed at minimizing transaction cost by avoiding small investments, this new model includes $\ell_0$-norm regularization of the asset weights to promote sparsity, hence the acronym SEPO-$\ell_0$. The selection model is also subjected to a minimum expected return. The complexity of the model calls for proximal method, which allows us to handle the objectives terms separately via the corresponding proximal operators. We develop an efficient ADMM-like algorithm to find the optimal portfolio and prove its global convergence. The efficiency of the algorithm is demonstrated using real stock data and the model is promising in portfolio selection in terms of generating higher expected return while maintaining good level of sparsity, and thus minimizing transaction cost.