Title

Moving Average Reversion Strategy for On-line Portfolio Selection

Publication Type

Journal Article

Publication Date

5-2015

Abstract

On-line portfolio selection, a fundamental problem in computational finance, has attracted increasing interest from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock prices are likely to follow the mean reversion phenomenon. While 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 certain real datasets. To overcome this 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 via efficient and scalable online machine learning techniques. From our empirical results on real markets, 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 its superior empirical performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications. Finally, we have made all the datasets and source codes of this work publicly available at our project website: http://OLPS.stevenhoi.org/.

Keywords

Mean reversion, Moving average reversion, On-line learning, Portfolio selection

Discipline

Computer Sciences | Databases and Information Systems | Finance and Financial Management | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

Artificial Intelligence

Volume

222

First Page

104

Last Page

133

ISSN

0004-3702

Identifier

10.1016/j.artint.2015.01.006

Publisher

Elsevier

Additional URL

http://dx.doi.org/10.1016/j.artint.2015.01.006

Comments

The short version of this work appeared at the 29th International Conference on Machine Learning (ICML 2012).