New approximate stochastic dominance approaches for Enhanced Indexation models

In this paper we discuss portfolio selection strategies for Enhanced Indexation (EI), that 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 select 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 Ruszczynski (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.