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.

提出了一种名为Moving Average Reversion（MAR）的多期均值回归方法和一个名为On-Line Moving Average Reversion（OLMAR）的在线投资组合选择策略，其利用在线学习技术，从实证结果来看，OLMAR可以克服现有的均值回归算法的局限性，在特定数据集上实现了显著更好的结果，而且运行速度非常快，可以广泛应用于各种应用领域。

移动平均回归下的在线组合投资选择