Modelling of multiple constraints portfolio optimization using modified particle swarm optimization

In finance, the portfolio is the set of investment in the assets. Meanwhile, its optimization leads towards the best selection and diversification of investments. Portfolio optimization involves the objectives (mean, variance or Sharp Ratio (SR)) and constraints (budget, short sell, outliers, cardin...

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Main Author:	Zaheer, Kashif
Format:	Thesis
Language:	English
Published:	2020
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/102421/1/KashifZaheerPFS2021.pdf.pdf
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spelling	my-utm-ep.1024212023-08-28T06:29:59Z Modelling of multiple constraints portfolio optimization using modified particle swarm optimization 2020 Zaheer, Kashif QA Mathematics In finance, the portfolio is the set of investment in the assets. Meanwhile, its optimization leads towards the best selection and diversification of investments. Portfolio optimization involves the objectives (mean, variance or Sharp Ratio (SR)) and constraints (budget, short sell, outliers, cardinality, lot and transaction cost, liquidity), as well as some others which makes it more complex, dynamic and intractable. The SR function is considered to be the measuring tool for best portfolio selection and optimization. At present, the area of portfolio optimization lacks in having multiple constraints with the SR as the objective function. This research focuses on a two-stage portfolio selection, diversification, and optimization. The normality tests have been performed from the data considered and it is found that the data is nonlinear and stochastic. The two selection criterion (mean and variance) have been introduced in this research. Furthermore, several constraints have been considered for the problem of Multiple Constraints Portfolio Optimization (MCPO). A metaheuristic technique needs to be developed with the financial toolbox inMATLAB and the Particle SwarmOptimization (PSO) for portfolio construction, diversification, and optimization, namely, the Modified PSO (MPSO). The simulation on the benchmark model for restriction on the short sale was performed. Also, the diversification phenomenon for having the 10, 50 and 150 assets collection has been observed. The obtained results for the benchmark model are 42.51% and 84.20% increment in Maximum of Maximum Sharp Ratio (MMSR), whereas 39.88% and 84.30% increment in Average of Maximum Sharp Ratio (AMSR). The results of the models having mean of return selection criteria have increments of 2.58%, 21.10%, 16.41%, 11.67%, and 6.42%; whereas, models M3 and M4 for MMSR values have decrement of 3.52% in comparison with the model having the variance of return selection criteria. This research will be beneficial for those involved such as in mathematical finance modeling, asset portfolio optimization and financial model optimization using metaheuristic techniques. 2020 Thesis http://eprints.utm.my/id/eprint/102421/ http://eprints.utm.my/id/eprint/102421/1/KashifZaheerPFS2021.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:146035 phd doctoral Universiti Teknologi Malaysia Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Zaheer, Kashif Modelling of multiple constraints portfolio optimization using modified particle swarm optimization
description	In finance, the portfolio is the set of investment in the assets. Meanwhile, its optimization leads towards the best selection and diversification of investments. Portfolio optimization involves the objectives (mean, variance or Sharp Ratio (SR)) and constraints (budget, short sell, outliers, cardinality, lot and transaction cost, liquidity), as well as some others which makes it more complex, dynamic and intractable. The SR function is considered to be the measuring tool for best portfolio selection and optimization. At present, the area of portfolio optimization lacks in having multiple constraints with the SR as the objective function. This research focuses on a two-stage portfolio selection, diversification, and optimization. The normality tests have been performed from the data considered and it is found that the data is nonlinear and stochastic. The two selection criterion (mean and variance) have been introduced in this research. Furthermore, several constraints have been considered for the problem of Multiple Constraints Portfolio Optimization (MCPO). A metaheuristic technique needs to be developed with the financial toolbox inMATLAB and the Particle SwarmOptimization (PSO) for portfolio construction, diversification, and optimization, namely, the Modified PSO (MPSO). The simulation on the benchmark model for restriction on the short sale was performed. Also, the diversification phenomenon for having the 10, 50 and 150 assets collection has been observed. The obtained results for the benchmark model are 42.51% and 84.20% increment in Maximum of Maximum Sharp Ratio (MMSR), whereas 39.88% and 84.30% increment in Average of Maximum Sharp Ratio (AMSR). The results of the models having mean of return selection criteria have increments of 2.58%, 21.10%, 16.41%, 11.67%, and 6.42%; whereas, models M3 and M4 for MMSR values have decrement of 3.52% in comparison with the model having the variance of return selection criteria. This research will be beneficial for those involved such as in mathematical finance modeling, asset portfolio optimization and financial model optimization using metaheuristic techniques.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Zaheer, Kashif
author_facet	Zaheer, Kashif
author_sort	Zaheer, Kashif
title	Modelling of multiple constraints portfolio optimization using modified particle swarm optimization
title_short	Modelling of multiple constraints portfolio optimization using modified particle swarm optimization
title_full	Modelling of multiple constraints portfolio optimization using modified particle swarm optimization
title_fullStr	Modelling of multiple constraints portfolio optimization using modified particle swarm optimization
title_full_unstemmed	Modelling of multiple constraints portfolio optimization using modified particle swarm optimization
title_sort	modelling of multiple constraints portfolio optimization using modified particle swarm optimization
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Science
publishDate	2020
url	http://eprints.utm.my/id/eprint/102421/1/KashifZaheerPFS2021.pdf.pdf
_version_	1776100918963470336

Modelling of multiple constraints portfolio optimization using modified particle swarm optimization

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