Cardinality constrained portfolio optimization using multi-objective evolutionary algorithms

Constructing an optimal portfolio of assets is a multi-objective optimization process of maximizing return while minimizing risk. Two objectives need to be optimized simultaneously, making it a real world multi-objective optimization problem. Solving such problems is complex and tedious. The clas...

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Main Author: Ashok, Vivek Kumar.
Other Authors: Ponnuthurai Nagaratnam Suganthan
Format: Final Year Project (FYP)
Language:English
Published: 2013
Subjects:
Online Access:http://hdl.handle.net/10356/54299
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author Ashok, Vivek Kumar.
author2 Ponnuthurai Nagaratnam Suganthan
author_facet Ponnuthurai Nagaratnam Suganthan
Ashok, Vivek Kumar.
author_sort Ashok, Vivek Kumar.
collection NTU
description Constructing an optimal portfolio of assets is a multi-objective optimization process of maximizing return while minimizing risk. Two objectives need to be optimized simultaneously, making it a real world multi-objective optimization problem. Solving such problems is complex and tedious. The classical model of portfolio allocation was developed by Henry Markowitz. It talked about diversification being an important tool applied to minimize the riskiness associated with the final portfolio. However, optimization based on such mathematical derivatives is very complex and requires strong mathematical knowledge. Genetic Algorithms have a big advantage in this scenario as they offer robustness, speed and requires little mathematical knowledge. This paper focuses on the use of two popular Multi-objective Evolutionary Algorithms to construct optimal portfolio of stocks, while being subject to a cardinality constraint. Additionally, to improve the performance of the algorithms, different techniques to filter the initial population of stocks are implemented and analyzed.
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spelling ntu-10356/542992023-07-07T16:25:54Z Cardinality constrained portfolio optimization using multi-objective evolutionary algorithms Ashok, Vivek Kumar. Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Constructing an optimal portfolio of assets is a multi-objective optimization process of maximizing return while minimizing risk. Two objectives need to be optimized simultaneously, making it a real world multi-objective optimization problem. Solving such problems is complex and tedious. The classical model of portfolio allocation was developed by Henry Markowitz. It talked about diversification being an important tool applied to minimize the riskiness associated with the final portfolio. However, optimization based on such mathematical derivatives is very complex and requires strong mathematical knowledge. Genetic Algorithms have a big advantage in this scenario as they offer robustness, speed and requires little mathematical knowledge. This paper focuses on the use of two popular Multi-objective Evolutionary Algorithms to construct optimal portfolio of stocks, while being subject to a cardinality constraint. Additionally, to improve the performance of the algorithms, different techniques to filter the initial population of stocks are implemented and analyzed. Bachelor of Engineering 2013-06-18T07:35:47Z 2013-06-18T07:35:47Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/54299 en Nanyang Technological University 82 p. application/pdf
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Ashok, Vivek Kumar.
Cardinality constrained portfolio optimization using multi-objective evolutionary algorithms
title Cardinality constrained portfolio optimization using multi-objective evolutionary algorithms
title_full Cardinality constrained portfolio optimization using multi-objective evolutionary algorithms
title_fullStr Cardinality constrained portfolio optimization using multi-objective evolutionary algorithms
title_full_unstemmed Cardinality constrained portfolio optimization using multi-objective evolutionary algorithms
title_short Cardinality constrained portfolio optimization using multi-objective evolutionary algorithms
title_sort cardinality constrained portfolio optimization using multi objective evolutionary algorithms
topic DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
url http://hdl.handle.net/10356/54299
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