A NEW LP MODEL FOR ENHANCED INDEXATION

Enhanced Indexation is the problem of selecting a portfolio that should produce excess return with respect to a given benchmark index. In this work we propose a linear bi-objective optimization approach to Enhanced Indexation that maximizes average excess return and minimizes underperformance over a learning period. This can be formulated as a simple Linear Programming problem that is solved to optimality by standard LP codes. Moreover, we investigate conditions that guarantee or forbid the existence of a portfolio strictly outperforming the index. We present extensive computational analysis of the results on publicly available real-world nancial datasets, including comparison with previous results, performance and diversication analysis, and empirical verication of some of the proposed theoretical results.