Fuzzy portfolio optimization using conditional drawdown at risk: Empirical evidence on selective companies in the Tehran Stock Exchange

Zarezade, R.; Ghousi, R.; Mohammadi, E.; Ghanbari, H.

Growing Science » Accounting » Fuzzy portfolio optimization using conditional drawdown at risk: Empirical evidence on selective companies in the Tehran Stock Exchange

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ISSN 2369-7407 (Online) - ISSN 2369-7393 (Print) Quarterly Publication Volume 11 Issue 2 pp. 131-144 , 2025

Fuzzy portfolio optimization using conditional drawdown at risk: Empirical evidence on selective companies in the Tehran Stock Exchange Pages 131-144 Download PDF

Authors: Roghaye Zarezade, Rouzbeh Ghousi, Emran Mohammadi, Hossein Ghanbari

DOI: 10.5267/j.ac.2025.2.002

Keywords: Portfolio optimization, Multi-objective programming, Fuzzy sets theory, Conditional Drawdown at Risk

Abstract: This article introduces an innovative fuzzy-based approach for developing a comprehensive portfolio optimization model that effectively accounts for inherent uncertainty while incorporating the investor's unique perspective on the dynamic stock market. The multi-objective optimization framework employs Conditional Drawdown at Risk to enhance investor flexibility in determining risk tolerance and optimal investment strategies tailored to specific needs. The research is notable for its pioneering use of intelligent methods to systematically collect valuable data from the Tehran Stock Exchange under fuzzy uncertainty. It incorporates important constraints such as cardinality and ceiling and floor limits for each investment period, allowing for a detailed analysis of various stock market scenarios and potential future outcomes. A case study is conducted with 25 diverse assets from the top five industry groups based on profit per share, from which five shares are thoughtfully selected to effectively demonstrate the model's unique effectiveness. The analysis rigorously assesses the model's performance in real-world conditions, highlighting the importance of accurately understanding the current stock market outlook and trends. To validate the model, the research compares results with a portfolio constructed under similar conditions of certainty and risk. The findings indicate that portfolios created under certainty yield significantly higher values, suggesting that successful portfolio construction is heavily influenced by the prevailing market conditions experienced by investors.

How to cite this paper

 Zarezade, R., Ghousi, R., Mohammadi, E & Ghanbari, H. (2025). Fuzzy portfolio optimization using conditional drawdown at risk: Empirical evidence on selective companies in the Tehran Stock Exchange.Accounting, 11(2), 131-144.

