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Soft Computing

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27.09.2019 | Focus

Polynomial goal programming and particle swarm optimization for enhanced indexation

verfasst von: Massimiliano Kaucic, Fabrizio Barbini, Federico Julian Camerota Verdù

Erschienen in: Soft Computing | Ausgabe 12/2020

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Abstract

Enhanced indexation is an investment strategy that aims to generate moderate and consistent excess returns with respect to a tracked benchmark index. In this work, we introduce an optimization approach where the risk of under-performing the benchmark is separated from the potential over-performance, and the Sharpe ratio measures the profitability of the active management. In addition, a cardinality constraint controls the number of active positions in the portfolio, while a turnover threshold limits the transaction costs. We adopt a polynomial goal programming approach to combine these objectives with the investor’s preferences. An improved version of the particle swarm optimization algorithm with a novel constraint-handling mechanism is proposed to solve the optimization problem. A numerical example, where the Euro Stoxx 50 Index is used as the benchmark, shows that our method consistently produces larger returns, with reduced costs and risk exposition, than the standard indexing strategies over a 10-year backtesting period.

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Titel: Polynomial goal programming and particle swarm optimization for enhanced indexation
verfasst von: Massimiliano Kaucic
Fabrizio Barbini
Federico Julian Camerota Verdù
Publikationsdatum: 27.09.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 12/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04378-5

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