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International Conference on Neural Information Processing

ICONIP 2015: Neural Information Processing pp 52–60Cite as

Trading Optimally Diversified Portfolios in Emerging Markets with Neuro-Particle Swarm Optimisation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9490))

Abstract

In previous work the authors have developed trading models using both particle swarm optimisation and neural networks for specific emerging markets industry sectors. Here, a more flexible model is developed that is effective across a wide range of sectors. It is discovered there is a strong dependence of the quality of returns on the minimum number of trades allowed within a given time period (a risk-minimisation measure used to maintain portfolio diversity) and that in the case of emerging markets the optimal value for this parameter may be different to the standard investment industry recommendation. Learning is then extended to include this parameter, with out-of-sample testing demonstrating very promising results.

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References

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Author information

Authors and Affiliations

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

    Pascal Khoury & Denise Gorse

  2. Charlemagne Capital, 39 St. James’s Street, London, SW1A 1JD, UK

    Pascal Khoury

Authors
  1. Pascal Khoury
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  2. Denise Gorse
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Corresponding author

Correspondence to Denise Gorse .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Khoury, P., Gorse, D. (2015). Trading Optimally Diversified Portfolios in Emerging Markets with Neuro-Particle Swarm Optimisation. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-26535-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26534-6

  • Online ISBN: 978-3-319-26535-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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