Portfolio optimization using the semi-variance model with a focus on positive potential (Case study: Tehran Stock Exchange)

Document Type : Original Article

Authors

1 Master student, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

2 Ph.D. Student, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

3 Associate Professor, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

Abstract

Investing in the stock market plays a fundamental role in economic growth and development by providing companies and governments with investment opportunities, funding and stimulating economic activity. Putting together a suitable investment portfolio for stock market activities is of utmost importance and requires skill and the ability to optimally combine different stocks to achieve desirable performance and better returns. Paying attention to market fluctuations when constructing an investment portfolio is crucial as they indicate changes and dynamics in the market. Investment decisions made based on these fluctuations help investors reduce their financial risks and identify investment opportunities. In addition, attention to positive volatility is important in portfolio construction as these fluctuations indicate the growth potential and profitability of stocks. The aim of this research is to construct an optimal investment portfolio to take advantage of the benefits and investment opportunities arising from positive volatility, while considering negative volatility and its reduction. To achieve this, a novel approach, the so-called two-stage semi-variance approach, is introduced. Using monthly stock data from the beginning of March 2018 to March 2023, the stock portfolio is constructed, and the efficiency of this model is evaluated by comparing it with the semi-variance model and the equally weighted portfolio. The results show that the two-stage semi-variance approach outperforms the semi-variance model and the equally weighted portfolio. This indicates that this approach can significantly improve the efficiency and performance of investment portfolios compared to traditional methods.

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References

Bramante, R., & Facchinetti, S. (2021). Combining Upside and Downside Volatility in Investment Decision. Journal of Mathematical Finance, 12(1), 97–104.
Comeig, I., Holt, C., & Jaramillo-Gutiérrez, A. (2022). Upside versus downside risk: Gender, stakes, and skewness. Journal of Economic Behavior & Organization, 200(1), 21–30.
Cumova, D., & Nawrocki, D. (2014). Portfolio optimization in an upside potential and downside risk framework. Journal of Economics and Business, 71, 68–89.
Eskorouchi, A., Ghanbari, H., & Mohammadi, E. (2023). A Scientometric Analysis of Robust Portfolio Optimization. Iranian Journal of Accounting, Auditing and Finance, 8, 59–74.
Gambrah, P. S. N., & Pirvu, T. A. (2014). Risk Measures and Portfolio Optimization. Journal of Risk and Financial Management, 7(3), 113–129.
Ghanbari, H., Safari, M., Ghousi, R., Mohammadi, E., & Nakharutai, N. (2023). Bibliometric analysis of risk measures for portfolio optimization. Accounting, 9, 95–108.
Gunjan, A., & Bhattacharyya, S. (2023). A brief review of portfolio optimization techniques. Artificial Intelligence Review, 56(5), 1–40.
Hashemi, A., & Chavoshi, S. K. (2023). The Impact of Risk Tolerance, Capital Adequacy, Corporate Governance on the Performance of Tse’s Banks. Scientific Journal of Budget and Finance Strategic Research, 4(4), 91–116. [In Persian].
Kalayci, C. B., Ertenlice, O., & Akbay, M. A. (2019). A comprehensive review of deterministic models and applications for mean-variance portfolio optimization. Expert Systems with Applications, 125, 345–368.
Kaucic, M., & Daris, R. (2017). Interval-valued upside potential and downside risk portfolio optimisation. Economic Research-Ekonomska Istraživanja, 30(1), 1–21.
Mirabbasi, Y., Nikoumaram, H., Saeidi, A., & Haghshenas, F. (2018). Study of portfolio optimization based on downside risk, upside potential and behavioral variables efficiency. Financial Engineering and Portfolio Management, 9(34), 305–333. [In Persian].
Mousavi Loleti, S. A., Mohammadi, E., & Shavvalpour, S. (2023). Forecasting Future Trends of the Stock Market Using the Probit Regression Approach with Emphasis on Value at Risk. Journal of Capital Market Analysis, 4(1), 79–109. [In Persian].
Mousavi Loleti, S. A., Mohammadi, E., & Shavvalpour, S. (2023). Forecasting the status of the total index of the Tehran Stock Exchange using macroeconomic variables. The Ninth International Conference on Industrial and Systems Engineering. [In Persian].
Nourahmadi, M., Rasti, F., & Sadeqi, H. (2023). The Art of Investment Portfolio Curation through Centrality Metrics (An Enchanting Network Analysis of Tehran Stock Exchange’s Top 50 Companies). Scientific Journal of Budget and Finance Strategic Research, 4(4), 35–61. [In Persian].
Rom, B. M., & Ferguson, K. W. (1993). Post-Modern Portfolio Theory Comes of Age. The Journal of Investing, 2(4), 27–33.
Sadeghi, M., soroosh, A., & Farhanian, M. J. (2010). Investigating the volatility, upside risk, downside risk and Capital Asset Pricing Model: Evidences from Tehran Stock Exchange. Financial Research Journal, 12(29), 1–20. [In Persian].
Sadeghi Shahedani, M. (2020). Modeling the At risk Values Optimal Pattern in Pension Funds. Scientific Journal of Budget and Finance Strategic Research, 1(2), 11–31. [In Persian].
Sortino, F., van der Meer, R. A. H., & Plantinga, A. (1999). The Dutch triangle—A framework to measure upside potential relative to downside risk. Journal of Portfolio Management, 26(1), 50–58.
Zanjirdar, M. (2020). Overview of Portfolio Optimization Models. Advances in Mathematical Finance and Applications, 5(4), 419–435.

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History

  • Receive Date: 31 December 2023
  • Revise Date: 28 January 2024
  • Accept Date: 13 April 2024

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