Portfolio Optimization Subject to Second-Order Stochastic Dominance Constraints
提出一种受二阶随机占优约束的投资组合优化方法，通过最大化九个风险调整收益指标，并开发高效算法处理大量约束，实证表明该方法在全球主要股市数据上优于多个基线。
In this article, we propose a constrained optimization approach to portfolio selection by maximizing nine risk-adjusted return metrics, subject to second-order stochastic dominance (SSD) constraints. The SSD constraints ensure that the portfolio returns are no less than an amplified proportion of returns from a benchmark in the sense of SSD. Because the number of SSD constraints is extremely large, the resulting constrained optimization problems are computationally challenging. To reduce computational complexity, we develop an efficient algorithm to solve the problems iteratively by incrementally adding SSD constraints. We experimentally demonstrate the superiority of the proposed approaches to several baselines in terms of out-of-sample performance criteria based on financial data from major world stock markets.