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Can We Learn to Beat the Best Stock

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  • A. Borodin
  • R. El-Yaniv
  • V. Gogan
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    Abstract

    A novel algorithm for actively trading stocks is presented. While traditional expert advice and "universal" algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of technical trading can "beat the market" and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning.

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    File URL: http://arxiv.org/pdf/1107.0036
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1107.0036.

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    Date of creation: Jun 2011
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    Publication status: Published in Journal Of Artificial Intelligence Research, Volume 21, pages 579-594, 2004
    Handle: RePEc:arx:papers:1107.0036

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    Web page: http://arxiv.org/

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    1. Thomas M. Cover, 1991. "Universal Portfolios," Mathematical Finance, Wiley Blackwell, Wiley Blackwell, vol. 1(1), pages 1-29.
    2. Nicolo Cesa Bianchi & Gábor Lugosi, 1998. "On prediction of individual sequences," Economics Working Papers 324, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Brock, W. & Lakonishok, J. & Lebaron, B., 1991. "Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns," Working papers, Wisconsin Madison - Social Systems 90-22, Wisconsin Madison - Social Systems.
    4. David P. Helmbold & Robert E. Schapire & Yoram Singer & Manfred K. Warmuth, 1998. "On-Line Portfolio Selection Using Multiplicative Updates," Mathematical Finance, Wiley Blackwell, Wiley Blackwell, vol. 8(4), pages 325-347.
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