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Can We Learn to Beat the Best Stock

Author

Listed:
  • A. Borodin
  • R. El-Yaniv
  • V. Gogan

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.

Suggested Citation

  • A. Borodin & R. El-Yaniv & V. Gogan, 2011. "Can We Learn to Beat the Best Stock," Papers 1107.0036, arXiv.org.
    • Handle: RePEc:arx:papers:1107.0036
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    File URL: http://arxiv.org/pdf/1107.0036
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    References listed on IDEAS

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    1. Nicolo Cesa Bianchi & Gábor Lugosi, 1998. "On prediction of individual sequences," Economics Working Papers 324, Department of Economics and Business, Universitat Pompeu Fabra.
    2. David P. Helmbold & Robert E. Schapire & Yoram Singer & Manfred K. Warmuth, 1998. "On‐Line Portfolio Selection Using Multiplicative Updates," Mathematical Finance, Wiley Blackwell, vol. 8(4), pages 325-347, October.
    3. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    4. Thomas M. Cover, 1991. "Universal Portfolios," Mathematical Finance, Wiley Blackwell, vol. 1(1), pages 1-29, January.
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    Cited by:

    1. Chu, Gang & Zhang, Wei & Sun, Guofeng & Zhang, Xiaotao, 2019. "A new online portfolio selection algorithm based on Kalman Filter and anti-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    2. Hongliu He & Hua Li, 2024. "A New Boosting Algorithm for Online Portfolio Selection Based on dynamic Time Warping and Anti-correlation," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1777-1803, May.
    3. Zhengyong Jiang & Jeyan Thiayagalingam & Jionglong Su & Jinjun Liang, 2023. "CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy," Papers 2310.01319, arXiv.org.

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