An Empirical Investigation of Portfolio Optimisation Using the Markowitz Model
Abstract
In finance, portfolio optimisation involves an essential concept that requires determining the ideal combination of assets to optimise returns by lowering the return risk. The concept of efficient portfolios, which aims to attain the maximum return for a given level of risk or the minimum risk for a given level of return, was initially suggested by Markowitz's model. Considering an emphasis on the Shanghai Stock Exchange (SSE), this research explores portfolio optimisation using Markowitz's Portfolio Theory about the Chinese stock market. The objective is to identify the optimal stock portfolio from a selection of various companies listed on the SSE for the 2020-2024 periods, balancing risk and expected return. A purposive sampling method is used to select various stocks based on their historical performance, with stocks screened through a two-level process: first by correlation coefficients, and by their coefficient of variation to assess risk-return trade-offs. Weekly return rates of selected stocks from the SSE over four years are used for the analysis. Using the mean-variance optimisation approach, the ideal weights for each stock in the portfolio are determined using the expected return effect. The results show that the optimized portfolio, consisting of various stocks (Industrial and Commercial Bank of China (ICBC), GD Power Development Co., Ltd, Beiqi Foton Motor Co., Ltd., Shanghai Automotive Industry Corporation (SAIC Motor), China Life Insurance Company (LIC)), performs more effectively with the return in trading days. The portfolio includes companies with diversified sectors, ensuring a balanced risk and return profile.
Keywords
References
M. Dhaini and N. Mansour, “Squirrel search algorithm for portfolio optimization,” Expert Syst. Appl., vol. 178, p. 114968, 2021.
A. Naccarato, A. Pierini, and G. Ferraro, “Markowitz portfolio optimization through pairs trading cointegrated strategy in long-term investment,” Ann. Oper. Res., vol. 299, no. 1, pp. 81–99, 2021.
Q. Zaman, S. Safwandi, and F. Fajriana, “Supporting Application Fast Learning of Kitab Kuning for Santri’ Ula Using Natural Language Processing Methods,” Int. J. Eng. Sci. Inf. Technol., vol. 5, no. 1, pp. 278–289, Jan. 2025, doi: 10.52088/ijesty.v5i1.713.
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.
G. Meisel and M. S. Sinaga, “Optimization of the Technology Sector Stock Portfolio during the Covid-19 Pandemic Using the Markowitz Model,” Asian J. Community Serv., vol. 1, no. 4, pp. 155–168, 2022.
I. G. Dharma Utamayasa, “Efect Physical Activity and Nutrition During The Covid-19 Pandemic,” Int. J. Eng. Sci. Inf. Technol., vol. 1, no. 1, 2021, doi: 10.52088/ijesty.v1i1.58.
Y. Berouaga, C. El Msiyah, and J. Madkour, “Portfolio optimization using minimum spanning tree model in the Moroccan stock exchange market,” Int. J. Financ. Stud., vol. 11, no. 2, p. 53, 2023.
D. Abdullah, S. Susilo, A. S. Ahmar, R. Rusli, and R. Hidayat, “The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data,” Qual. Quant., 2021, doi: 10.1007/s11135-021-01176-w.
T. Petropoulos, K. Liapis, and E. Thalassinos, “Optimal structure of real estate portfolio using EVA: a stochastic markowitz model using data from greek real estate market,” Risks, vol. 11, no. 2, p. 43, 2023.
F. Anuno, M. Madaleno, and E. Vieira, “Testing of portfolio optimization by Timor-Leste portfolio investment strategy on the stock market,” J. Risk Financ. Manag., vol. 17, no. 2, p. 78, 2024.
A. Sengupta, P. Jana, P. N. Dutta, and I. Mukherjee, “Optimal stock allocation for an automated portfolio recommender system in the perspective of maximum fund utilization,” Expert Syst. Appl., vol. 242, p. 122857, 2024.
B. Alidaee, H. Wang, and W. Wang, “Comparative Study of Portfolio Optimization Models for Cryptocurrency and Stock Markets,” IEEE Access, 2025.
S. Oktarian, S. Defit, and Sumijan, “Clustering Students’ Interest Determination in School Selection Using the K-Means Clustering Algorithm Method,” J. Inf. dan Teknol., vol. 2, pp. 68–75, 2020, doi: 10.37034/jidt.v2i3.65.
R. A. Busari, D. A. Awotundun, and A. D. Anifowose, “Stock portfolio and stock return in Nigeria capital market.,” J. Manag. Econ. & Ind. Organ., vol. 9, no. 1, 2025.
O. V la Torre-Torres, E. Galeana-Figueroa, and J. Álvarez-Garc’ia, “A markov-switching VSTOXX trading algorithm for enhancing EUR stock portfolio performance,” Mathematics, vol. 9, no. 9, p. 1030, 2021.
J. Mazanec, “Portfolio optimalization on digital currency market,” J. Risk Financ. Manag., vol. 14, no. 4, p. 160, 2021.
I. Yaman and T. E. Dalkiliç, “A hybrid approach to cardinality constraint portfolio selection problem based on nonlinear neural network and genetic algorithm,” Expert Syst. Appl., vol. 169, p. 114517, 2021.
N. B. Mazraeh, A. Daneshvar, M. Madanchi zaj, and F. R. Roodposhti, “Stock portfolio optimization using a combined approach of multi objective grey wolf optimizer and machine learning preselection methods,” Comput. Intell. Neurosci., vol. 2022, no. 1, p. 5974842, 2022.
J. C. Mba, K. A. Ababio, and S. K. Agyei, “Markowitz mean-variance portfolio selection and optimization under a behavioral spectacle: New empirical evidence,” Int. J. Financ. Stud., vol. 10, no. 2, p. 28, 2022.
M. Rasoulzadeh, S. A. Edalatpanah, M. Fallah, and S. E. Najafi, “A multi-objective approach based on Markowitz and DEA cross-efficiency models for the intuitionistic fuzzy portfolio selection problem,” Decis. Mak. Appl. Manag. Eng., vol. 5, no. 2, pp. 241–259, 2022.
B. Yilmaz, R. Korn, and A. S. Selcuk-Kestel, “The impact of large investors on the portfolio optimization of single-family houses in housing markets,” Comput. Econ., vol. 61, no. 2, pp. 855–873, 2023.
K. Erwin and A. Engelbrecht, “Meta-heuristics for portfolio optimization,” Soft Comput., vol. 27, no. 24, pp. 19045–19073, 2023.
X. Cao and S. Li, “A novel dynamic neural system for nonconvex portfolio optimization with cardinality restrictions,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 53, no. 11, pp. 6943–6952, 2023.
A. Petukhina, Y. Klochkov, W. K. Härdle, and N. Zhivotovskiy, “Robustifying markowitz,” J. Econom., vol. 239, no. 2, p. 105387, 2024.
DOI: https://doi.org/10.52088/ijesty.v5i4.959
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Yixi Ni



























