Portfolio Choice Based on Third-Degree Stochastic Dominance
提出一种优化方法，构建在第三度随机占优意义上优于基准指数的投资组合，利用半方差性质和二次约束规划，实证显示相比均值-方差和第二度随机占优策略，年化超额收益提高近7个百分点且不增加下行风险。
We develop an optimization method for constructing investment portfolios that dominate a given benchmark portfolio in terms of third-degree stochastic dominance. Our approach relies on the properties of the semivariance function, a refinement of an existing “superconvex” dominance condition, and quadratic constrained programming. We apply our method to historical stock market data using an industry momentum strategy. Our enhanced portfolio generates important performance improvements compared with alternatives based on mean-variance dominance and second-degree stochastic dominance. Relative to the Center for Research in Security Prices all-share index, our portfolio increases average out-of-sample return by almost seven percentage points per annum without incurring more downside risk, using quarterly rebalancing and without short selling. This paper was accepted by Lauren Cohen, finance.