Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2012

Abstract

This project proposes a novel online portfolio selection strategy named ``Passive Aggressive Mean Reversion" (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the mean reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the mean reversion property of markets. By analyzing PAMR's update scheme, we find that it nicely trades off between portfolio return and volatility risk and reflects the mean reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We conduct extensive numerical experiments to evaluate the empirical performance of the proposed algorithms on various real datasets. The encouraging results show that in most cases the proposed PAMR strategy outperforms all benchmarks and almost all state-of-the-art portfolio selection strategies under various performance metrics. In addition to its superior performance, the proposed PAMR runs extremely fast and thus is very suitable for real-life online trading applications

Keywords

Portfolio selection, Mean reversion, Passive aggressive learning, Online learning

Discipline

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

Research Areas

Data Science and Engineering

Publication

Machine Learning

Volume

87

Issue

2

First Page

221

Last Page

258

ISSN

0885-6125

Identifier

10.1007/s10994-012-5281-z

Publisher

Springer

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s10994-012-5281-z

Download

Share

COinS