A Meta-heuristic Algorithm for Portfolio Selection Problem under Cardinality and Bounding Constraints

The focus of this paper is on standard Markowitz mean–variance model and its traditional approach to solve portfolio selection problem (Quadratic Planning). For this goal we have applied a meta-heuristic method based on genetic algorithms (GA) in order to trace out the efficient frontier associated...

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Main Authors: mohammad taghi Taqavi Fard, taha Mansouri, mohsen Khosh Tinat
Format: Article
Language:fas
Published: Tarbiat Modares University 2008-01-01
Series:پژوهشهای اقتصادی
Subjects:
Online Access:http://ecor.modares.ac.ir/article-18-273-en.pdf
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author mohammad taghi Taqavi Fard
taha Mansouri
mohsen Khosh Tinat
author_facet mohammad taghi Taqavi Fard
taha Mansouri
mohsen Khosh Tinat
author_sort mohammad taghi Taqavi Fard
collection DOAJ
description The focus of this paper is on standard Markowitz mean–variance model and its traditional approach to solve portfolio selection problem (Quadratic Planning). For this goal we have applied a meta-heuristic method based on genetic algorithms (GA) in order to trace out the efficient frontier associated with the portfolio selection problem under cardinality and bounding constraints. These constraints ensure the investment in a given number of different assets and limit the amount of capital to be invested in each asset. We have presented some experimental results in two samples from Iranian stock market and overseas ones and compare the GA result with unconstrained quadratic results. Finally, we have found out which proposed GA can optimize portfolio selection problem under cardinality and bounded constrains
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spelling doaj.art-69406c82219d42ce8d56819c05ac3dc72023-06-15T20:31:37ZfasTarbiat Modares Universityپژوهشهای اقتصادی1735-67682980-78322008-01-01744969A Meta-heuristic Algorithm for Portfolio Selection Problem under Cardinality and Bounding Constraintsmohammad taghi Taqavi Fard0taha Mansouri1mohsen Khosh Tinat2 allame university allame univarsity allame university The focus of this paper is on standard Markowitz mean–variance model and its traditional approach to solve portfolio selection problem (Quadratic Planning). For this goal we have applied a meta-heuristic method based on genetic algorithms (GA) in order to trace out the efficient frontier associated with the portfolio selection problem under cardinality and bounding constraints. These constraints ensure the investment in a given number of different assets and limit the amount of capital to be invested in each asset. We have presented some experimental results in two samples from Iranian stock market and overseas ones and compare the GA result with unconstrained quadratic results. Finally, we have found out which proposed GA can optimize portfolio selection problem under cardinality and bounded constrainshttp://ecor.modares.ac.ir/article-18-273-en.pdfmarkowitz mean–variance modelefficient frontierquadratic planningcardinality constrainsgenetic algorithm
spellingShingle mohammad taghi Taqavi Fard
taha Mansouri
mohsen Khosh Tinat
A Meta-heuristic Algorithm for Portfolio Selection Problem under Cardinality and Bounding Constraints
پژوهشهای اقتصادی
markowitz mean–variance model
efficient frontier
quadratic planning
cardinality constrains
genetic algorithm
title A Meta-heuristic Algorithm for Portfolio Selection Problem under Cardinality and Bounding Constraints
title_full A Meta-heuristic Algorithm for Portfolio Selection Problem under Cardinality and Bounding Constraints
title_fullStr A Meta-heuristic Algorithm for Portfolio Selection Problem under Cardinality and Bounding Constraints
title_full_unstemmed A Meta-heuristic Algorithm for Portfolio Selection Problem under Cardinality and Bounding Constraints
title_short A Meta-heuristic Algorithm for Portfolio Selection Problem under Cardinality and Bounding Constraints
title_sort meta heuristic algorithm for portfolio selection problem under cardinality and bounding constraints
topic markowitz mean–variance model
efficient frontier
quadratic planning
cardinality constrains
genetic algorithm
url http://ecor.modares.ac.ir/article-18-273-en.pdf
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