Original Research

Computation of optimal investment allocations in a sequential portfolio optimisation

Masiala Mavungu, Evan Hurwitz, Tshilidzi Marwala
Journal of Economic and Financial Sciences | Vol 12, No 1 | a416 | DOI: https://doi.org/10.4102/jef.v12i1.416 | © 2019 Masiala Mavungu, Evan Hurwitz, Tshilidzi Marwala | This work is licensed under CC Attribution 4.0
Submitted: 07 September 2018 | Published: 26 September 2019

About the author(s)

Masiala Mavungu, Department of Electrical and Electronic Engineering, University of Johannesburg, Johannesburg, South Africa
Evan Hurwitz, Department of Electrical and Electronic Engineering, University of Johannesburg, Johannesburg, South Africa
Tshilidzi Marwala, Department of Electrical and Electronic Engineering, University of Johannesburg, Johannesburg, South Africa


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Abstract

Orientation: This article is related to Financial Risk Management, Investment Management and Portfolio Optimisation.

Research purpose: The aim is to compute optimal investment allocations from one period to another.

Motivation of the study: Financial market systems are governed by random behaviours expressing the complexity of the economy and the politics. Risk Measure and Management are current and major issues for financial market operators and attract the attention of researchers who develop suitable tools and methods to describe and control risk. In this article, financial risk management is considered for an investor operating in the financial market.

Research approach/design and method: This research developed Mathematical Models to describe the problem and Computational Simulations to compute, summarise the results and show their reliabilities.

Main findings: The results are the investments allocations stored, some tables and the related computational simulations. By going from period one to another, one can notice from the graphs that the portfolio risk is decreasing and the portfolio profit increasing.

Practical/managerial implications: The approach used in this article shows a way of solving rigorously any linearly constrained quadratic optimisation problem and any constrained nonlinear problem. It gives the ability of transforming judiciously certain linearly constrained nonlinear programming problems into sequences of linearly constrained quadratic problems and solving them efficiently.

Contributions/value-add: This article developed Mathematical Models and Matlab Computer Optimisation Programs to give Computational Simulations. It wrote Computer Programs for a fifth-order autoregressive model to forecast asset profits.


Keywords

investment allocations; portfolio selection; portfolio optimisation; data forecasting; risk minimisation; profit maximisation; sequential quadratic programming

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