  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.
