Portfolio Selection Strategies in Bursa Malaysia Based on Quadratic Programming
Article Sidebar
Main Article Content
Abstract
The study aims to select the efficient portfolio on stock listed in Bursa Malaysia by using the quadratic programming method. It can help the investors to gain expected returns from the diversification portfolio. However, there are some problems that should be considered such as the measurement of inputs for Mean-Variance Models (MVM), use of portfolio models through time and consistency with management objectives in the portfolio. These problems will affect the performance of selected portfolio and cause the loss problem. Therefore, this study implements a quadratic programming approach to select an efficient portfolio on stocks listed in Bursa Malaysia. The study will choose 15 potential companies which have the best performance in the Bursa Malaysia. Quadratic programming (QP) model can solve any type of mathematical optimisation problem in the study. Therefore, investors can optimise the investment portfolio returns by using QP methods. However, we can observe the efficient frontier which is a graph that representing a list of portfolios that optimising expected return for a different level of portfolio risk so can help the investors make a good decision. The findings of this study will give important inputs, especially to the investors to maximise their portfolio return at different level of risks.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
M. Kacperczyk, S. Van Nieuwerburgh, and L. Veldkamp, “A rational theory of mutual funds’ attention allocation,” Econometrica, vol. 84, no. 2, pp. 571–626, 2016.
S. M. Gasser, M. Rammerstorfer, and K. Weinmayer, “Markowitz revisited: Social portfolio engineering,” Eur. J. Oper. Res., vol. 258, no. 3, pp. 1181–1190, 2017.
A. E. Baum, N. Crosby, and S. Devaney, Property investment appraisal. John Wiley & Sons, 2021.
J.-S. Pang, “A New and Efficient Algorithm for a Class of Portfolio Selection Problems,” Oper. Res., vol. 28, no. 3, pp. 754–767, Jul. 1980, [Online]. Available: http://www.jstor.org/stable/170040
G. Pye, “Portfolio Selection and Security Prices,” Rev. Econ. Stat., vol. 49, no. 1, pp. 111–115, Jul. 1967, doi: 10.2307/1937889.
A. S. Courakis, “Modelling Portfolio Selection,” Econ. J., vol. 98, no. 392, pp. 619–642, Jul. 1988, doi: 10.2307/2233906.
S. Perrin and T. Roncalli, “Machine learning optimization algorithms & portfolio allocation,” Mach. Learn. Asset Manag. New Dev. Financ. Appl., pp. 261–328, 2020.
H. A. Rothan and S. N. Byrareddy, “The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak,” J. Autoimmun., vol. 109, p. 102433, 2020.
J. Beckert, Imagined futures: Fictional expectations and capitalist dynamics. Harvard University Press, 2016.
C. Elleby, I. P. Domínguez, M. Adenauer, and G. Genovese, “Impacts of the COVID-19 pandemic on the global agricultural markets,” Environ. Resour. Econ., vol. 76, no. 4, pp. 1067–1079, 2020.
H. M. Markowitz, “Portfolio theory,” Pers. Financ. An Encycl. Mod. Money Manag. An Encycl. Mod. Money Manag., vol. 321, 2015.
J. STACHO, “Introduction to operations research,” 2021.
W. Niu, Z. Feng, and C. Cheng, “Optimization of variable-head hydropower system operation considering power shortage aspect with quadratic programming and successive approximation,” Energy, vol. 143, pp. 1020–1028, 2018.
W. Xu, Y. Jiang, B. Svetozarevic, and C. Jones, “Constrained efficient global optimization of expensive black-box functions,” in International Conference on Machine Learning, 2023, pp. 38485–38498.
A. M. Schweidtmann and A. Mitsos, “Deterministic global optimization with artificial neural networks embedded,” J. Optim. Theory Appl., vol. 180, no. 3, pp. 925–948, 2019.
D. M. Himmelblau, Applied nonlinear programming. McGraw-Hill, 2018.
S. Gu, T. Hao, and H. Yao, “A pointer network based deep learning algorithm for unconstrained binary quadratic programming problem,” Neurocomputing, vol. 390, pp. 1–11, 2020.
A. Chaweewanchon and R. Chaysiri, “Markowitz Mean-Variance Portfolio Optimization with Predictive Stock Selection Using Machine Learning,” Int. J. Financ. Stud., vol. 10, no. 3, p. 64, 2022.
R. Chetty, L. Sándor, and A. Szeidl, “The effect of housing on portfolio choice,” J. Finance, vol. 72, no. 3, pp. 1171–1212, 2017.
H. A. Hamad, K. S. Qader, B. Gardi, P. A. Hamza, and G. Anwar, “The essential variables to consider before investing in financial markets during Covid-19,” Int. J. Electr. Electron. Comput., vol. 6, no. 5, 2021.
S. D. Hodges and R. A. Brealey, “Portfolio Selection in a Dynamic and Uncertain World,” Financ. Anal. J., vol. 29, no. 2, pp. 50–65, Jul. 1973, [Online]. Available: http://www.jstor.org/stable/4529570
A. Mafusalov and S. Uryasev, “Buffered probability of exceedance: mathematical properties and optimization,” SIAM J. Optim., vol. 28, no. 2, pp. 1077–1103, 2018.
B. Fernando, E. Gavves, J. Oramas, A. Ghodrati, and T. Tuytelaars, “Rank pooling for action recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 773–787, 2016.
R. Korn*, “Optimal portfolios with a positive lower bound on final wealth,” Quant. Financ., vol. 5, no. 3, pp. 315–321, 2005.
M. Saunders, P. Lewis, and A. Thornhill, “Research methods,” Bus. Students 4th Ed. Pearson Educ. Limited, Engl., vol. 6, no. 3, pp. 1–268, 2007.
S. Lewis, “Qualitative inquiry and research design: Choosing among five approaches,” Health Promot. Pract., vol. 16, no. 4, pp. 473–475, 2015.