Open AccessMean-Variance Portfolio Optimization on Islamic Stocks by Using Non Constant Mean and Volatility Models and Genetic Algorithm
Author(s) -
Endang Soeryana Hasbullah,
Nurfadhlina bt Abdul Halim,
Sukono Sukono,
Adam Sukma Putra,
Abdul Talib Bon
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.20.19274
Subject(s) - portfolio optimization , portfolio , volatility (finance) , econometrics , heteroscedasticity , autoregressive conditional heteroskedasticity , rate of return on a portfolio , post modern portfolio theory , economics , stock market , financial economics , mathematics , replicating portfolio , paleontology , horse , biology
The risk in stock market has taken an sinificant issue in investment of stock market, including Investment in some Islamic stocks. In order to minimize the level of risk, investors usually forming an investment portfolio. Establishment of a portfolio consisting of several Islamic stocks are intended to get the optimal composition of the investment portfolio. This paper discussed about optimizing investment portfolio of Mean-Variance to Islamic stocks by using mean and volatility is not constant approaches. Non constant mean analyzed using models Autoregressive Moving Average (ARMA), while non constant volatility models are analyzed using the Generalized Autoregressive Conditional heteroscedastic (GARCH). Optimization process is performed by using the Lagrangian multiplier technique followed by the Genetic Algorithm (GA). The expected result is to get the proportion of investment in each Islamic stock analyzed. Based on the result, we got that GA give a proportion of portfolio optimum selection with the best expected return. However, The GA has more potential candidate of solution that give the investor an alternative of their optimum portfolio selection. in this paper, we only present the best solution which has the highest fitness to the model.