Refrences

Ammar, E., & Khalifa, H. A. (2003). Fuzzy portfolio optimization is a quadratic programming approach. Chaos, Solitons & Fractals, 18(5), 1045-1054.
Ammar, E. E. (2008). On solutions of fuzzy random multiobjective quadratic programming with applications in a portfolio problem. Information Sciences, 178(2), 468-484.
Björk, K.-M. (2009). An analytical solution to a fuzzy economic order quantity problem. International journal of approximate reasoning, 50(3), 485-493.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2004). Portfolio optimization with drawdown constraints. In Supply chain and finance (pp. 209-228). World Scientific.
Chen, L.-H., & Huang, L. (2009). Portfolio optimization of equity mutual funds with fuzzy return rates and risks. Expert Systems with Applications, 36(2), 3720-3727.
Chen, S.-P. (2004). Parametric nonlinear programming for analyzing fuzzy queues with finite capacity. European Journal of Operational Research, 157(2), 429-438.
Eskorouchi, A., Mohammadi, E., & Sadjadi, S. J. (2022). Robust Portfolio Optimization based on Evidence Theory. Scientia Iranica.
Fei, W. (2007). Optimal consumption and portfolio choice with ambiguity and anticipation. Information Sciences, 177(23), 5178-5190.
Ghanbari, H., Safari, M., Ghousi, R., Mohammadi, E., & Nakharutai, N. (2023). Bibliometric analysis of risk measures for portfolio optimization. Accounting, 9(2), 95-108.
Gupta, P., Mehlawat, M. K., & Saxena, A. (2008). Asset portfolio optimization using fuzzy mathematical programming. Information Sciences, 178(6), 1734-1755.
Huang, S.-H., Miao, Y.-H., & Hsiao, Y.-T. (2021). Novel deep reinforcement algorithm with adaptive sampling strategy for continuous portfolio optimization. IEEE Access, 9, 77371-77385.
Khosravi, A., Sadjadi, S., & Ghanbari, H. (2024). A bibliometric analysis and visualization of the scientific publications on multi-period portfolio optimization: From the current status to future directions. Accounting, 10(3), 107-120.
Konno, H., & Yamazaki, H. (1991). Mean-absolute deviation portfolio optimization model and its applications to the Tokyo stock market. Management Science, 37(5), 519-531.
Krokhmal, P., Uryasev, S., & Zrazhevsky, G. (2005). Numerical comparison of conditional value-at-risk and conditional drawdown-at-risk approaches: application to hedge funds. In Applications of stochastic programming (pp. 609-631). SIAM.
Li, C., Wu, Y., Lu, Z., Wang, J., & Hu, Y. (2020). A multiperiod multiobjective portfolio selection model with fuzzy random returns for large-scale securities data. IEEE Transactions on Fuzzy Systems, 29(1), 59-74.
Liu, S.-T. (2005). Fuzzy measures for fuzzy signal-to-noise ratios.
Markowitz, H. (1952). Modern portfolio theory. Journal of Finance, 7(11), 77-91.
Markowitz Harry, M. (1959). Portfolio selection: Efficient diversification of investments. Cowles Foundation Monograph, 16.
Perold, A. F. (1984). Large-scale portfolio optimization. Management Science, 30(10), 1143-1160.
Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2, 21-42.
Sakawa, M., Utaka, J., Inniguchi, I., Shiromaru, I., Suginohara, N., & Inoue, T. (1993). Hot parts operating schedule of gas turbines by genetic algorithms and fuzzy satisficing methods. Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan),
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance, 19(3), 425-442.
Sharpe, W. F. (1967). A linear programming algorithm for mutual fund portfolio selection. Management Science, 13(7), 499-510.
Sharpe, W. F. (1971). A linear programming approximation for the general portfolio analysis problem. Journal of Financial and Quantitative Analysis, 6(5), 1263-1275.
Stone, B. K. (1973). A linear programming formulation of the general portfolio selection problem. Journal of Financial and Quantitative Analysis, 8(4), 621-636.
Vercher, E. (2008). Portfolios with fuzzy returns: selection strategies based on semi-infinite programming. Journal of computational and applied mathematics, 217(2), 381-393.
Vijayan, T., & Kumaran, M. (2009). Fuzzy economic order time models with random demand. International journal of approximate reasoning, 50(3), 529-540.
Wu, M.-E., Syu, J.-H., Lin, J. C.-W., & Ho, J.-M. (2021). Effective fuzzy system for qualifying the characteristics of stocks by random trading. IEEE Transactions on Fuzzy Systems, 30(8), 3152-3165.
Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 1(1), 3-28.
Zarezade, R., Ghousi, G., & Mohammadi, E. (2024). Spillover effects of volatility between the Chinese stock market and selected emerging economies in the Middle East: A conditional correlation analysis with portfolio optimization perspective. Accounting, 10(2), 97-106.
Zarezade, R., Ghousi, R., Mohammadi, E., & Ghanbari, H. (2024). Chance-constrained programming for Cryptocurrency portfolio optimization using Conditional Drawdown at Risk. Journal of Industrial and Systems Engineering, 16(2), 130-153.
Zimmerman, J. (1978). Topographic generation of residual circulation by oscillatory (tidal) currents. Geophysical & Astrophysical Fluid Dynamics, 11(1), 35-47.