Model and upper–lower bound estimation scheme for portfolio optimisation considering uncertain investment time horizon

Abstract

In this paper, we study the portfolio selection problem considering the uncertain investment time horizon. We employ the Period Value at Risk (PVaR) to characterise the risk in the special cases and establish a fundamental mixed integer linear programming (MILP) model to obtain the optimal portfolio solutions. Identifying that the symmetric property of PVaR can significantly reduce the computation burden of the CPLEX solver, an enhanced MILP model is proposed. To verify the quality of obtained solutions, we also develop fast lower and upper bound estimation schemes respectively. Using the real-world data sets from NYSE and NASDAQ stock markets, numerical results are provided to show the efficiency of the enhanced model.

Keywords:

• Risk management
• portfolio optimisation
• uncertain time horizon
• mixed integer linear programming

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research is supported by the National Key R&D Program of China Grant No. 2020YFB1708202, 2021YFB3300900; the NSFC Major International (Regional) Joint Research Project Grant No. 71620107003; the Fundamental Research Funds for State Key Laboratory of Synthetical Automation for Process Industries Grant No. 2013ZCX11; Liaoning Revitalising Talent Program[XLYC1802115]; the Fundamental Research Funds for Central Universities of China[N150404016]

Notes on contributors

Dazhi Wang

Dazhi Wang received the Ph.D. degree from the College of Information Science and Engineering, Northeastern University, Shenyang, China, in 2009. He is currently a lecturer with the College of Information Science and Engineering, Northeastern University, Shenyang, China. He has published over 30 research articles in referred journals and international conferences. His research interests include financial engineering, risk management, portfolio optimisation, soft computing, evolutionary algorithms, mathematical programming, scheduling problems.

Yanhua Chen

Yanhua Chen received the B.S. degree in Automatic Control from Northeastern University, Shenyang, China, in 2019. He is studying for the Ph.D. degree in Systems Engineering in Northeastern University, Shenyang, China. His research interests include portfolio selection, heuristic search algorithm and mathematical programming.

Mingqiang Yin

Mingqiang Yin received the B.S. degree in Mathematics and Applied Mathematics and the M.S. degree in operational research and cybernetics from Bohai University, Jinzhou, China, in 2012 and 2015, respectively. He is studying for the Ph.D. degree in Systems Engineering in Northeastern University, Shenyang, China. His research interests include stochastic programming and its applications.

Min Huang

Min Huang received the B.S. degree in Automatic Instrument, the M.S. degree in Systems Engineering and the Ph.D. degree in Control Theory from Northeastern University, Shenyang, China, in 1990, 1993 and 1999, respectively. She is currently a professor with the College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. Dr. Huang has been recognised as the Distinguished Young Scholar by the National Science Foundation of China, Changjiang Scholarship Chair Professor of MOE in China. Her research interests cover the management of logistics and supply chain systems, the modelling, analytics and optimisation for manufacturing and service systems, the theory and application of planning and scheduling, risk management, behavioural operations management, data analysis and machine learning, computational intelligent, etc. Her research is supported by more than 30 projects funded by NSFC (including National Science Foundation for Distinguished Young Scholars of China, Major International Joint Research Project of NSFC, etc.), the National High-Tech Program (863 planning) of China and foundation of MOE of China as PI. She has authored over 100 journal articles, books and refereed conference papers, such as such as Omega, EJOR, AOR, C&OR, IEEE Transactions on Cybernetics, etc. She currently serves as the associate editor of Asia-Pacific Journal of Operational Research and a member of the editorial board of AJMSA. Her research interests include modelling and optimisation for logistics and supply chain system.

Chunhui Xu

Chunhui Xu received the Ph.D. degree from the College of Automation, Huazhong University of Science and Technology, Wuhan, China, in 2009 and Doctoral degree in Engineering at Tokyo Institute of Technology, Great Tokyo Area, Japan, in 2012. He is currently a professor in Department of Finance and Management Science, Chiba Institute of Technology, Chiba, Japan. He has published over 100 research articles in referred journals and international conferences, such as EJOR, JORS and so on. His research interests include decision-making under uncertainty and conflicts, optimisation, financial investment and risk management.