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利用移动平均回归进行在线投资组合选择

文章 arXiv papers · 作者: Bin Li et al.
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总结

本文介绍移动平均回归（MAR），这是一些在线投资组合策略所采用的单期均值回归假设的一种多期替代方案。本文提出在线移动平均回归（OLMAR），运用在线学习技术，根据MAR构建投资组合。研究的动机是，股价暂时的高点和低点可能在多个时期内回归，而单期回归并非在每个数据集中都稳定出现。

作者报告了基于真实数据集的实证比较：OLMAR优于现有均值回归算法，尤其是在这些算法表现不佳的数据集上，而且运行速度快。这些结论依据本文报告的实验，但所提供的描述没有说明数据集身份、基准细节、交易成本或数值结果。因此，应将该策略的表现视为针对所测试数据的实证证据，而不是多期回归将持续存在或结果能够迁移至其他市场的保证。

核心观点

  • MAR将均值回归信号扩展到多个时期，而非依赖单期假设。
  • OLMAR运用在线学习技术，将移动平均回归用于投资组合选择。
  • 该方法针对单期均值回归不稳定的情形。
  • 作者报告称，该方法的实证表现优于现有均值回归算法，尤其是在这些算法失效的数据集上。
  • 所提供的描述报告了较快的计算速度，但没有说明数据集和交易成本细节。

标签

股票均值回归机器学习投资组合构建
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# On-Line Portfolio Selection with Moving Average Reversion


# On-Line Portfolio Selection with Moving Average Reversion









On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied in some real datasets, leading to poor performance when the assumption does not hold. To overcome the limitation, this article proposes a multiple-period mean reversion, or so-called Moving Average Reversion (MAR), and a new on-line portfolio selection strategy named "On-Line Moving Average Reversion" (OLMAR), which exploits MAR by applying powerful online learning techniques. From our empirical results, we found that OLMAR can overcome the drawback of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where the existing mean reversion algorithms failed. In addition to superior trading performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。