Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection
On-line portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing on-line 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 evidences show that the stock price relatives may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel on-line 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 which is theoretical universal. Empirically, CWMR strategy is able to effectively exploit the power of mean reversion for on-line portfolio selection. Extensive experiments on various real markets show that the proposed strategy is superior in comparison to the state-of-the-art techniques.
Databases and Information Systems | Finance and Financial Management | Management Information Systems
Data Management and Analytics
ACM Transactions on Knowledge Discovery from Data
LI, Bin; HOI, Chu Hong; ZHAO, Peilin; and Gopalkrishnan, Vivekanand.
Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection. (2013). ACM Transactions on Knowledge Discovery from Data. 7, (1),. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2268