A Penalty Decomposition Algorithm with Greedy Improvement for Mean-Reverting Portfolios with Sparsity and Volatility Constraints
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Date
2022-02-07
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Citation of Original Publication
Mousavi, A. and Shen, J. (2023), A penalty decomposition algorithm with greedy improvement for mean-reverting portfolios with sparsity and volatility constraints. Intl. Trans. in Op. Res., 30: 2415-2435. https://doi.org/10.1111/itor.13123
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This is the pre-peer reviewed version of the following article: Mousavi, A. and Shen, J. (2023), A penalty decomposition algorithm with greedy improvement for mean-reverting portfolios with sparsity and volatility constraints. Intl. Trans. in Op. Res., 30: 2415-2435. https://doi.org/10.1111/itor.13123, which has been published in final form at https://doi.org/10.1111/itor.13123. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
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Abstract
Mean-reverting portfolios with few assets, but high variance, are of great interest for investors in financial markets. Such portfolios are straightforwardly profitable because they include a small number of assets whose prices not only oscillate predictably around a long-term mean but also possess enough volatility. Roughly speaking, sparsity minimizes trading costs, volatility provides arbitrage opportunities, and mean-reversion property equips investors with ideal investment strategies. Finding such favorable portfolios can be formulated as a nonconvex quadratic optimization problem with an additional sparsity constraint. To the best of our knowledge, there is no method for solving this problem and enjoying favorable theoretical properties yet. In this paper, we develop an effective two-stage algorithm for this problem. In the first stage, we apply a tailored penalty decomposition method for finding a stationary point of this nonconvex problem. For a fixed penalty parameter, the block coordinate descent method is utilized to find a stationary point of the associated penalty subproblem. In the second stage, we improve the result from the first stage via a greedy scheme that solves restricted nonconvex quadratically constrained quadratic programs (QCQPs). We show that the optimal value of such a QCQP can be obtained by solving their semidefinite relaxations. Numerical experiments on S\&P 500 are conducted to demonstrate the effectiveness of the proposed algorithm.