جستجوی مقالات مرتبط با کلیدواژه "سبد بهینه سهام" در نشریات گروه "مدیریت"

Considering that in most specialized fields, decisions are made in groups, in this study, a method to select the desired option in conditions of uncertainty is presented, which also considers the group decisions to increase the effectiveness.

In this study, the ZlCWAA method and data envelopment analysis, which are responsible for the averaging of Z numbers and assigning weight to specialists, were used to create the Z number extension of the best-worst method. This method is innovative in decision-making and, in this study, we used the Z number extension of the best-worst group method.

For better decision-making, the information should be more valid, accurate, and reliable. So we compared the FBWM, ZBWM, and DEA-GZBWM methods with each other, based on the case study and data collection from the perspective of each expert on the most important indicators of optimal stock portfolio selection. This comparison showed that the proposed method of DEA-GZBWM has a lower rate of incompatibility than the others. Therefore, the information obtained in this method can be more reliable for us.

To prove the effectiveness of the DEA-GZBWM method, a case study was conducted to show how to use this method in optimal stock portfolio selection; In which the investor, with the help of financial experts (experts in financial markets), invests and selects the optimal stock portfolio from the companies in the stock exchange and securities organization. Then, based on weight, rank, and incompatibility rate, the results of the proposed method, FBWM, and ZBWM methods were compared. This comparison showed that the proposed method is more functional than the others due to its lower incompatibility rate (0.108).

Optimal stock portfolio selection is an optimization problem that can be solved by meta-heuristic algorithms. The search power of the meta-innovation algorithm is directly related to the accuracy of selecting the best stocks in the portfolio portfolio. salp swarm algorithm is one of the new meta-heuristic algorithms that has had good results in selecting the optimal stock portfolio. In this research, a new solution to strengthen the search power in salp swarm algorithm using cosine sine algorithm is presented. Research shows that all-in-one is one of the best ways to choose the best portfolio, but it is also important to consider the future as well. In the first twenty years of the stock market, from the first fifty years of 1398. In this research, using the forward neural network, the future final price of stocks is predicted and by new method for of cosine sine salp swarm algorithm is used to select the optimal stock portfolio. The results indicate that the model presented in this article, compared to traditional methods and market index, provides a higher yield for investors.