Robust Hybrid Optimization Method to Reduce Investment Portfolio Risk using Fusion of Modern Portfolio Theory and Genetic Algorithm
Nashirah Abu Bakar1, Sofian Rosbi2
1Nashirah Abu Bakar, Islamic Business School, College of Business, Universiti Utara Malaysia, Kedah, Malaysia.
2Sofian Rosbi, Department of Mechatronic Engineering, Universiti Malaysia Perlis, Arau, Malaysia.
Manuscript received on 27 September 2019 | Revised Manuscript received on 09 November 2019 | Manuscript Published on 22 November 2019 | PP: 136-148 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F10230986S319/19©BEIESP | DOI: 10.35940/ijeat.F1023.0986S319
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Abstract: Main objective of this study is to develop hybrid optimization method for reducing investment portfolio risk. The methods selected in this study are the combination of Modern Portfolio Theory (MPT) and genetic algorithm optimization approach. Three stocks from Malaysian Stock Exchange are selected in developing the investment portfolio namely Malayan Banking Berhad, Hap Seng Consolidated Berhad and Top Glove Corporation Berhad. Result indicates the modern portfolio theory can give optimal portfolio weightage with maximum return for tolerate level of investment risk. In addition, genetic algorithm enhanced the optimal searching method to find global minimum of investment risk. Result shows the minimum portfolio risk in objective function is 2.122118 with implementation genetic algorithm optimization. The optimal combination of portfolio investment is 32.24 % in asset A (Malayan Banking Berhad), 52.37 % in asset B (Hap Seng Consolidated Berhad), and 15.30 % in asset C (Top Gove Corporation Berhad). The important of this study is it will assist investor in making better decision to optimize their return for given level of investment risk. Furthermore, this hybrid method provides a better accuracy of prediction for return of investment and portfolio risk.
Keywords: Investment, Portfolio Risk, Modern Portfolio Theory, Genetic Algorithm.
Scope of the Article: Algorithm Engineering