PAMR : passive aggressive mean reversion strategy for portfolio selection
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 tech...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/79730 http://hdl.handle.net/10220/18316 |
| _version_ | 1826120960123600896 |
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| author | Li, Bin Zhao, Peilin Gopalkrishnan, Vivekanand Hoi, Steven C. H. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Li, Bin Zhao, Peilin Gopalkrishnan, Vivekanand Hoi, Steven C. H. |
| author_sort | Li, Bin |
| collection | NTU |
| description | 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/. |
| first_indexed | 2024-10-01T05:24:57Z |
| format | Journal Article |
| id | ntu-10356/79730 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:24:57Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/797302020-05-28T07:18:27Z PAMR : passive aggressive mean reversion strategy for portfolio selection Li, Bin Zhao, Peilin Gopalkrishnan, Vivekanand Hoi, Steven C. H. School of Computer Engineering DRNTU::Engineering::Computer science and engineering 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/. Accepted version 2013-12-18T06:46:50Z 2019-12-06T13:32:57Z 2013-12-18T06:46:50Z 2019-12-06T13:32:57Z 2012 2012 Journal Article 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. https://hdl.handle.net/10356/79730 http://hdl.handle.net/10220/18316 10.1007/s10994-012-5281-z en Machine learning © The Author(s) 2012. application/pdf application/octet-stream application/pdf application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Li, Bin Zhao, Peilin Gopalkrishnan, Vivekanand Hoi, Steven C. H. PAMR : passive aggressive mean reversion strategy for portfolio selection |
| title | PAMR : passive aggressive mean reversion strategy for portfolio selection |
| title_full | PAMR : passive aggressive mean reversion strategy for portfolio selection |
| title_fullStr | PAMR : passive aggressive mean reversion strategy for portfolio selection |
| title_full_unstemmed | PAMR : passive aggressive mean reversion strategy for portfolio selection |
| title_short | PAMR : passive aggressive mean reversion strategy for portfolio selection |
| title_sort | pamr passive aggressive mean reversion strategy for portfolio selection |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/79730 http://hdl.handle.net/10220/18316 |
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