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A Two-Stage Stochastic Optimization Model for Portfolio Selection Under Decision-Making Uncertainties | ||
| Journal of Mathematics and Modeling in Finance | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 آذر 1404 اصل مقاله (490.52 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2025.86179.1186 | ||
| نویسندگان | ||
| Mostafa Sharif1؛ Parisa Shahnazari-Shahrezaei* 1؛ Meysam Doaei2 | ||
| 1Department of Industrial Engineering, CT.C., Islamic Azad University, Tehran, Iran. | ||
| 2Department of Finance, Esf.C., Islamic Azad University, Esfarayen, Iran. | ||
| چکیده | ||
| This paper introduces a two-stage stochastic optimization model for portfolio selection, designed to address decision-making uncertainties in the context of the Iranian stock market. The model accounts for a range of disruption scenarios—including economic sanctions, oil price fluctuations, political instability, and currency devaluation—enabling dynamic portfolio adjustments to optimize risk-adjusted returns. To manage extreme downside risks, it employs Conditional Value-at-Risk (CVaR) as the risk measure, while simultaneously aiming to maximize expected returns. Compared to traditional mean-variance portfolio optimization, the proposed model demonstrates clear advantages by adapting to uncertain market conditions through scenario-based rebalancing. Sensitivity analysis highlights the model’s responsiveness to critical parameters such as risk aversion, scenario probabilities, and adjustment costs, offering valuable insights into their impact on portfolio performance. The results show that the two-stage model delivers stronger risk management and improved return outcomes than static approaches. Nevertheless, limitations exist, particularly regarding the reliance on accurate scenario probabilities and the assumption of fixed adjustment costs, which may affect real-world applicability. Future research could enhance the model by applying machine learning to refine probability estimates, extending its use to other emerging markets, and integrating more flexible and dynamic cost structures for asset reallocation. The proposed model provides a robust framework for managing investment portfolios in volatile and uncertain environments. | ||
| کلیدواژهها | ||
| Two-stage stochastic optimization؛ portfolio selection؛ decision-making uncertainties؛ scenario-based adjustments | ||
| مراجع | ||
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[1] Barro, D., Consigli, G., & Varun, V. (2022). A stochastic programming model for dynamic portfolio management with financial derivatives. Journal of Banking & Finance, 140, 106445. [2] Bauder, D., Bodnar, T., Parolya, N., & Schmid, W. (2021). Bayesian meanvariance analysis: optimal portfolio selection under parameter uncertainty. Quantitative Finance, 21(2), 221– 242. [3] Campbell, S., & Wong, T. K. L. (2022). Functional portfolio optimization in stochastic portfolio theory. SIAM Journal on Financial Mathematics, 13(2), 576–618. [4] Cui, T., Bai, R., Ding, S., Parkes, A. J., Qu, R., He, F., & Li, J. (2020). A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices. Soft Computing, 24, 2809–2831. [5] Dai, T. S., Chen, B. J., Sun, Y. J., Yang, D. Y., & Wu, M. E. (2024). Constructing Optimal Portfolio Rebalancing Strategies with a Two-Stage Multiresolution-Grid Model. Computational Economics, 1–26. [6] Dai, Y., & Qin, Z. (2021). Multi-period uncertain portfolio optimization model with minimum transaction lots and dynamic risk preference. Applied Soft Computing, 109, 107519. [7] Doaei, M. (2024). A bi-level optimization heuristic for solving portfolio selection problem. International Journal of Finance & Managerial Accounting, 11(41), 123–138. [8] Doaei, M., Dehnad, K., & Dehnad, M. (2024). A hybrid approach based on multicriteria decision making and data-driven optimization in solving portfolio selection problem. OPSEARCH, 1–36. [9] He, F., & Qu, R. (2014). A two-stage stochastic mixed-integer program modelling and hybrid solution approach to portfolio selection problems. Information Sciences, 289, 190–205. [10] Krokhmal, P., Palmquist, J., & Uryasev, S. (2002). Portfolio optimization with conditional value-at-risk objective and constraints. Journal of Risk, 4, 43–68. [11] Markowitz, H. M. (1991). Foundations of portfolio theory. The Journal of Finance, 46(2), 469–477. [12] Ramedani, A. M., Mehrabian, A., & Didehkhani, H. (2024). A two-stage sustainable uncertain multi-objective portfolio selection and scheduling considering conflicting criteria. Engineering Applications of Artificial Intelligence, 132, 107942. [13] Taleb, N. N. (2008). The impact of the highly improbable. Penguin Books Limited. [14] Topaloglou, N., Vladimirou, H., & Zenios, S. A. (2008). A dynamic stochastic programming model for international portfolio management. European Journal of Operational Research, 185(3), 1501–1524. [15] Yadav, S., Gupta, P., Mehlawat, M. K., & Kumar, A. (2024). A multiobjective multiperiod portfolio selection approach with different investor attitudes under an uncertain environment. Soft Computing, 28(13), 8013–8050. [16] Zahmati Iraj, M., & Doaei, M. (2024). A Hybrid Decision-Making Model for Optimal Portfolio Selection under Interval Uncertainty. Iranian Journal of Accounting, Auditing and Finance, 8(4), 1–24. [17] Zandieh, M., & Mohaddesi, S. O. (2019). Portfolio rebalancing under uncertainty using metaheuristic algorithm. International Journal of Operational Research, 36(1), 12–39. [18] Zolfaghari, S., & Mousavi, S. M. (2021). A novel mathematical programming model for multimode project portfolio selection and scheduling with flexible resources and due dates under interval-valued fuzzy random uncertainty. Expert Systems with Applications, 182, 115207. | ||
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