Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection

• Main Authors: LI, Bin, HOI, Steven C. H., ZHAO, Peilin, Gopalkrishnan, Vivek
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2011
• 主題: Databases and Information Systems; Finance and Financial Management; Management Information Systems
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/2292; https://ink.library.smu.edu.sg/context/sis_research/article/3292/viewcontent/Hoi2011ConfidenceWeighted.pdf

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.