Flexible enhanced indexation models through stochastic dominance and ordered weighted average optimization
提出一种基于有序加权平均算子的增强指数化方法，通过新的近似随机优势规则选择能随机支配基准指数并产生超额收益的投资组合，实证表明优于多种现有策略。
In this paper, we discuss portfolio selection strategies for Enhanced Indexation (EI), which are based on stochastic dominance relations. The goal is to select portfolios that stochastically dominate a given benchmark but that, at the same time, must generate some excess return with respect to a benchmark index. To achieve this goal, we propose a new methodology that selects portfolios using the ordered weighted average (OWA) operator, which generalizes previous approaches based on minimax selection rules and still leads to solving linear programming models. We also introduce a new type of approximate stochastic dominance rule and show that it implies the almost Second-order Stochastic Dominance (SSD) criterion proposed by Lizyayev and Ruszczyński (2012). We prove that our EI model based on OWA selects portfolios that dominate a given benchmark through this new form of stochastic dominance criterion. We test the performance of the obtained portfolios in an extensive empirical analysis based on real-world datasets. The computational results show that our proposed approach outperforms several SSD-based strategies widely used in the literature, as well as the global minimum variance portfolio. • An approach based on the Ordered Weighted Average operator for enhanced indexation. • Approximate stochastic dominance through OWA scalarization for portfolio selection. • The OWA representation leads to solving linear programming problems. • New theoretical results for approximate stochastic dominance rules. • Good performance on real-world data of the proposed approach.