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Time-varying mean–variance portfolio selection problem solving via LVI-PDNN

Vasilios N. Katsikis, Spyridon D. Mourtas, Predrag S. Stanimirović, Shuai Li Orcid Logo, Xinwei Cao

Computers & Operations Research, Volume: 138, Start page: 105582

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.cor.2021.105582

Abstract

It is widely acclaimed that the Markowitz mean–variance portfolio selection is a very important investment strategy. One approach to solving the static mean–variance portfolio selection (MVPS) problem is based on the usage of quadratic programming (QP) methods. In this article, we define and study t...

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Published in: Computers & Operations Research
ISSN: 0305-0548
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa58177
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spelling 2021-11-10T16:24:40.1522333 v2 58177 2021-10-04 Time-varying mean–variance portfolio selection problem solving via LVI-PDNN 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-10-04 MECH It is widely acclaimed that the Markowitz mean–variance portfolio selection is a very important investment strategy. One approach to solving the static mean–variance portfolio selection (MVPS) problem is based on the usage of quadratic programming (QP) methods. In this article, we define and study the time-varying mean–variance portfolio selection (TV-MVPS) problem both in the cases of a fixed target portfolio’s expected return and for all possible portfolio’s expected returns as a time-varying quadratic programming (TVQP) problem. The TV-MVPS also comprises the properties of a moving average. These properties make the TV-MVPS an even greater analysis tool suitable to evaluate investments and identify trading opportunities across a continuous-time period. Using an originally developed linear-variational-inequality primal–dual neural network (LVI-PDNN), we also provide an online solution to the static QP problem. To the best of our knowledge, this is an innovative approach that incorporates robust neural network techniques to provide an online, thus more realistic, solution to the TV-MVPS problem. In this way, we present an online solution to a time-varying financial problem while eliminating static method limitations. It has been shown that when applied simultaneously to TVQP problems subject to equality, inequality and boundary constraints, the LVI-PDNN approaches the theoretical solution. Our approach is also verified by numerical experiments and computer simulations as an excellent alternative to conventional MATLAB methods. Journal Article Computers & Operations Research 138 105582 Elsevier BV 0305-0548 Portfolio selection, Time-varying systems, Quadratic programming, Continuous neural networks 1 2 2022 2022-02-01 10.1016/j.cor.2021.105582 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2021-11-10T16:24:40.1522333 2021-10-04T09:53:24.2389759 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Vasilios N. Katsikis 1 Spyridon D. Mourtas 2 Predrag S. Stanimirović 3 Shuai Li 0000-0001-8316-5289 4 Xinwei Cao 5 58177__21080__f1001a801e50479ebd0c4d92c5dd300a.pdf 58177.pdf 2021-10-04T09:55:09.2896385 Output 4163692 application/pdf Accepted Manuscript true 2023-03-30T00:00:00.0000000 ©2021 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Time-varying mean–variance portfolio selection problem solving via LVI-PDNN
spellingShingle Time-varying mean–variance portfolio selection problem solving via LVI-PDNN
Shuai Li
title_short Time-varying mean–variance portfolio selection problem solving via LVI-PDNN
title_full Time-varying mean–variance portfolio selection problem solving via LVI-PDNN
title_fullStr Time-varying mean–variance portfolio selection problem solving via LVI-PDNN
title_full_unstemmed Time-varying mean–variance portfolio selection problem solving via LVI-PDNN
title_sort Time-varying mean–variance portfolio selection problem solving via LVI-PDNN
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Vasilios N. Katsikis
Spyridon D. Mourtas
Predrag S. Stanimirović
Shuai Li
Xinwei Cao
format Journal article
container_title Computers & Operations Research
container_volume 138
container_start_page 105582
publishDate 2022
institution Swansea University
issn 0305-0548
doi_str_mv 10.1016/j.cor.2021.105582
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description It is widely acclaimed that the Markowitz mean–variance portfolio selection is a very important investment strategy. One approach to solving the static mean–variance portfolio selection (MVPS) problem is based on the usage of quadratic programming (QP) methods. In this article, we define and study the time-varying mean–variance portfolio selection (TV-MVPS) problem both in the cases of a fixed target portfolio’s expected return and for all possible portfolio’s expected returns as a time-varying quadratic programming (TVQP) problem. The TV-MVPS also comprises the properties of a moving average. These properties make the TV-MVPS an even greater analysis tool suitable to evaluate investments and identify trading opportunities across a continuous-time period. Using an originally developed linear-variational-inequality primal–dual neural network (LVI-PDNN), we also provide an online solution to the static QP problem. To the best of our knowledge, this is an innovative approach that incorporates robust neural network techniques to provide an online, thus more realistic, solution to the TV-MVPS problem. In this way, we present an online solution to a time-varying financial problem while eliminating static method limitations. It has been shown that when applied simultaneously to TVQP problems subject to equality, inequality and boundary constraints, the LVI-PDNN approaches the theoretical solution. Our approach is also verified by numerical experiments and computer simulations as an excellent alternative to conventional MATLAB methods.
published_date 2022-02-01T04:14:30Z
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