A Risk-Return Approach to Enhanced Indexation

Enhanced Indexation is the problem of selecting a portfolio that generates excess return with respect to a benchmark index. We propose a linear bi-objective optimization model for enhanced indexation that maximizes average excess return and minimizes underperformance with respect to the index over a given observation period. The efficient frontier for this problem can be easily computed with standard LP solvers. We describe interesting theoretical features of the model and we present some experimental results for well-known financial data sets showing promising out-of-sample performance