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Approximation of Pareto Set in Multi Objective Portfolio Optimization

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Advances in Electrical Engineering and Computational Science

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 39))

In this chapter we experimentally investigate several evolutionary multi objective optimization methods and compare their efficiency in problems of portfolio selection with the efficiency of specially tailored method of adjustable weights. Test problems were based on standard portfolio quality criteria, and data on stocks of ten Lithuanian companies. We do not concern here in mach between analytical properties of the criteria functions and such properties favorable for the considered methods; we believe, however that general (global) structure of multi-criteria portfolio selection problem will be invariant with respect to switching from criteria defined by simple analytical formula to criteria defined by complicated numerical methods.

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Author information

Authors and Affiliations

  1. Department of Informatics, Vytautas Magnus University, 8 Vileikos str., Kaunas, LT, 44404, Lithuania

    I. Radziukyniene

Authors
  1. I. Radziukyniene
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  2. A. Zilinskas
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Editor information

Editors and Affiliations

  1. Harvard School of Engineering and Applied Sciences, Harvard University, 60 Oxford Street, Cambridge, MA, 02138, USA

    Sio-Iong Ao

  2. School of Engineering, Dept. of Process and Systems Engineering, Cranfield University, Cranfield, Beds, MK43 0AL, UK

    Len Gelman

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Radziukyniene, I., Zilinskas, A. (2009). Approximation of Pareto Set in Multi Objective Portfolio Optimization. In: Ao, SI., Gelman, L. (eds) Advances in Electrical Engineering and Computational Science. Lecture Notes in Electrical Engineering, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2311-7_47

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  • DOI: https://doi.org/10.1007/978-90-481-2311-7_47

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-2310-0

  • Online ISBN: 978-90-481-2311-7

  • eBook Packages: EngineeringEngineering (R0)

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