Abstract A portfolio optimization problem equipped with stochastic dominance constraints creates optimal portfolio ideal for rational and risk-averse investors. This paper proposes a robust portfolio optimization model involving second-order stochastic dominance in constraints. The input returns of the assets are considered as uncertain parameters and are varied in symmetric and bounded intervals to construct an optimal robust portfolio. Although the resulting optimization model is a linear program, it involves a large number of constraints, thereby urging us to apply the cutting plane algorithm. We experimentally examine the performance of our model on datasets drawn from S&P 500, S&P BSE 500, Nikkei 225, S&P Global 100, FTSE 100, and BOVESPA index, and compare it with the corresponding non-robust counterpart model. The optimal portfolios from the proposed model are shown to yield better performance on several performance measures, including Sharpe ratio, STARR ratio, standard deviation, worst return, violation area in SSD, value at risk, and conditional value at risk. The results demonstrate the effectiveness and efficiency of the presented robust model.

中文翻译：

---

具有二阶随机优势约束的稳健投资组合优化

摘要 配备随机优势约束的投资组合优化问题为理性和规避风险的投资者创造了理想的最佳投资组合。本文提出了一种稳健的投资组合优化模型，该模型涉及约束中的二阶随机优势。资产的输入回报被视为不确定参数，并在对称和有界区间内变化以构建最优稳健投资组合。虽然得到的优化模型是一个线性规划，但它涉及到大量的约束，从而促使我们应用切割平面算法。我们通过实验检验了我们的模型在来自 S&P 500、S&P BSE 500、Nikkei 225、S&P Global 100、FTSE 100 和 BOVESPA 指数的数据集上的性能，并将其与相应的非稳健对应模型进行比较。所提出模型的最佳投资组合在多种性能指标上表现出更好的性能，包括夏普比率、STARR 比率、标准差、最差回报、SSD 中的违规区域、风险价值和条件风险价值。结果证明了所提出的鲁棒模型的有效性和效率。