Document Type : Research Article
Authors
1 Department of Industrial Engineering, CT.C., Islamic Azad University, Tehran, Iran.
2 Department of Finance, Esf.C., Islamic Azad University, Esfarayen, Iran.
Abstract
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.
Keywords
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