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Nonparametric nearest neighbor based empirical portfolio selection strategies

Author

Listed:
  • Györfi László
  • Udina Frederic

    (Universitat Pompeu Fabra, Dept. of Economics and Business, Barcelona, Spanien)

  • Walk Harro

    (Universität Stuttgart, Institute of Stochastics and Applications, Stuttgart, Deutschland)

Abstract

In recent years optimal portfolio selection strategies for sequential investment have been shown to exist. Although their asymptotical optimality is well established, finite sample properties do need the adjustment of parameters that depend on dimensionality and scale. In this paper we introduce some nearest neighbor based portfolio selectors that solve these problems, and we show that they are also log-optimal for the very general class of stationary and ergodic random processes. The newly proposed algorithm shows very good finite-horizon performance when applied to different markets with different dimensionality or scales without any change: we see it as a very robust strategy.

Suggested Citation

  • Györfi László & Udina Frederic & Walk Harro, 2008. "Nonparametric nearest neighbor based empirical portfolio selection strategies," Statistics & Risk Modeling, De Gruyter, vol. 26(2), pages 145-157, March.
    • Handle: RePEc:bpj:strimo:v:26:y:2008:i:2:p:145-157:n:5
    DOI: 10.1524/stnd.2008.0917
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    References listed on IDEAS

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    1. László Györfi & Gábor Lugosi & Frederic Udina, 2006. "Nonparametric Kernel‐Based Sequential Investment Strategies," Mathematical Finance, Wiley Blackwell, vol. 16(2), pages 337-357, April.
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    Cited by:

    1. Fayyaaz Loonat & Tim Gebbie, 2018. "Learning zero-cost portfolio selection with pattern matching," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-38, September.
    2. Guo, Sini & Gu, Jia-Wen & Fok, Christopher H. & Ching, Wai-Ki, 2023. "Online portfolio selection with state-dependent price estimators and transaction costs," European Journal of Operational Research, Elsevier, vol. 311(1), pages 333-353.
    3. Victor DeMiguel & Francisco J. Nogales & Raman Uppal, 2014. "Stock Return Serial Dependence and Out-of-Sample Portfolio Performance," The Review of Financial Studies, Society for Financial Studies, vol. 27(4), pages 1031-1073.
    4. Guy Uziel & Ran El-Yaniv, 2017. "Growth-Optimal Portfolio Selection under CVaR Constraints," Papers 1705.09800, arXiv.org.
    5. Tim Gebbie & Fayyaaz Loonat, 2016. "Learning zero-cost portfolio selection with pattern matching," Papers 1605.04600, arXiv.org.
    6. Yang Wang & Dong Wang & Yaodong Wang & You Zhang, 2018. "RACORN-K: Risk-Aversion Pattern Matching-based Portfolio Selection," Papers 1802.10244, arXiv.org.
    7. Vajda, István & Ottucsák, György, 2006. "Empirikus portfólióstratégiák [Empirical portfolio strategies]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 624-640.
    8. Bin Li & Steven C. H. Hoi, 2012. "Online Portfolio Selection: A Survey," Papers 1212.2129, arXiv.org, revised May 2013.
    9. Man Yiu Tsang & Tony Sit & Hoi Ying Wong, 2022. "Adaptive Robust Online Portfolio Selection," Papers 2206.01064, arXiv.org.
    10. Mih�ly Ormos & Andr�s Urb�n, 2013. "Performance analysis of log-optimal portfolio strategies with transaction costs," Quantitative Finance, Taylor & Francis Journals, vol. 13(10), pages 1587-1597, October.
    11. Ha, Youngmin & Zhang, Hai, 2020. "Algorithmic trading for online portfolio selection under limited market liquidity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1033-1051.
    12. Guo, Sini & Gu, Jia-Wen & Ching, Wai-Ki, 2021. "Adaptive online portfolio selection with transaction costs," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1074-1086.

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