Multi-period and Multi-objective Stock Selection Optimization Model Based on Fuzzy Interval Approach

Document Type : Original Article

Authors

1 Department of Financial Engineering, Kooshyar Higher Education Institute, Rasht, Iran

2 Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran

3 Department of Industrial Engineering, Gilan University, Rasht, Iran

Abstract

The optimization of investment portfolios is the most important topic in financial decision making, and many relevant models can be found in the literature.  According to importance of portfolio optimization in this paper, deals with novel solution approaches to solve new developed portfolio optimization model. Contrary to previous work, the uncertainty of future returns of a given portfolio is modeled using LR-FUZZY numbers while the function of its return are evaluated using possibility theory. We used a novel Lp-metric method to solve the model. The efficacy of the proposed model is tested on criterion problems of portfolio optimization  on LINGO provides a framework to optimize objectives when creating the loan portfoliso, in a search for a dynamic markets decision. In addition to, the performance of the proposed efficiently encoded multi-objective portfolio optimization solver is assessed in comparison with two well-known MOEAs, namely NSGAII and ICA. To the best of our knowledge, there is no research that considered NSGAΠ, ICA fuzzy simultaneously. Due to improve the performance of algorithm, the performance of this approach more study is probed by using a dataset of assets from the Iran’s stock market for three years historical data and PRE method. The results are analyzed through novel performance parameters RPD method. Thus, the potential of our comparison led to improve different portfolios in different generations.

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1. Cheraghalipour, A., Paydar, M.M. and Hajiaghaei-Keshteli, M.,
“An integrated approach for collection center selection in reverse
logistics”, International Journal of Engineering - Transactions
A: Basics, Vol. 30, No. 7, (2017), 1005–1016.  
2. Markowitz, H., “Portfolio selection”, The Journal of Finance,
Vol. 7, No. 1, (1952), 77–91.  
3. Saborido, R., Ruiz, A.B., Bermúdez, J.D., Vercher, E. and Luque,
M., “Evolutionary multi-objective optimization algorithms for
fuzzy portfolio selection”, Applied Soft Computing, Vol. 39,
(2016), 48–63.  
4. Bermúdez, J.D., Segura, J.V., and Vercher, E., “A multi-objective
genetic algorithm for cardinality constrained fuzzy portfolio 
selection”, Fuzzy Sets and Systems, Vol. 188, No. 1, (2012), 16–
26. 
5. Vercher, E. and Bermudez, J.D., “A Possibilistic Mean-Downside 
Risk-Skewness Model for Efficient Portfolio Selection”, IEEE
Transactions on Fuzzy Systems, Vol. 21, No. 3, (2013), 585–595.  
6. Kaviyani-Charati, M., Heidarzadeh Souraki, F. and HajiaghaeiKeshteli,
M.,
“A
Robust
Optimization
Methodology
for Multiobjective
Location-transportation
Problem
in Disaster Response
Phase under Uncertainty”, International Journal of Engineering
- Transactions B: Applications, Vol. 31, No. 11, (2018), 1953–
1961.  
7. Li, X., Qin, Z., and Kar, S., “Mean-variance-skewness model for
portfolio selection with fuzzy returns”, European Journal of
Operational Research, Vol. 202, No. 1, (2010), 239–247.  
8. Vercher, E. and Bermúdez, J.D., “Portfolio optimization using a
credibility mean-absolute semi-deviation model”, Expert Systems
with Applications, Vol. 42, No. 20, (2015), 7121–7131.  9. Wang, S. and Xia, Y., Portfolio Selection and Asset Pricing,
Springer Berlin Heidelberg, Vol. 514, (2002). 
10. Bermudez, J.D., Segura, J.V., and Vercher, E., “A fuzzy ranking
strategy for portfolio selection applied to the Spanish stock
market”, 2007 IEEE International Fuzzy Systems Conference,
(2007), 1–4.  
11. Harvey, C.R., Liechty, J.C., Liechty, M.W. and Müller, P.,
“Portfolio selection with higher moments”, Quantitative
Finance, Vol. 10, No. 5, (2010), 469–485.  
12. Cao, J.L., “Algorithm research based on multi period fuzzy
portfolio optimization model”, Cluster Computing, (2018), 1–8.  
13. Liu, S., Wang, S. Y., and Qiu, W., “Mean-variance-skewness
model for portfolio selection with transaction costs”,
International Journal of Systems Science, Vol. 34, No. 4,
(2003), 255–262.  
14. Gupta, P., Inuiguchi, M., Mehlawat, M.K. and Mittal, G.,
“Multiobjective credibilistic portfolio selection model with fuzzy
chance-constraints”, Information Sciences, Vol. 229, (2013), 1–17.  
15. Mokhtarian Asl, M. and Sattarvand, J., “Integration of commodity
price uncertainty in long-term open pit mine production planning
by using an imperialist competitive algorithm”, Journal of the
Southern African Institute of Mining and Metallurgy, Vol. 118,
No. 2, (2018), 165–172.  
16. Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S. and Luo, J., “New
Frontiers in Applied Data Mining”, PAKDD: Pacific-Asia
Conference on Knowledge Discovery and Data Mining, PAKDD
2011 International Workshops, Shenzhen, China, (2012). 
17. Barbati, M., Greco, S., Kadziński, M. and Słowiński, R.,
“Optimization of multiple satisfaction levels in portfolio decision
analysis”, Omega, Vol. 78, (2018), 192–204.  
18. Liagkouras, K. and Metaxiotis, K., “A new efficiently encoded
multiobjective algorithm for the solution of the cardinality
constrained portfolio optimization problem”, Annals of
Operations Research, Vol. 267, No. 1–2, (2018), 281–319.  
19. Li, H. and Zhang, Q., “Multiobjective Optimization Problems
With Complicated Pareto Sets, MOEA/D and NSGA-II”, IEEE
Transactions on Evolutionary Computation, Vol. 13, No. 2,
(2009), 284–302.