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PAMR: Passive aggressive mean reversion strategy for portfolio selection.

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Title: PAMR: Passive aggressive mean reversion strategy for portfolio selection.
Authors: Li, Bin1 s080061@ntu.edu.sg, Zhao, Peilin1 zhao0106@ntu.edu.sg, Hoi, Steven1 chhoi@ntu.edu.sg, Gopalkrishnan, Vivekanand2 vivekanand@deloitte.com
Source: Machine Learning. May2012, Vol. 87 Issue 2, p221-258. 38p.
Subjects: Passive learning, Distance education, Financial markets, Machine learning, Market volatility, Electronic trading of securities
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 . [ABSTRACT FROM AUTHOR]
Copyright of Machine Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Passive+learning%22">Passive learning</searchLink><br /><searchLink fieldCode="DE" term="%22Distance+education%22">Distance education</searchLink><br /><searchLink fieldCode="DE" term="%22Financial+markets%22">Financial markets</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Market+volatility%22">Market volatility</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+trading+of+securities%22">Electronic trading of securities</searchLink>
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  Data: 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 . [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Machine Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10994-012-5281-z
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        Text: English
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      – SubjectFull: Distance education
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      – SubjectFull: Financial markets
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      – SubjectFull: Machine learning
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      – SubjectFull: Market volatility
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      – SubjectFull: Electronic trading of securities
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              Text: May2012
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