Quadratic Programming for Optimizing the Diversified Shariah Stock Portfolio

DOI

10.2991/assehr.k.201010.021How to use a DOI?

Keywords

quadratic programming, constant correlation model, diversified shariah stock portfolio

Abstract

Risk is a challenging module in evaluating stock investment prospects that are often taken into account by investors. This paper presents the method of Quadratic Programming to optimize the risk of Shariah stock portfolio. The dataset deals with the weekly close price of all active issuers listed in FTSE Bursa Malaysia Hijrah Shariah Index from January 2016 to December 2018. In general, there are two issues that are highlighted: portfolio selection and portfolio optimization. Portfolio selection is carried out in several phases, namely grouping the issuers into two portfolios by considering the technical and fundamental aspects, nominating feasibility of each portfolio using a Constant Correlation Model, and finally selecting the most diversified Shariah stock portfolio. Furthermore, the selected portfolio risk optimization is formulated by Quadratic Programming. The results of this study show that the optimum portfolio is the portfolio B which includes 42.73% of BTKW, 8.1% of GENP, 13.5% of IHHH, 0.84% of IOIB, 5.04% of PCGB, 15.48% of PEPT, 4.86% of PGAS, and 9.46% of TENA with a minimum value of risk 0.58%. Since the variance of portfolio A is 0.81%, it implies that a maximum diversified Shariah portfolio provides better risk.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Noor Saif Muhammad Mussafi
AU  - Zuhaimy Ismail
PY  - 2020
DA  - 2020/10/11
TI  - Quadratic Programming for Optimizing the Diversified Shariah Stock Portfolio
BT  - Proceedings of the 2nd International Seminar on Science and Technology (ISSTEC 2019)
PB  - Atlantis Press
SP  - 139
EP  - 147
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.201010.021
DO  - 10.2991/assehr.k.201010.021
ID  - Mussafi2020
ER  -