The Social Sciences

Year: 2016
Volume: 11
Issue: 14
Page No. 3408 - 3417

Portfolio Selection Problem: Models Review

Authors : Rula Hani Salman AlHalaseh, Md. Aminul Islam and Rosni Bakar

Abstract: This study introduces a survey of contributions to dynamic multi-objectives portfolio from finance and operation research to the portfolio selection. This survey includes popular risk-measures and extends to operation research models and mathematical models. In contrast to other survey, this study focuses on highlighting the strengths and weaknesses of different models to choose the most appropriate model achieves the optimality and easy in application by investors. To describe the latest results accompanying each model and the similarity between them. To illustrate the modeling idea and to show the effectiveness of the proposed approach. This paper discusses in brief the most popular mean-risk models then multi-period models from the point view of operation research and stochastic programming. Many researchers conducted portfolio optimization problem by offering new models. These researches success in providing mathematical and theoretical models that enriched the finance literature but few of it satisfies the market application. This study reviewed some of these models relating to single-period, multi-periods models, single-objective and multi-objectives and concluded that SGMIP is the most effective model since it able to deal with real world application considering multi-factors, multi-periods, different risk measures without affecting the computation time which facilitate the mission of decision makers. There is a plenty of models discussing optimizing portfolio for that the writer of this study selects the original models MV and MAV, risk measures models VaR and CVaR and the latest models related to operation research to achieve the study objectives.

How to cite this article:

Rula Hani Salman AlHalaseh, Md. Aminul Islam and Rosni Bakar, 2016. Portfolio Selection Problem: Models Review. The Social Sciences, 11: 3408-3417.

Design and power by Medwell Web Development Team. © Medwell Publishing 2022 All Rights Reserved