Portfolio Optimization Considering Financial Constraints Using Gray Wolf Algorithm

Abstract

The issue of optimal investment portfolio selection is one of the infrastructures and cornerstones of the financial industry. This issue is not only in the area of optimal resource allocation in the market, but also in the ability to meet the needs of market participants and effectively manage investment risk. Therefore, investors are constantly and continuously faced with the challenge of identifying and measuring the potential value and risk of each investment opportunity, such as stocks, using various methods, tools and criteria. One of the basic methods for managing and reducing investment risk is the use of appropriate algorithms to identify the optimal stock portfolio. In this study, portfolio optimization is presented considering financial constraints and using the Gray Wolf (GOW) algorithm. The main goal of this model is to maximize the return and minimize the risk of the portfolio. The Gray Wolf algorithm is used to solve the multi-objective optimization problem in this study due to its high ability to search for optimal solutions and escape from local optima. The models used in this study include models developed from the Markowitz mean-variance model, the mean-half-variance model, the mean-absolute deviation model, and the mean-value-at-risk conditional model, to which some constraints have been added.