网上投资组合选择研究综述
Online Portfolio Selection: A Survey
摘要：
在线投资组合选择是计算金融领域的一个基本问题，在金融学、统计学、人工智能、机器学习和数据挖掘等领域得到了广泛的研究。从在线机器学习的角度来看，我们首先制定在线投资组合选择作为一个顺序决策问题，然后调查各种国家的最先进的方法，分为几个主要类别，包括基准，“跟随赢家”的方法，“跟随输家”的方法，“模式匹配”为基础的方法，和“元学习算法”。除了问题公式和相关算法，我们还讨论了这些算法与资本增长理论的关系，以便更好地理解其基本交易思想的异同。本文旨在为学术界的机器学习和数据挖掘研究人员以及金融行业的量化投资组合经理提供及时而全面的调查，以帮助他们了解最新技术，并促进他们的研究和实际应用。我们还讨论了一些开放的问题，并评估了未来研究方向的一些新趋势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
