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Online Portfolio Selection: A Survey

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  • Bin Li
  • Steven C. H. Hoi
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    Abstract

    Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining, etc. This article aims to provide a comprehensive survey and a structural understanding of published online portfolio selection techniques. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and "Meta-Learning Algorithms". In addition to the problem formulation and related algorithms, we also discuss the relationship of these algorithms with the Capital Growth theory in order to better understand the similarities and differences of their underlying trading ideas. This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them understand the state-of-the-art and facilitate their research and practical applications. We also discuss some open issues and evaluate some emerging new trends for future research directions.

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    File URL: http://arxiv.org/pdf/1212.2129
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1212.2129.

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    Date of creation: Dec 2012
    Date of revision: May 2013
    Handle: RePEc:arx:papers:1212.2129

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    Web page: http://arxiv.org/

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