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Portfolio selection with probabilistic utility

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  • Robert Marschinski
  • Pietro Rossi
  • Massimo Tavoni
  • Flavio Cocco

Abstract

Inspired by statistical physics, we present a probabilistic approach to portfolio selection. Instead of seeking the global extremum of some chosen utility function, we reinterpret the latter as a probability distribution of ‘optimal’ portfolios, and select the portfolio that is given by the mean value with respect to that distribution. Compared to the standard maximization of expected utility, this approach has several attractive features. First, it significantly reduces the excessive sensitivity to external parameters that often plague optimization procedures. Second, it mitigates the commonly observed concentration on too few assets; and third, it provides a natural and consistent way to account for the incompleteness of information and the aversion to uncertainty. Supportive empirical evidence is derived by using artificial data to simulate finite-sample behavior and out-of-sample performance. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Robert Marschinski & Pietro Rossi & Massimo Tavoni & Flavio Cocco, 2007. "Portfolio selection with probabilistic utility," Annals of Operations Research, Springer, vol. 151(1), pages 223-239, April.
    • Handle: RePEc:spr:annopr:v:151:y:2007:i:1:p:223-239:10.1007/s10479-006-0117-5
    DOI: 10.1007/s10479-006-0117-5
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    References listed on IDEAS

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    Cited by:

    1. Ankit Dangi, 2013. "Financial Portfolio Optimization: Computationally guided agents to investigate, analyse and invest!?," Papers 1301.4194, arXiv.org.
    2. Matkovskyy, Roman & Jalan, Akanksha & Dowling, Michael & Bouraoui, Taoufik, 2021. "From bottom ten to top ten: The role of cryptocurrencies in enhancing portfolio return of poorly performing stocks," Finance Research Letters, Elsevier, vol. 38(C).
    3. Emmanuel Jurczenko & Bertrand Maillet & Paul Merlin, 2008. "Efficient Frontier for Robust Higher-order Moment Portfolio Selection," Post-Print halshs-00336475, HAL.

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