Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2012

Abstract

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

Keywords

Data sets, Empirical evidence, Empirical performance, Mean reversion, Moving averages, Online learning, Portfolio selection

Discipline

Computer Sciences | Databases and Information Systems | Portfolio and Security Analysis | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Proceedings of the Twenty-Ninth International Conference on Machine Learning: June 26 - July 1, 2012, Edinburgh, Scotland

First Page

273

Last Page

280

ISBN

9781450312851

Publisher

International Machine Learning Society

City or Country

Madison, WI

Copyright Owner and License

Authors

Additional URL

https://icml.cc/2012/papers/168.pdf

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