An enhanced GRASP approach for the index tracking problem

Index tracking models build portfolios of limited size that replicate the performance of a market index. As the size of the index grows, it becomes impractical to find an optimal solution. As far as we know, this work proposes the first greedy randomized adaptive search procedure (GRASP) approach fo...

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Bibliographic Details
Published inInternational transactions in operational research Vol. 31; no. 3; pp. 1828 - 1858
Main Authors Silva, Julio Cezar Soares, Silva, Diogo Ferreira de Lima, de Almeida Filho, Adiel Teixeira
Format Journal Article
LanguageEnglish
Published Oxford Blackwell Publishing Ltd 01.05.2024
Subjects
Adaptive search techniques
Combinatorial analysis
genetic algorithm
Genetic algorithms
GRASP
Heuristic
index tracking
Operations research
Optimization
Parameterization
portfolio optimization
Tracking problem
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Summary:Index tracking models build portfolios of limited size that replicate the performance of a market index. As the size of the index grows, it becomes impractical to find an optimal solution. As far as we know, this work proposes the first greedy randomized adaptive search procedure (GRASP) approach for index tracking. GRASP has proven to be efficient in combinatorial optimization problems and offers a solution construction procedure different from the standard index tracking optimization approaches, bringing a new perspective to the field. Results showed that the proposed GRASP approach found solutions with almost the same quality as those found by CPLEX solver in a smaller time, and the proposed local search component was competitive depending on the problem parametrization. The results found have practical implications concerning the achievement of good solutions by GRASP approaches in a smaller time when compared with hybrid genetic algorithms, and new perspectives for building GRASP heuristics for portfolio optimization problems.
ISSN:0969-6016
1475-3995
DOI:10.1111/itor.13163