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Competitive Portfolio Selection Using Stochastic Predictions

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Algorithmic Learning Theory (ALT 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9925))

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Abstract

We study a portfolio selection problem where a player attempts to maximise a utility function that represents the growth rate of wealth. We show that, given some stochastic predictions of the asset prices in the next time step, a sublinear expected regret is attainable against an optimal greedy algorithm, subject to tradeoff against the “accuracy” of such predictions that learn (or improve) over time. We also study the effects of introducing transaction costs into the model.

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Notes

  1. 1.

    The notations \(b_t x_t\) is used as a short-hand for vector dot product.

  2. 2.

    The notations \(\preceq \), \(\succeq \), \(\prec \), and \(\succ \) denote component-wise vector inequalities.

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Author information

Authors and Affiliations

  1. Department of Mathematics, London School of Economics, London, UK

    Tuğkan Batu & Pongphat Taptagaporn

Authors
  1. Tuğkan Batu
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  2. Pongphat Taptagaporn
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Corresponding author

Correspondence to Pongphat Taptagaporn .

Editor information

Editors and Affiliations

  1. Montanuniversität Leoben , Leoben, Austria

    Ronald Ortner

  2. Ruhr-Uni-Bochum , Bochum, Germany

    Hans Ulrich Simon

  3. University of Regina , Regina, Saskatchewan, Canada

    Sandra Zilles

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Batu, T., Taptagaporn, P. (2016). Competitive Portfolio Selection Using Stochastic Predictions. In: Ortner, R., Simon, H., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2016. Lecture Notes in Computer Science(), vol 9925. Springer, Cham. https://doi.org/10.1007/978-3-319-46379-7_20

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  • DOI: https://doi.org/10.1007/978-3-319-46379-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46378-0

  • Online ISBN: 978-3-319-46379-7

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