Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30509
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices
Author(s): Cui, Tianxiang
Bai, Ruibin
Ding, Shusheng
Parkes, Andrew J
Qu, Rong
He, Fang
Li, Jingpeng
Contact Email: jli@cs.stir.ac.uk
Keywords: Hybrid algorithm
Combinatorial approach
Stochastic programming
Population-based incremental learning
Local search
Learning inheritance
Portfolio optimization problem
Issue Date: Feb-2020
Date Deposited: 6-Dec-2019
Citation: Cui T, Bai R, Ding S, Parkes AJ, Qu R, He F & Li J (2020) A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices. Soft Computing, 24 (4), p. 2809–2831. https://doi.org/10.1007/s00500-019-04517-y
Abstract: 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.
DOI Link: 10.1007/s00500-019-04517-y
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is a post-peer-review, pre-copyedit version of an article published in Soft Computing. The final authenticated version is available online at: https://doi.org/10.1007/s00500-019-04517-y
Licence URL(s): https://storre.stir.ac.uk/STORREEndUserLicence.pdf

Files in This Item:
File Description SizeFormat 
portfolio_journal.pdfFulltext - Accepted Version460.06 kBAdobe PDFView/Open



This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.