Statistical proxy based mean‐reverting portfolios with sparsity and volatility constraints.
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| Title: | Statistical proxy based mean‐reverting portfolios with sparsity and volatility constraints. |
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| Authors: | Mousavi, Ahmad1 (AUTHOR) mousavi@american.edu, Michilidis, George2 (AUTHOR) gmichil@ufl.edu |
| Source: | International Transactions in Operational Research. Nov2025, Vol. 32 Issue 6, p3848-3869. 22p. |
| Subjects: | Decomposition method, Standard & Poor's 500 Index, Transaction costs, Algorithms, Statistics |
| Abstract: | Mean‐reverting portfolios with volatility and sparsity constraints are of prime interest to practitioners in finance since they are both profitable and well‐diversified, while also managing risk and minimizing transaction costs. Three main measures that serve as statistical proxies to capture the mean‐reversion property are predictability, portmanteau criterion, and crossing statistics. If in addition, reasonable volatility and sparsity for the portfolio are desired, a convex quadratic or quartic objective function, subject to nonconvex quadratic and cardinality constraints needs to be minimized. In this paper, we introduce and investigate a comprehensive modeling framework that incorporates all the previous proxies proposed in the literature and develop an effective unifying algorithm that is enabled to obtain a Karush–Kuhn–Tucker (KKT) point under mild regularity conditions. Specifically, we present a tailored penalty decomposition method that approximately solves a sequence of penalized subproblems by a block coordinate descent algorithm. To the best of our knowledge, our proposed algorithm is the first method for directly solving volatile, sparse, and mean‐reverting portfolio problems based on the portmanteau criterion and crossing statistics proxies. Further, we establish that the convergence analysis can be extended to a nonconvex objective function case if the starting penalty parameter is larger than a finite bound and the objective function has a bounded level set. Numerical experiments on the S&P 500 data set demonstrate the efficiency of the proposed algorithm in comparison to a semidefinite relaxation‐based approach and suggest that the crossing statistics proxy yields more desirable portfolios. [ABSTRACT FROM AUTHOR] |
| Copyright of International Transactions in Operational Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Statistical proxy based mean‐reverting portfolios with sparsity and volatility constraints. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mousavi%2C+Ahmad%22">Mousavi, Ahmad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mousavi@american.edu</i><br /><searchLink fieldCode="AR" term="%22Michilidis%2C+George%22">Michilidis, George</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> gmichil@ufl.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Transactions+in+Operational+Research%22">International Transactions in Operational Research</searchLink>. Nov2025, Vol. 32 Issue 6, p3848-3869. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Decomposition+method%22">Decomposition method</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+%26+Poor's+500+Index%22">Standard & Poor's 500 Index</searchLink><br /><searchLink fieldCode="DE" term="%22Transaction+costs%22">Transaction costs</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Mean‐reverting portfolios with volatility and sparsity constraints are of prime interest to practitioners in finance since they are both profitable and well‐diversified, while also managing risk and minimizing transaction costs. Three main measures that serve as statistical proxies to capture the mean‐reversion property are predictability, portmanteau criterion, and crossing statistics. If in addition, reasonable volatility and sparsity for the portfolio are desired, a convex quadratic or quartic objective function, subject to nonconvex quadratic and cardinality constraints needs to be minimized. In this paper, we introduce and investigate a comprehensive modeling framework that incorporates all the previous proxies proposed in the literature and develop an effective unifying algorithm that is enabled to obtain a Karush–Kuhn–Tucker (KKT) point under mild regularity conditions. Specifically, we present a tailored penalty decomposition method that approximately solves a sequence of penalized subproblems by a block coordinate descent algorithm. To the best of our knowledge, our proposed algorithm is the first method for directly solving volatile, sparse, and mean‐reverting portfolio problems based on the portmanteau criterion and crossing statistics proxies. Further, we establish that the convergence analysis can be extended to a nonconvex objective function case if the starting penalty parameter is larger than a finite bound and the objective function has a bounded level set. Numerical experiments on the S&P 500 data set demonstrate the efficiency of the proposed algorithm in comparison to a semidefinite relaxation‐based approach and suggest that the crossing statistics proxy yields more desirable portfolios. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Transactions in Operational Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/itor.13442 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 3848 Subjects: – SubjectFull: Decomposition method Type: general – SubjectFull: Standard & Poor's 500 Index Type: general – SubjectFull: Transaction costs Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Statistics Type: general Titles: – TitleFull: Statistical proxy based mean‐reverting portfolios with sparsity and volatility constraints. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mousavi, Ahmad – PersonEntity: Name: NameFull: Michilidis, George IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09696016 Numbering: – Type: volume Value: 32 – Type: issue Value: 6 Titles: – TitleFull: International Transactions in Operational Research Type: main |
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