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 diversi cation analysis, and empirical veri cation of some of the proposed theoretical results.
|Date of creation:||Nov 2012|
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"Exact and Heuristic Approaches for the Index Tracking Problem with UCITS Constraints,"
Department of Economics
0685, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
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