International Journal of Finance & Managerial Accounting

International Journal of Finance & Managerial Accounting

Development of Markowitz Portfolio Optimization Model Considering Time Factor and Skewness and kurtosis of Returns

Document Type : Original Article

Authors
1 Department of Accounting and Finance, ST.C, Islamic Azad University, Tehran, Iran.
2 Department of Accounting and Finance, ST.C, Islamic Azad University, Tehran, Iran
10.22034/ijfma.2025.78915.2329
Abstract
The investment portfolio optimization problem is a topic that has always been of interest to financial researchers. The aim of this research is to develop the two-dimensional Markowitz portfolio optimization model into a five-dimensional model considering the mean, variance, skewness, kurtosis, and time factor, and then identify the optimal portfolio for investment. In this regard, the return of each share was identified using daily stock price information. Then, the average return, return variance, skewness, and kurtosis of the stock returns of the companies under study were identified in the short-term, medium-term, and long-term time periods, and then the optimal portfolio was identified based on the calculated values and the efficient utility function. To test the model, five main industries of the Tehran Stock Exchange were used, including chemical industries, petroleum products and coke, basic metals, cement, lime and gypsum, and pharmaceuticals. The most profitable company from each industry was selected, including Persian Gulf Petrochemical Industries with the trading symbol Fars, Isfahan Oil Refining with the trading symbol Shapna, Isfahan Mobarakeh Steel Company with the trading symbol Foolad, Tehran Cement Company with the trading symbol Setran, and Pars Daru Company with the trading symbol Depars. The results indicate that the proposed model is able to identify optimal portfolios in different time periods and investors choose a portfolio consisting of Foolad and Setran stocks to obtain maximum utility in the short term. In the medium term, Foolad and Setran stocks and invest in Fars and Foolad stocks in the long term.
Keywords

1.      Aghamohammadi, A., Dadashi Arani, H., & Ghorbani Naderabadi, H. (2017). Skewness-Mean-Variance Test in Selecting Optimal Portfolio and Normal Skew Distribution (Master’s thesis). University of Zanjan, Iran.
2.      Al Ali, S. A., & Bakhtiar Dehkordi, M. (2022). Comparison of Support Vector Machine and Adaptive Neuro-Fuzzy Inference System in Predicting Stock Price Trends of Companies Listed on Tehran Stock Exchange (Master’s thesis). Islamic Azad University, Shahrekord, Iran.
3.      Bajlan, S., & Fallahpour, S. (2017). Stock price trend forecasting using modified support vector machine with hybrid feature selection. Financial Management Perspective, 7(17), 69–86.
4.      Becker, G. S. (1965). A theory of the allocation of time. Journal of Political Economy, 75, 493–517.
5.      Chen, B., Zhong, J., & Chen, Y. (2020). A hybrid approach for portfolio selection with higher order moments: Empirical evidence from Shanghai Stock Exchange. Expert Systems with Applications, 145, 1–26.
6.      Di Pierro, M., & Mosevic, J. (2008). Portfolio rankings with skewness and kurtosis. WIT Transactions on Information and Communication Technologies, 41. WIT Press.
7.      Díaz, A., Esparcia, C., & López, R. (2022). The diversifying role of socially responsible investments during the COVID-19 crisis: A risk management and portfolio performance analysis. Economic Analysis and Policy, 75, 39–60.
8.      Fahmy, H. (2019). Mean-variance-time: An extension of Markowitz’s mean-variance portfolio theory. Journal of Economics and Business, 109, Article 105888. https://ssrn.com/abstract=3961458
9.      Goncalves, G., Wanke, P., & Tan, Y. (2022). A higher order portfolio optimization model incorporating information entropy. Intelligent Systems with Applications, 15, 200101. https://doi.org/10.1016/j.iswa.2022.200101
10.  Gossen, H. H. (1854). Entwickelung der Gesetze des Menschlichen Verkehrs and der daraus áieflenden Regeln für menschliches Handeln. Vieweg und Sohn.
11.  Gotoh, J., Kim, M. J., & Lim, A. E. B. (2018). Robust empirical optimization is almost the same as mean–variance optimization. Operations Research Letters, 46(4), 448–452.
12.  Gubu, L. A., Rashif Hilmi, M. (2024). Beyond mean-variance Markowitz portfolio selection: A comparison of mean-variance-skewness-kurtosis model and robust mean-variance model. Economic Computation and Economic Cybernetics Studies and Research, 58(1). https://doi.org/10.24818/18423264/58.1.24.19
13.  Khan, K. I., Waqar, S. M., Naqvi, A., & Ghafoor, M. M. (2020). Sustainable portfolio optimization with higher-order moments of risk. Sustainability, 12(5), 1–14.
14.  Konno, H., & Suzuki, K.-i. (1995). A mean-variance skewness portfolio optimization model. Journal of the Operations Research Society of Japan, 38(2), 173–187. https://doi.org/10.15807/jorsj.38.173
15.  Lai, K. K., Yu, L., & Wang, S. (2006). Mean-variance-skewness-kurtosis-based portfolio optimization. In First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS’06), 2, 292–297.
16.  Lu, X., Liu, Q., & Xue, F. (2019). Unique closed-form solutions of portfolio selection subject to mean-skewness-normalization constraints. Operations Research Perspectives, 6, 1–15.
17.  Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7, 77–91.
18.  Marques, J. M. E., & Benasciutti, D. (2020). More on variance of fatigue damage in non-Gaussian random loadings—Effect of skewness and kurtosis. Procedia Structural Integrity, 25(1), 101–111.
19.  Metaxiotis, K. (2019). A mean-variance-skewness portfolio optimization model. International Journal of Computer and Information Engineering, 13(2), 85–88.
20.  Mohammadi, O., Mohammadi, M., & Sajadi, S. J. (2020). Portfolio optimization considering higher-order moments, semi-moments, and entropy (Master’s thesis). Iran University of Science and Technology.
21.  Nasrollahi, H., & Hadadi, M. R. (2025). Comparing the efficiency of option pricing models under non-normal jumps, skewness, and kurtosis. Journal of Asset Management and Finance, 13(4), 3–56.
22.  Naqvi, B., Mirza, N., Naqvi, W. A., & Rizvi, S. K. A. (2017). Portfolio optimisation with higher moments of risk at the Pakistan Stock Exchange. Economic Research-Ekonomska Istrazivanja, 30(1), 1594–1610.
23.  Neumann, J. v., & Morgenstern, O. (1947). Theory of games and economic behavior (3rd ed.). Princeton University Press.
24.  Pierdzioch, C., & Christoph, J. (2012). Forecasting stock prices: Do forecasters herd? Economics Letters, 116(3), 326–329.
25.  Rezaii, H., Gol Arzi, G., & Karimi, O. (2024). Comparison of optimal portfolio performance based on normal skew distribution and Laplace-normal skew distribution using mean-absolute deviation-entropy approach. Industrial Management Quarterly, 16(2), 192–214.
26.  Shokrkhah, J., Bolou, G., & Haghighat, M. (2017). The effect of higher-order moments and unsystematic volatility on future stock returns using Fama–MacBeth model. Quarterly Journal of Empirical Accounting Studies, 14(56), 1–83.
27.  Xidonas, P., Mavrotas, G., Krintas, T., Psarras, J., & Zopounidis, C. (2012). Multicriteria portfolio management. In Multicriteria Portfolio Management (pp. 5–21). Springer.