Tax-Aware Portfolio Construction via Convex Optimization
Abstract We describe an optimization-based tax-aware portfolio construction method that adds tax liability to standard Markowitz-based portfolio construction. Our method produces a trade list that specifies the number of shares to buy of each asset and the number of shares to sell from each tax lot...
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| Autor*in: |
Moehle, Nicholas [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Systematik: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of optimization theory and applications - Springer US, 1967, 189(2021), 2 vom: 25. Feb., Seite 364-383 |
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| Übergeordnetes Werk: |
volume:189 ; year:2021 ; number:2 ; day:25 ; month:02 ; pages:364-383 |
| Links: |
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| DOI / URN: |
10.1007/s10957-021-01823-0 |
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OLC2125369982 |
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| 520 | |a Abstract We describe an optimization-based tax-aware portfolio construction method that adds tax liability to standard Markowitz-based portfolio construction. Our method produces a trade list that specifies the number of shares to buy of each asset and the number of shares to sell from each tax lot held. To avoid wash sales (in which some realized capital losses are disallowed), we assume that we trade monthly and cannot simultaneously buy and sell the same asset. The tax-aware portfolio construction problem is not convex, but it becomes convex when we specify, for each asset, whether we buy or sell it. It can be solved using standard mixed-integer convex optimization methods at the cost of very long solve times for some problem instances. We present a custom convex relaxation of the problem that borrows curvature from the risk model. This relaxation can provide a good approximation of the true tax liability, while greatly enhancing computational tractability. This method requires the solution of only two convex optimization problems: the first determines whether we buy or sell each asset, and the second generates the final trade list. In our numerical experiments, our method almost always solves the nonconvex problem to optimality, and when it does not, it produces a trade list very close to optimal. Backtests show that the performance of our method is indistinguishable from that obtained using a globally optimal solution, but with significantly reduced computational effort. | ||
| 650 | 4 | |a Portfolio optimization | |
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| 650 | 4 | |a Shapley-Folkman lemma | |
| 650 | 4 | |a Tax-aware portfolio management | |
| 700 | 1 | |a Kochenderfer, Mykel J. |4 aut | |
| 700 | 1 | |a Boyd, Stephen |4 aut | |
| 700 | 1 | |a Ang, Andrew |4 aut | |
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10.1007/s10957-021-01823-0 doi (DE-627)OLC2125369982 (DE-He213)s10957-021-01823-0-p DE-627 ger DE-627 rakwb eng 330 510 000 VZ 17,1 ssgn SA 6420 VZ rvk SA 6420 VZ rvk Moehle, Nicholas verfasserin aut Tax-Aware Portfolio Construction via Convex Optimization 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract We describe an optimization-based tax-aware portfolio construction method that adds tax liability to standard Markowitz-based portfolio construction. Our method produces a trade list that specifies the number of shares to buy of each asset and the number of shares to sell from each tax lot held. To avoid wash sales (in which some realized capital losses are disallowed), we assume that we trade monthly and cannot simultaneously buy and sell the same asset. The tax-aware portfolio construction problem is not convex, but it becomes convex when we specify, for each asset, whether we buy or sell it. It can be solved using standard mixed-integer convex optimization methods at the cost of very long solve times for some problem instances. We present a custom convex relaxation of the problem that borrows curvature from the risk model. This relaxation can provide a good approximation of the true tax liability, while greatly enhancing computational tractability. This method requires the solution of only two convex optimization problems: the first determines whether we buy or sell each asset, and the second generates the final trade list. In our numerical experiments, our method almost always solves the nonconvex problem to optimality, and when it does not, it produces a trade list very close to optimal. Backtests show that the performance of our method is indistinguishable from that obtained using a globally optimal solution, but with significantly reduced computational effort. Portfolio optimization Convex optimization Convex relaxations Shapley-Folkman lemma Tax-aware portfolio management Kochenderfer, Mykel J. aut Boyd, Stephen aut Ang, Andrew aut Enthalten in Journal of optimization theory and applications Springer US, 1967 189(2021), 2 vom: 25. Feb., Seite 364-383 (DE-627)129973467 (DE-600)410689-1 (DE-576)015536602 0022-3239 nnns volume:189 year:2021 number:2 day:25 month:02 pages:364-383 https://doi.org/10.1007/s10957-021-01823-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_2030 SA 6420 SA 6420 AR 189 2021 2 25 02 364-383 |
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10.1007/s10957-021-01823-0 doi (DE-627)OLC2125369982 (DE-He213)s10957-021-01823-0-p DE-627 ger DE-627 rakwb eng 330 510 000 VZ 17,1 ssgn SA 6420 VZ rvk SA 6420 VZ rvk Moehle, Nicholas verfasserin aut Tax-Aware Portfolio Construction via Convex Optimization 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract We describe an optimization-based tax-aware portfolio construction method that adds tax liability to standard Markowitz-based portfolio construction. Our method produces a trade list that specifies the number of shares to buy of each asset and the number of shares to sell from each tax lot held. To avoid wash sales (in which some realized capital losses are disallowed), we assume that we trade monthly and cannot simultaneously buy and sell the same asset. The tax-aware portfolio construction problem is not convex, but it becomes convex when we specify, for each asset, whether we buy or sell it. It can be solved using standard mixed-integer convex optimization methods at the cost of very long solve times for some problem instances. We present a custom convex relaxation of the problem that borrows curvature from the risk model. This relaxation can provide a good approximation of the true tax liability, while greatly