Mean-Variance Portfolio Optimization on Islamic Stocks by Using Non Constant Mean and Volatility Models and Genetic Algorithm

  • Authors

    • Endang Soeryana Hasbullah
    • Nurfadhlina bt Abdul Halim
    • Sukono .
    • Adam Sukma Putra
    • Abdul Talib Bon
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.20.19274
  • 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 cons

  • Abstract

    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.

     

  • References

    1. [1] El hachlou, M., Guennoun, Z. and Hamza, F. 2012. Stocks Portfolio Optimization Using Classification and Genetic Algorithms. Applied Mathematical Sciences, Vol. 6, 2012, no. 94, pp. 4673 – 4684.

      [2] Febrian, E. & Herwany, A. (2009). Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets. Working Paper in Economics and Development Studies. Department of Ekonomics, Padjadjaran University.

      [3] Kapiamba, J.N., Ulungu, B.E.L., and Mubenga, P.K. 2015. Simulated Annealing vs Genetic Algorithm to Portfolio Selection. International Journal of Scientific and Innovative Mathematical Research (IJSIMR), Volume 3, Issue 5, May 2015, PP 18-30.

      [4] ISSN 2347-307X (Print) & ISSN 2347-3142 (Online). http://www.arcjournals.org.

      [5] Goto, S. & Yan Xu. (2012). On Mean Variance Portfolio Optimization: Improving Performance Through Better Use of Hedging Relations. Working Paper. Moore School of Business, University of South Carolina. email: shingo.goto@moore.sc.edu.

      [6] Gujarati, D.N. (2004). Basic Econometrics. Fourth Edition. The McGraw−Hill Companies, Arizona.

      [7] Kheirollah, A. & Bjarnbo, O., (2007). A Quantitative Risk Optimization of Markowitz Model: An Empirical Investigation on Swedish Large Cap List, Master Thesis, in Mathematics/Applied Mathematics, University Sweden, Department of Mathematics and Physics, www.mdh.se/polopoly_fs/ 1.16205!MasterTheses.pdf.

      [8] Lin, C.C. and Liu, Y.T. 2008. Genetic Algorithms for Portfolio Selection Problems with Minimum Transaction Lots. European Journal of Operational Research 185 (2008), pp. 393–404.

      [9] Panjer, H.H. Ed., et al. (1998). Financial Economics: With Applicationsto Investments, Insurance, and Pensions. Schaumburg, Ill.: The Actuarial Foundation.

      [10] Rifqiawan, R.A. (2008). Analisis Perbedaan Volume Perdagangan Saham-Saham yang Optimal Pada Jakarta Islamic Index (JII) di Bursa Efek Indonesia (BEI). Tesis Program Magister. Program Stdi Magister Sains Akuntansi, Program Pascasarjana, Universitas Diponegoro, Semarang, 2008.

      [11] Shewhart, Walter A and Samuel S. Wilks. (2004). Applied Econometric Time Series. John Wiley &Sons, Inc. United States of America.

      [12] Shi-Jie Deng. (2004). Heavy-Tailed GARCH models: Pricing and Risk Management Applications in Power Market, IMA Control & Pricing in Communication & Power Networks. 7-17 Mar http://www.ima.umn.edu/talks/.../deng/power_ workshop_ ima032004-deng.pdf.

      [13] Sukono, Subanar & Dedi Rosadi. (2011). Pengukuran VaR Dengan Volatilitas Tak Konstan dan Efek Long Memory. Disertasi. Prtogram Studi S3 Statistika, Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Gajah Mada, Yogyakarta, 2011.

      [14] Tsay, R.S. (2005). Analysis of Financial Time Serie, Second Edition, USA: John Wiley & Sons, Inc.

      [15] Yoshimoto, A. (1996). The Mean-Variance Approach To Portfolio Optimization Subject To Transaction Costs. Journal of the Operations Research Society of Japan, Vol. 39, No. 1, March 1996

      [16] Zhang, D., (2006). Portfolio Optimization with Liquidity Impact, Working Paper, Center for Computational Finance and Economic Agents, University of Essex, www.orfe.princeton.edu/ oxfordprinceton5/slides/yu.pdf.

  • Downloads

  • How to Cite

    Soeryana Hasbullah, E., bt Abdul Halim, N., ., S., Sukma Putra, A., & Talib Bon, A. (2018). Mean-Variance Portfolio Optimization on Islamic Stocks by Using Non Constant Mean and Volatility Models and Genetic Algorithm. International Journal of Engineering & Technology, 7(3.20), 366-371. https://doi.org/10.14419/ijet.v7i3.20.19274

    Received date: 2018-09-08

    Accepted date: 2018-09-08

    Published date: 2018-09-01