Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/79730
Title: PAMR : passive aggressive mean reversion strategy for portfolio selection
Authors: Li, Bin
Zhao, Peilin
Gopalkrishnan, Vivekanand
Hoi, Steven C. H.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Li, B., Zhao, P., Hoi, S. C. H., & Gopalkrishnan, V. (2012). PAMR : passive aggressive mean reversion strategy for portfolio selection. Machine learning, 87(2), 221-258.
Series/Report no.: Machine learning
Abstract: This article 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. The experimental testbed including source codes and data sets is available at http://www.cais.ntu.edu.sg/~chhoi/PAMR/.
URI: https://hdl.handle.net/10356/79730
http://hdl.handle.net/10220/18316
DOI: 10.1007/s10994-012-5281-z
Rights: © The Author(s) 2012.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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PAMR_ML_final.pdfMain Article662.28 kBAdobe PDFThumbnail
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PAMR.zipMatlab source codes for PAMR3.94 MBUnknownView/Open
Terms of Use.pdfTerms of Releasing Datasets and Implementation172.18 kBAdobe PDFThumbnail
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Instructions for the PAMR Code and Data.pdfInstructions for the PAMR Code and Data124.03 kBAdobe PDFThumbnail
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