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Multiobjective Programming and Multiattribute Utility Functions in Portfolio Optimization

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Multiobjective Programming and Multiattribute Utility Functions in Portfolio Optimization. / Ehrgott, Matthias; Waters, Chris; Kasimbeyli, Refail et al.
In: Information Systems and Operational Research , Vol. 47, No. 1, 01.02.2009, p. 31-42.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Ehrgott, M, Waters, C, Kasimbeyli, R & Ustun, O 2009, 'Multiobjective Programming and Multiattribute Utility Functions in Portfolio Optimization', Information Systems and Operational Research , vol. 47, no. 1, pp. 31-42. https://doi.org/10.3138/infor.47.1.31

APA

Ehrgott, M., Waters, C., Kasimbeyli, R., & Ustun, O. (2009). Multiobjective Programming and Multiattribute Utility Functions in Portfolio Optimization. Information Systems and Operational Research , 47(1), 31-42. https://doi.org/10.3138/infor.47.1.31

Vancouver

Ehrgott M, Waters C, Kasimbeyli R, Ustun O. Multiobjective Programming and Multiattribute Utility Functions in Portfolio Optimization. Information Systems and Operational Research . 2009 Feb 1;47(1):31-42. doi: 10.3138/infor.47.1.31

Author

Ehrgott, Matthias ; Waters, Chris ; Kasimbeyli, Refail et al. / Multiobjective Programming and Multiattribute Utility Functions in Portfolio Optimization. In: Information Systems and Operational Research . 2009 ; Vol. 47, No. 1. pp. 31-42.

Bibtex

@article{1c83ae51f6a442d09e305b0fbe1a2a7a,
title = "Multiobjective Programming and Multiattribute Utility Functions in Portfolio Optimization",
abstract = "In recent years portfolio optimization models that consider more criteria than the expected return and variance objectives of the Markowitz model have become popular. These models are harder to solve than the quadratic mean-variance problem. Two approaches to find a suitable portfolio for an investor are possible. In the multiattribute utility theory (MAUT) approach a utility function is constructed based on the investor's preferences and an optimization problem is solved to find a portfolio that maximizes the utility function. In the multiobjective programming (MOP) approach a set of efficient portfolios is computed by optimizing a scalarized objective function. The investor then chooses a portfolio from the efficient set according to his/her preferences. We outline these two approaches using the UTADIS method to construct a utility function and present numerical results for an example",
keywords = "Portfolio optimization, multiobjective programming , multiattribute utility function , UTADIS",
author = "Matthias Ehrgott and Chris Waters and Refail Kasimbeyli and Ozden Ustun",
year = "2009",
month = feb,
day = "1",
doi = "10.3138/infor.47.1.31",
language = "English",
volume = "47",
pages = "31--42",
journal = "Information Systems and Operational Research ",
issn = "0315-5986",
publisher = "University of Toronto Press",
number = "1",

}

RIS

TY - JOUR

T1 - Multiobjective Programming and Multiattribute Utility Functions in Portfolio Optimization

AU - Ehrgott, Matthias

AU - Waters, Chris

AU - Kasimbeyli, Refail

AU - Ustun, Ozden

PY - 2009/2/1

Y1 - 2009/2/1

N2 - In recent years portfolio optimization models that consider more criteria than the expected return and variance objectives of the Markowitz model have become popular. These models are harder to solve than the quadratic mean-variance problem. Two approaches to find a suitable portfolio for an investor are possible. In the multiattribute utility theory (MAUT) approach a utility function is constructed based on the investor's preferences and an optimization problem is solved to find a portfolio that maximizes the utility function. In the multiobjective programming (MOP) approach a set of efficient portfolios is computed by optimizing a scalarized objective function. The investor then chooses a portfolio from the efficient set according to his/her preferences. We outline these two approaches using the UTADIS method to construct a utility function and present numerical results for an example

AB - In recent years portfolio optimization models that consider more criteria than the expected return and variance objectives of the Markowitz model have become popular. These models are harder to solve than the quadratic mean-variance problem. Two approaches to find a suitable portfolio for an investor are possible. In the multiattribute utility theory (MAUT) approach a utility function is constructed based on the investor's preferences and an optimization problem is solved to find a portfolio that maximizes the utility function. In the multiobjective programming (MOP) approach a set of efficient portfolios is computed by optimizing a scalarized objective function. The investor then chooses a portfolio from the efficient set according to his/her preferences. We outline these two approaches using the UTADIS method to construct a utility function and present numerical results for an example

KW - Portfolio optimization

KW - multiobjective programming

KW - multiattribute utility function

KW - UTADIS

U2 - 10.3138/infor.47.1.31

DO - 10.3138/infor.47.1.31

M3 - Journal article

VL - 47

SP - 31

EP - 42

JO - Information Systems and Operational Research

JF - Information Systems and Operational Research

SN - 0315-5986

IS - 1

ER -