Mean-Reverting Portfolio Design via Majorization-Minimization Method

TL;DR

This paper introduces a novel algorithm based on the majorization-minimization method for designing mean-reverting portfolios, optimizing mean-reversion strength while considering variance and budget constraints, outperforming existing methods.

Contribution

The paper proposes an efficient MM-based algorithm for mean-reverting portfolio design that improves performance over existing approaches.

Findings

The proposed method significantly outperforms single spreads.

It surpasses benchmark methods in mean-reversion strength.

Numerical results validate the effectiveness of the approach.

Abstract

This paper considers the mean-reverting portfolio design problem arising from statistical arbitrage in the financial markets. The problem is formulated by optimizing a criterion characterizing the mean-reversion strength of the portfolio and taking into consideration the variance of the portfolio and an investment budget constraint at the same time. An efficient algorithm based on the majorization-minimization (MM) method is proposed to solve the problem. Numerical results show that our proposed mean-reverting portfolio design method can significantly outperform every underlying single spread and the benchmark method in the literature.