An MCDM approach to portfolio optimization

Ehrgott, Matthias; Klamroth, Kathrin; Schwehm, Christian

doi:10.1016/s0377-2217(02)00881-0

Cited by 269 publications

(126 citation statements)

References 12 publications

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“…Arenas Parra et al (2001) proposed a model that considers three criteria viz., return, risk and liquidity. Ehrgott et al (2004) took into consideration five criteria including short and long term return, dividend, ranking and risk; then use multi criteria decision making (MCDM) approach to solve the resulting optimization problem. Fang et al (2006) proposed a portfolio rebalancing model with transaction costs using fuzzy decision theory considering three criteria.…”

Section: Literature Reviewmentioning

confidence: 99%

A fuzzy compromise programming approach for the Black-Litterman portfolio selection model

Gharakhani¹,

Sadjadi²

2013

dsl

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In this paper, we examine advanced optimization approach for portfolio problem introduced by Black and Litterman to consider the shortcomings of Markowitz standard Mean-Variance optimization. Black and Litterman propose a new approach to estimate asset return. They present a way to incorporate the investor's views into asset pricing process. Since the investor's view about future asset return is always subjective and imprecise, we can represent it by using fuzzy numbers and the resulting model is multi-objective linear programming. Therefore, the proposed model is analyzed through fuzzy compromise programming approach using appropriate membership function. For this purpose, we introduce the fuzzy ideal solution concept based on investor preference and indifference relationships using canonical representation of proposed fuzzy numbers by means of their correspondingα-cuts. A real world numerical example is presented in which MSCI (Morgan Stanley Capital International Index) is chosen as the target index. The results are reported for a portfolio consisting of the six national indices. The performance of the proposed models is compared using several financial criteria.

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Section: Literature Reviewmentioning

confidence: 99%

A fuzzy compromise programming approach for the Black-Litterman portfolio selection model

Gharakhani¹,

Sadjadi²

2013

dsl

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“…And majority of them underlie Markowitz approach and his proposed mathematical model which was based on mean-variance (Markowitz, 1952). This model by keeping its centrality has been groundwork of new models of portfolio (Ehrgott et al, 2004;Campbell et al, 1997;Elton and Gruber, 1995;Jorion, 1992).…”

Section: Technology Portfolio Modelingmentioning

confidence: 99%

Technology portfolio modeling in hybrid environment

Sattari¹,

Farzad²

2011

Afr. J. Bus. Manage.

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Technology portfolio selection is one of the critical decision making process for any manager. This paper considers a fuzzy mixed portfolio selection with random fuzzy return and new hybrid algorithm approach for solving it. Fuzzy set theory is applied to model uncertain and flexible information. Since traditional technology evaluation methods is often unable to consider vague data gathered from environment, in this paper, the returns of each technology are assumed to be fuzzy random variables. In proposed hybrid algorithm we employ outputs of neural networks to produce initial solution for genetic algorithm and in order to reduce the computational work. Finally, we apply the model for investment case in carwash technologies selection. We concluded that the proposed approach can assist decision makers in selecting suitable technology portfolios, while there is a lack of reliable project information.

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“…The applications of MCDM are numerous and it has been applied to human resource management (Shih et al, 2007), transportation (Tsaur et al, 2002), portfolio optimization (Ehrgott et al, 2004), product design (Liu, 2011), vendor selection (Shyur and Shih, 2006) and visual inspection (Verma et al, 2015). The most commonly used method for MCDM is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).…”

Section: Introductionmentioning

confidence: 99%

A thermodynamical approach towards group multi-criteria decision making (GMCDM) and its application to human resource selection

Verma

Rajasankar

2017

Applied Soft Computing

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In multi-criteria decision making (MCDM) problems, ratings are assigned to the alternatives on different criteria by the expert group. In this paper, we propose a thermodynamically consistent model for MCDM using the analogies for thermodynamical indicators -energy, exergy and entropy. The most commonly used method for analysing MCDM problem is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The conventional TOPSIS method uses a measure similar to that of energy for the ranking of alternatives. We demonstrate that the ranking of the alternatives is more meaningful if we use exergy in place of energy. The use of exergy is superior due to the inclusion of a factor accounting for the quality of the ratings by the expert group. The unevenness in the ratings by the experts is measured by entropy. The procedure for the calculation of the thermodynamical indicators is explained in both crisp and fuzzy environment. Finally, two case studies are carried out to demonstrate effectiveness of the proposed model.

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An MCDM approach to portfolio optimization

Cited by 269 publications

References 12 publications

A fuzzy compromise programming approach for the Black-Litterman portfolio selection model

A fuzzy compromise programming approach for the Black-Litterman portfolio selection model

Technology portfolio modeling in hybrid environment

A thermodynamical approach towards group multi-criteria decision making (GMCDM) and its application to human resource selection

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