A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices
Portfolio optimization is one of the most important problems in the finance field. The traditional Markowitz mean-variance model is often unrealistic since it relies on the perfect market information. In this work, we propose a two-stage stochastic portfolio optimization model with a comprehensive s...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2020
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| Online Access: | https://eprints.nottingham.ac.uk/60468/ |
| _version_ | 1848799766946775040 |
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| author | Cui, Tianxiang Bai, Ruibin Ding, Shusheng Parkes, Andrew J. Qu, Rong He, Fang Li, Jingpeng |
| author_facet | Cui, Tianxiang Bai, Ruibin Ding, Shusheng Parkes, Andrew J. Qu, Rong He, Fang Li, Jingpeng |
| author_sort | Cui, Tianxiang |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Portfolio optimization is one of the most important problems in the finance field. The traditional Markowitz mean-variance model is often unrealistic since it relies on the perfect market information. In this work, we propose a two-stage stochastic portfolio optimization model with a comprehensive set of real-world trading constraints to address this issue. Our model incorporates the market uncertainty in terms of future asset price scenarios based on asset return distributions stemming from the real market data. Compared with existing models, our model is more reliable since it encompasses real-world trading constraints and it adopts CVaR as the risk measure. Furthermore, our model is more practical because it could help investors to design their future investment strategies based on their future asset price expectations. In order to solve the proposed stochastic model, we develop a hybrid combinatorial approach, which integrates a hybrid algorithm and a linear programming (LP) solver for the problem with a large number of scenarios. The comparison of the computational results obtained with three different metaheuristic algorithms and with our hybrid approach shows the effectiveness of the latter. The superiority of our model is mainly embedded in solution quality. The results demonstrate that our model is capable of solving complex portfolio optimization problems with tremendous scenarios while maintaining high solution quality in a reasonable amount of time and it has outstanding practical investment implications, such as effective portfolio constructions. |
| first_indexed | 2025-11-14T20:40:53Z |
| format | Article |
| id | nottingham-60468 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:40:53Z |
| publishDate | 2020 |
| publisher | Springer Nature |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-604682020-04-28T02:14:22Z https://eprints.nottingham.ac.uk/60468/ A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices Cui, Tianxiang Bai, Ruibin Ding, Shusheng Parkes, Andrew J. Qu, Rong He, Fang Li, Jingpeng Portfolio optimization is one of the most important problems in the finance field. The traditional Markowitz mean-variance model is often unrealistic since it relies on the perfect market information. In this work, we propose a two-stage stochastic portfolio optimization model with a comprehensive set of real-world trading constraints to address this issue. Our model incorporates the market uncertainty in terms of future asset price scenarios based on asset return distributions stemming from the real market data. Compared with existing models, our model is more reliable since it encompasses real-world trading constraints and it adopts CVaR as the risk measure. Furthermore, our model is more practical because it could help investors to design their future investment strategies based on their future asset price expectations. In order to solve the proposed stochastic model, we develop a hybrid combinatorial approach, which integrates a hybrid algorithm and a linear programming (LP) solver for the problem with a large number of scenarios. The comparison of the computational results obtained with three different metaheuristic algorithms and with our hybrid approach shows the effectiveness of the latter. The superiority of our model is mainly embedded in solution quality. The results demonstrate that our model is capable of solving complex portfolio optimization problems with tremendous scenarios while maintaining high solution quality in a reasonable amount of time and it has outstanding practical investment implications, such as effective portfolio constructions. Springer Nature 2020-02-01 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/60468/1/A%20hybrid%20combinatorial%20approach%20to%20a%20two-stage%20stochastic%20portfolio%20optimization%20model%20with%20uncertain%20asset%20prices.pdf Cui, Tianxiang, Bai, Ruibin, Ding, Shusheng, Parkes, Andrew J., Qu, Rong, He, Fang and Li, Jingpeng (2020) A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices. Soft Computing, 24 (4). pp. 2809-2831. ISSN 1432-7643 Hybrid Algorithm; Combinatorial Approach; Stochastic Programming; Population-based Incremental Learning; Local Search; Learning Inheritance; Portfolio Optimization Problem http://dx.doi.org/10.1007/s00500-019-04517-y doi:10.1007/s00500-019-04517-y doi:10.1007/s00500-019-04517-y |
| spellingShingle | Hybrid Algorithm; Combinatorial Approach; Stochastic Programming; Population-based Incremental Learning; Local Search; Learning Inheritance; Portfolio Optimization Problem Cui, Tianxiang Bai, Ruibin Ding, Shusheng Parkes, Andrew J. Qu, Rong He, Fang Li, Jingpeng A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices |
| title | A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices |
| title_full | A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices |
| title_fullStr | A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices |
| title_full_unstemmed | A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices |
| title_short | A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices |
| title_sort | hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices |
| topic | Hybrid Algorithm; Combinatorial Approach; Stochastic Programming; Population-based Incremental Learning; Local Search; Learning Inheritance; Portfolio Optimization Problem |
| url | https://eprints.nottingham.ac.uk/60468/ https://eprints.nottingham.ac.uk/60468/ https://eprints.nottingham.ac.uk/60468/ |