A New Lp Model For Enhanced Indexation
AbstractEnhanced 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.
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Bibliographic InfoPaper provided by Department of Economics - University Roma Tre in its series Departmental Working Papers of Economics - University 'Roma Tre' with number 0168.
Date of creation: Nov 2012
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Enhanced Indexation; Linear Programming; Performance Analysis; Portfolio Management; No Arbitrage condition.;
Find related papers by JEL classification:
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
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Department of Economics
0685, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
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