Publication Type

Journal Article

Version

acceptedVersion

Publication Date

3-2013

Abstract

Online portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing online portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidence shows that relative stock prices may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel online portfolio selection strategy named Confidence Weighted Mean Reversion (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, CWMR models the portfolio vector as a Gaussian distribution, and sequentially updates the distribution by following the mean reversion trading principle. CWMR’s closed-form updates clearly reflect the mean reversion trading idea. We also present several variants of CWMR algorithms, including a CWMR mixture algorithm that is theoretical universal. Empirically, CWMR strategy is able to effectively exploit the power of mean reversion for online portfolio selection. Extensive experiments on various real markets show that the proposed strategy is superior to the state-of-the-art techniques. The experimental testbed including source codes and data sets is available online.

Keywords

Online learning, confidence weighted learning, Portfolio selection, mean reversion

Discipline

Databases and Information Systems | Finance and Financial Management | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Knowledge Discovery from Data

Volume

7

Issue

1

First Page

4-1

Last Page

38

ISSN

1556-4681

Identifier

10.1145/2435209.2435213

Publisher

ACM

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/2435209.2435213

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