enhancing computational tractability. This method requires the solution of only two convex optimization problems: the first determines whether we buy or sell each asset, and the second generates the final trade list. In our numerical experiments, our method almost always solves the nonconvex problem to optimality, and when it does not, it produces a trade list very close to optimal. Backtests show that the performance of our method is indistinguishable from that obtained using a globally optimal solution, but with significantly reduced computational effort. Portfolio optimization Convex optimization Convex relaxations Shapley-Folkman lemma Tax-aware portfolio management Kochenderfer, Mykel J. aut Boyd, Stephen aut Ang, Andrew aut Enthalten in Journal of optimization theory and applications Springer US, 1967 189(2021), 2 vom: 25. Feb., Seite 364-383 (DE-627)129973467 (DE-600)410689-1 (DE-576)015536602 0022-3239 nnns volume:189 year:2021 number:2 day:25 month:02 pages:364-383 https://doi.org/10.1007/s10957-021-01823-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_2030 SA 6420 SA 6420 AR 189 2021 2 25 02 364-383 |
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Tax-Aware Portfolio Construction via Convex Optimization |
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Tax-Aware Portfolio Construction via Convex Optimization |
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Moehle, Nicholas |
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Moehle, Nicholas Kochenderfer, Mykel J. Boyd, Stephen Ang, Andrew |
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tax-aware portfolio construction via convex optimization |
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Tax-Aware Portfolio Construction via Convex Optimization |
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Abstract We describe an optimization-based tax-aware portfolio construction method that adds tax liability to standard Markowitz-based portfolio construction. Our method produces a trade list that specifies the number of shares to buy of each asset and the number of shares to sell from each tax lot held. To avoid wash sales (in which some realized capital losses are disallowed), we assume that we trade monthly and cannot simultaneously buy and sell the same asset. The tax-aware portfolio construction problem is not convex, but it becomes convex when we specify, for each asset, whether we buy or sell it. It can be solved using standard mixed-integer convex optimization methods at the cost of very long solve times for some problem instances. We present a custom convex relaxation of the problem that borrows curvature from the risk model. This relaxation can provide a good approximation of the true tax liability, while greatly enhancing computational tractability. This method requires the solution of only two convex optimization problems: the first determines whether we buy or sell each asset, and the second generates the final trade list. In our numerical experiments, our method almost always solves the nonconvex problem to optimality, and when it does not, it produces a trade list very close to optimal. Backtests show that the performance of our method is indistinguishable from that obtained using a globally optimal solution, but with significantly reduced computational effort. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstractGer |
Abstract We describe an optimization-based tax-aware portfolio construction method that adds tax liability to standard Markowitz-based portfolio construction. Our method produces a trade list that specifies the number of shares to buy of each asset and the number of shares to sell from each tax lot held. To avoid wash sales (in which some realized capital losses are disallowed), we assume that we trade monthly and cannot simultaneously buy and sell the same asset. The tax-aware portfolio construction problem is not convex, but it becomes convex when we specify, for each asset, whether we buy or sell it. It can be solved using standard mixed-integer convex optimization methods at the cost of very long solve times for some problem instances. We present a custom convex relaxation of the problem that borrows curvature from the risk model. This relaxation can provide a good approximation of the true tax liability, while greatly enhancing computational tractability. This method requires the solution of only two convex optimization problems: the first determines whether we buy or sell each asset, and the second generates the final trade list. In our numerical experiments, our method almost always solves the nonconvex problem to optimality, and when it does not, it produces a trade list very close to optimal. Backtests show that the performance of our method is indistinguishable from that obtained using a globally optimal solution, but with significantly reduced computational effort. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract We describe an optimization-based tax-aware portfolio construction method that adds tax liability to standard Markowitz-based portfolio construction. Our method produces a trade list that specifies the number of shares to buy of each asset and the number of shares to sell from each tax lot held. To avoid wash sales (in which some realized capital losses are disallowed), we assume that we trade monthly and cannot simultaneously buy and sell the same asset. The tax-aware portfolio construction problem is not convex, but it becomes convex when we specify, for each asset, whether we buy or sell it. It can be solved using standard mixed-integer convex optimization methods at the cost of very long solve times for some problem instances. We present a custom convex relaxation of the problem that borrows curvature from the risk model. This relaxation can provide a good approximation of the true tax liability, while greatly enhancing computational tractability. This method requires the solution of only two convex optimization problems: the first determines whether we buy or sell each asset, and the second generates the final trade list. In our numerical experiments, our method almost always solves the nonconvex problem to optimality, and when it does not, it produces a trade list very close to optimal. Backtests show that the performance of our method is indistinguishable from that obtained using a globally optimal solution, but with significantly reduced computational effort. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Tax-Aware Portfolio Construction via Convex Optimization |
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