Skip to content
All library documents

Designing Mean-Reverting Portfolios for Statistical Arbitrage

Article arXiv papers · Author: Ziping Zhao et al.
Read the original

Summary

This paper addresses how to construct a portfolio from underlying assets for use in statistical arbitrage, including pairs trading. The design seeks a portfolio with useful mean-reversion behavior while also controlling its variance, subject to an investment leverage constraint.

The authors formulate these objectives together and propose a successive convex approximation method for solving the resulting optimization problem. They report numerical simulations to assess the proposed model and algorithms. The document describes the design goals and solution approach but gives no specific assets, simulation outcomes, or empirical trading results, so it does not establish how the method performs in live markets or across different datasets.

Key ideas

  • The portfolio design problem targets both mean-reversion strength and variance characteristics.
  • The approach is intended for statistical arbitrage strategies such as pairs trading.
  • An investment leverage constraint is included in the formulation.
  • Successive convex approximation is used to solve the optimization problem.
  • Numerical simulations are reported as the method's evaluation, without details in the provided text.

Tags

Portfolio constructionMean reversionPairs tradingArbitrageStatistics
Full text
# Optimal Portfolio Design for Statistical Arbitrage in Finance


# Optimal Portfolio Design for Statistical Arbitrage in Finance









In this paper, the optimal mean-reverting portfolio (MRP) design problem is considered, which plays an important role for the statistical arbitrage (a.k.a. pairs trading) strategy in financial markets. The target of the optimal MRP design is to construct a portfolio from the underlying assets that can exhibit a satisfactory mean reversion property and a desirable variance property. A general problem formulation is proposed by considering these two targets and an investment leverage constraint. To solve this problem, a successive convex approximation method is used. The performance of the proposed model and algorithms are verified by numerical simulations.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.