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Kernel-Based Semi-Log-Optimal Empirical Portfolio Selection Strategies

Author

Listed:
  • LÁSZLÓ GYÖRFI

    (Department of Computer Science and Information Theory, Budapest University of Technology and Economics, 1521 Stoczek u. 2, Budapest, Hungary)

  • ANDRÁS URBÁN

    (Department of Computer Science and Information Theory, Budapest University of Technology and Economics, 1521 Stoczek u. 2, Budapest, Hungary)

  • ISTVÁN VAJDA

    (Department of Computer Science and Information Theory, Budapest University of Technology and Economics, 1521 Stoczek u. 2, Budapest, Hungary)

Abstract

The purpose of this paper is to introduce an approximation of the kernel-based log-optimal investment strategy that guarantees an almost optimal rate of growth of the capital under minimal assumptions on the behavior of the market. The new strategy uses much less knowledge on the distribution of the market process. It is analyzed both theoretically and empirically. The theoretical results show that the asymptotic rate of growth well approximates the optimal one that one could achieve with a full knowledge of the statistical properties of the underlying process generating the market, under the only assumption that the market is stationary and ergodic. The empirical results show that the proposed semi-log-optimal and the log-optimal strategies have essentially the same performance measured on past NYSE data.

Suggested Citation

  • László Györfi & András Urbán & István Vajda, 2007. "Kernel-Based Semi-Log-Optimal Empirical Portfolio Selection Strategies," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 10(03), pages 505-516.
    • Handle: RePEc:wsi:ijtafx:v:10:y:2007:i:03:n:s0219024907004251
    DOI: 10.1142/S0219024907004251
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    Citations

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    Cited by:

    1. Purushottam Parthasarathy & Avinash Bhardwaj & Manjesh K. Hanawal, 2023. "Online Universal Dirichlet Factor Portfolios," Papers 2308.07763, arXiv.org, revised Nov 2023.
    2. Bin Li & Steven C. H. Hoi, 2012. "Online Portfolio Selection: A Survey," Papers 1212.2129, arXiv.org, revised May 2013.
    3. Gabor Nagy & Gergo Barta & Tamas Henk, 2015. "Portfolio optimization using local linear regression ensembles in RapidMiner," Papers 1506.08690, arXiv.org.
    4. Constantinos Kardaras & Scott Robertson, 2010. "Robust maximization of asymptotic growth," Papers 1005.3454, arXiv.org, revised Aug 2012.
    5. 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.

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