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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- Andrea Scozzari & Fabio Tardella & Sandra Paterlini & Thiemo Krink, 2012.
"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".
- Andrea Scozzari & Fabio Tardella & Sandra Paterlini & Thiemo Krink, 2012. "Exact and heuristic approaches for the index tracking problem with UCITS constraints," Center for Economic Research (RECent) 081, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
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