Mendeley
TY  _  JOUR
ID  -  13971009166569
TI  -  Portfolio Selection under Trading Constraints and Data Uncertainty Using Robust Optimization Approach and NSGA-II Algorithm
JO  -  Modiriat-e- farda
JA  -  ES
LA  -  fa
SN  -  2228-6047
AU  -  Peykani Pejman
AU  -  Mohammadi Emran
AU  -  Barzinpour Farnaz 
AU  -  Jandaghian Alireza
AD  -  دانشگاه علم و صنعت، تهران، ایران
AD  -  دانشگاه علم و صنعت
AD  -  Iran university of science and Technology
AD  -  دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران
Y1  -  2020
PY  -  2020
VL  -  62
IS  -  
SP  -  206
EP  -  195
KW  -  Portfolio Selection Problem
Trading Constraints
Robust Optimization
Uncertainty
NSGA-II Algorithm
DO  -  
N2  -  Portfolio is a collection or combination of investments in financial and non-financial assets that may be carried out by an individual or organization. How to select and optimize of portfolio is very important. One of the most important points that should be considered in the proposed approach for portfolio selection, is uncertainty. Because, one of the most important features of financial markets is their uncertainty. Thus, the purpose of this study is to present a bi-objective model for portfolio selection that is capable to be used under uncertainty of financial data and for this purpose, a robust optimization approach has been used. It should be noted that return and conditional value at risk (CVaR) are considered as model objectives, and the constraints of the number of shares and the purchasing volume of each share have been added to the model. Also, due to the complexity of the proposed model, a NSGA-II meta-heuristic algorithm has been used to solve the suggested model of research. Finally, the presented model was solved by using the actual data of 200 stocks of Tehran stock market for the period of 2017 and the results were analyzed. The results indicate the efficiency of the proposed approach portfolio selection according to the investor's preferences and constraints under uncertainty of financial data.
UR  - rimag.ir/en/Article/25985
L1  - rimag.ir/en/Article/Download/25985
ER  -  
