Designing Budget-Constrained Mean-Reverting Portfolios

Summary

The paper formulates a portfolio design problem for statistical arbitrage, seeking combinations of underlying assets with strong mean-reverting behavior. Its objective balances a criterion measuring mean-reversion strength against portfolio variance, while enforcing an investment budget constraint. It then develops several specific formulations and proposes efficient algorithms to solve them.

Numerical studies using synthetic and market data are reported to show that the methods can produce consistent profits and outperform traditional portfolio design and benchmark methods discussed in the literature. The excerpt does not state the markets, evaluation periods, transaction cost assumptions, or magnitude and robustness of the results. Since the approach is motivated by statistical arbitrage, those omitted implementation details matter when judging whether designed portfolios remain profitable after trading costs and out of sample.

Key ideas

• The design objective seeks asset portfolios with strong mean-reverting behavior.
• Portfolio variance and an investment budget constraint are included in the formulation.
• The paper considers multiple formulations and proposes efficient solution algorithms.
• Numerical results on synthetic and market data are reported to outperform traditional and benchmark designs.
• The excerpt does not specify transaction costs, evaluation periods, or the robustness of market performance.

Tags

# Mean-Reverting Portfolio Design with Budget Constraint


# Mean-Reverting Portfolio Design with Budget Constraint









This paper considers the mean-reverting portfolio design problem arising from statistical arbitrage in the financial markets. We first propose a general problem formulation aimed at finding a portfolio of underlying component assets by optimizing a mean-reversion criterion characterizing the mean-reversion strength, taking into consideration the variance of the portfolio and an investment budget constraint. Then several specific problems are considered based on the general formulation, and efficient algorithms are proposed. Numerical results on both synthetic and market data show that our proposed mean-reverting portfolio design methods can generate consistent profits and outperform the traditional design methods and the benchmark methods in the literature.

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