Publication Type
Journal Article
Version
publishedVersion
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 Science and Engineering
Publication
Artificial Intelligence
Volume
222
First Page
104
Last Page
133
ISSN
0004-3702
Identifier
10.1016/j.artint.2015.01.006
Publisher
Elsevier
Citation
LI, Bin; HOI, Steven C. H.; SAHOO, Doyen; and LIU, Zhi-Yong.
Moving average reversion strategy for on-line portfolio selection. (2015). Artificial Intelligence. 222, 104-133.
Available at: https://ink.library.smu.edu.sg/sis_research/2971
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.artint.2015.01.006
Included in
Databases and Information Systems Commons, Finance and Financial Management Commons, Numerical Analysis and Scientific Computing Commons
Comments
The short version of this work appeared at the 29th International Conference on Machine Learning (ICML 2012).