Robust goal programming for multi-objective portfolio selection problem

Ghahtarani, Alireza; Najafi, Amir Abbas

doi:10.1016/j.econmod.2013.05.006

Cited by 42 publications

(24 citation statements)

References 31 publications

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“…• Ghahtarani & Najafi (2013) combined the robust stochastic optimization (Mulvey et al, 1995) with the GP, developing the Robust Goal Programming (RGP) model, which was applied in selecting investment portfolios in the stock market;…”

Section: • Net Present Value Methods (Npv) -It Allowsmentioning

confidence: 99%

Programação por Metas Fuzzy aplicada ao processo de orçamento de capital em um ambiente econômico sob incerteza

et al. 2017

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Resumo: A Programação por Metas (Goal Programming -GP) é uma abordagem multicritério da Pesquisa Palavras-chave: Programação por Metas Fuzzy; Orçamento de capital; Ambiente econômico sob incerteza.Abstract: The Goal Programming (GP) is a multi-criteria approach of Operational Research that has been used for solving complex decision problems. This paper proposes a new Fuzzy Goal Programming (FGP) model to handle the process of capital budget of companies in an economic environment under uncertainty. For performance comparison purposes, the FGP and another recently published model developed for the same purposes were applied to data from a company that was the object of the study. The modeling and optimization were made with the GAMS software -23.6.5 and using the CPLEX solver. The results obtained from the FGP model provided higher improvements than those obtained with the alternative model, as for example: increased profitability index, reduced payback and better application of the capital available in the budget. Furthermore, the FGP model has flexibility features that allow the manager to simulate, quickly and easily obtaining results about scenarios under uncertainty.

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Section: • Net Present Value Methods (Npv) -It Allowsmentioning

confidence: 99%

Programação por Metas Fuzzy aplicada ao processo de orçamento de capital em um ambiente econômico sob incerteza

et al. 2017

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“…Masmoudi and Abdelaziz [14] presented a bi-objective stochastic programming, portfolio optimization model, which is solved by goal programming with the objectives return and risk. Ghahtarani and Najafi [3] presented robust optimization goal programing for portfolio selection problem. Siew and Hoe [12] applied a goal programming model using mean return and tracking error for optimizing portfolio.…”

Section: Introductionmentioning

confidence: 99%

Portfolio Selection and Post Optimality Test Using Goal Programming

Panwar¹,

Jha²,

Srivastava³

2018

IJET

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In a practical portfolio planning process the investment decision to be taken by an investor is not simple and is influenced by several other constraints like stock price, co-moment with market, return with respect to risk, past performance and so many. In this purview, a hybrid approach is employed for portfolio selection which combines multiple methodologies like investor topology, cluster analysis, analyti cal hierarchy process (AHP) for ranking the assets and biogeographic-based optimization (BBO). Furthermore, with the help of goal programming (GP), performing post optimality test for betterment the result which is obtained by BBO. In the goal programming, objective is to be minimizing the weighted deviations of desire goals. Weighted deviation is known as achievement, which has two branches namely over achievement and under achievement.

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“…Satisficing is an old Scottish word that defines the desire to find a practical and real‐world solution to a problem, rather than an idealistic or optimal solution to a highly simplified model of that problem. These properties have made GP a widely used multiobjective technique for dealing with decision‐making problems with several conflicting objectives and incomplete or imprecise information (Romero, ; Ghahtarani and Najafi, ). In GP, the decision maker usually seeks a useful, practical, implementable, and attainable solution rather than one satisfying the mathematician's desire (Ignizio, ).…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have explored and reviewed the use of various types of GP in portfolio analysis. Specifically in fuzzy GP (Wu and Tsai, ; Messaoudi et al., ), robust optimization (Ghahtarani and Najafi, ), polynomial GP (Briec et al., ), stochastic GP (Abdulaziz and Masmoudi, ; Masri, ), lexicographic and nonlinear GP (Pardalos et al., ; Dash and Kajiji, ), and the weighted GP (Azmi and Tamiz, ; Aouni et al., ). Bravo et al.…”

Section: Introductionmentioning

confidence: 99%

Goal programming with extended factors for portfolio selection

Tamiz

Azmi

2017

Int Trans Operational Res

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This paper proposes and examines the use of several stock‐related factors, called extended factors, for portfolio selection. These factors, including the traditional factors of risk and return, are represented as objectives in weighted goal programming (WGP) models. Several WGP models with passive and active target values and various weights for their unwanted deviational variables in their achievement functions have been developed. The weights and target values of the extended factors represent decision makers’ utility toward their portfolio. The resulting portfolios for the proposed models are compared against each other as well as against the Dow Jones Industrial Average index and portfolios obtained from the well‐established Markowitz and Konno and Yamazaki's models. The experimental results strongly support the use of extended factors for portfolio selection problems and the assumption of meeting decision maker's preferences and utilities better than the portfolios based entirely on risk and return.

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Robust goal programming for multi-objective portfolio selection problem

Cited by 42 publications

References 31 publications

Programação por Metas Fuzzy aplicada ao processo de orçamento de capital em um ambiente econômico sob incerteza

Programação por Metas Fuzzy aplicada ao processo de orçamento de capital em um ambiente econômico sob incerteza

Portfolio Selection and Post Optimality Test Using Goal Programming

Goal programming with extended factors for portfolio selection

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