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Construction of Emerging Markets Exchange Traded Funds Using Multiobjective Particle Swarm Optimisation

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Artificial Neural Networks and Machine Learning – ICANN 2012 (ICANN 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7553))

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Abstract

Multiobjective particle swarm optimisation (MOPSO) techniques are used to implement a new Andean stock index as an exchange traded fund (ETF) with weightings adjusted to allow for a tradeoff between the minimisation of tracking error, and liquidity enhancement by the reduction of transaction costs and market impact. Solutions obtained by vector evaluated PSO (VEPSO) are compared with those obtained by the quantum-behaved version of this algorithm (VEQPSO) and it is found the best strategy for a portfolio manager would be to use a hybrid front with contributions from both versions of the MOPSO algorithm.

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Author information

Authors and Affiliations

  1. Dept of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    Marta Díez-Fernández, Sergio Alvarez Teleña & Denise Gorse

Authors
  1. Marta Díez-Fernández
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  2. Sergio Alvarez Teleña
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  3. Denise Gorse
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Editor information

Editors and Affiliations

  1. Neuro Heuristic Research Group, University of Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

  2. Department of Informatics, Nicolaus Copernicus University, 87-100, Toruń, Poland

    Włodzisław Duch

  3. Center for Complex Systems Studies, Kalamazoo College, 49006, Kalamazoo, MI, USA

    Péter Érdi

  4. Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, 16146, Genoa, Italy

    Francesco Masulli

  5. Institut für Neuroinformatik, Universität Ulm, 89069, Ulm, Germany

    Günther Palm

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© 2012 Springer-Verlag Berlin Heidelberg

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Díez-Fernández, M., Teleña, S.A., Gorse, D. (2012). Construction of Emerging Markets Exchange Traded Funds Using Multiobjective Particle Swarm Optimisation. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_18

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  • DOI: https://doi.org/10.1007/978-3-642-33266-1_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33265-4

  • Online ISBN: 978-3-642-33266-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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