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St. Petersburg Portfolio Games

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Algorithmic Learning Theory (ALT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5809))

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Abstract

We investigate the performance of the constantly rebalanced portfolios, when the random vectors of the market process { X i } are independent, and each of them distributed as (X (1), X (2), ..., X (d), 1), d ≥ 1, where X (1), X (2), ..., X (d) are nonnegative iid random variables. Under general conditions we show that the optimal strategy is the uniform: (1/d, ..., 1/d, 0), at least for d large enough. In case of St. Petersburg components we compute the average growth rate and the optimal strategy for d = 1,2. In order to make the problem non-trivial, a commission factor is introduced and tuned to result in zero growth rate on any individual St. Petersburg components. One of the interesting observations made is that a combination of two components of zero growth can result in a strictly positive growth. For d ≥ 3 we prove that the uniform strategy is the best, and we obtain tight asymptotic results for the growth rate.

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© 2009 Springer-Verlag Berlin Heidelberg

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Györfi, L., Kevei, P. (2009). St. Petersburg Portfolio Games. In: Gavaldà, R., Lugosi, G., Zeugmann, T., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2009. Lecture Notes in Computer Science(), vol 5809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04414-4_11

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  • DOI: https://doi.org/10.1007/978-3-642-04414-4_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04413-7

  • Online ISBN: 978-3-642-04414-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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