On-Line Portfolio Selection with Moving Average Reversion
On-Line Portfolio Selection with Moving Average Reversion
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发表时间：
2012-06
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通讯作者：
Bin Li;S. Hoi
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作者：
Bin Li;S. Hoi
最近，在线投资组合选择吸引了机器学习和人工智能社区越来越多的兴趣。实证研究表明，股票价格的高低是暂时的，股价相关者很可能会出现均值回归现象。虽然现有的均值回归策略在许多真实的数据集上表现出良好的经验性能，但它们往往使单周期均值回归假设并不总是满足，导致在某些真实的数据集上性能不佳。为了克服这一局限性，本文提出了一种多期均值回归，即所谓的“移动平均回归”（MAR），并提出了一种新的在线投资组合选择策略，即“在线移动平均回归”（OLMAR），它利用MAR通过应用强大的在线学习技术。从我们的实证结果中，我们发现OLMAR可以克服现有均值回归算法的缺点，并取得显着更好的结果，特别是在现有均值回归算法失败的数据集上。除了上级性能外，OLMAR还运行速度极快，进一步支持其在广泛应用中的实用性。
On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidence 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, leading to poor performance in some real datasets. 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 drawbacks of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where existing mean reversion algorithms failed. In addition to superior performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications.