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Efficient Universal Portfolios for Past‐Dependent Target Classes

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  • Jason E. Cross
  • Andrew R. Barron

Abstract

We present a new universal portfolio algorithm that achieves almost the same level of wealth as could be achieved by knowing stock prices ahead of time. Specifically the algorithm tracks the best in hindsight wealth achievable within target classes of linearly parameterized portfolio sequences. The target classes considered are more general than the standard constant rebalanced portfolio class and permit portfolio sequences to exhibit a continuous form of dependence on past prices or other side information. A primary advantage of the algorithm is that it is easily computable in a polynomial number of steps by way of simple closed‐form expressions. This provides an edge over other universal algorithms that require both an exponential number of computations and numerical approximation.

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  • Jason E. Cross & Andrew R. Barron, 2003. "Efficient Universal Portfolios for Past‐Dependent Target Classes," Mathematical Finance, Wiley Blackwell, vol. 13(2), pages 245-276, April.
    • Handle: RePEc:bla:mathfi:v:13:y:2003:i:2:p:245-276
    DOI: 10.1111/1467-9965.00016
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    Cited by:

    1. Ting-Kam Leonard Wong, 2015. "Universal portfolios in stochastic portfolio theory," Papers 1510.02808, arXiv.org, revised Dec 2016.
    2. 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.
    3. 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.
    4. Tim Gebbie & Fayyaaz Loonat, 2016. "Learning zero-cost portfolio selection with pattern matching," Papers 1605.04600, arXiv.org.

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