Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection

Bin Li, Steven C.H. Hoi, Peilin Zhao, Vivekanand Gopalkrishnan
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:434-442, 2011.

Abstract

This paper proposes a novel on-line portfolio selection strategy named “Confidence Weighted Mean Reversion” (CWMR). Inspired by the mean reversion principle and the confidence weighted online learning technique, CWMR models a portfolio vector as Gaussian distribution, and sequentially updates the distribution by following the mean reversion trading principle. The CWMR strategy is able to effectively exploit the power of mean reversion for on-line portfolio selection. Extensive experiments on various real markets demonstrate the effectiveness of our strategy in comparison to the state of the art.

Cite this Paper

BibTeX
@InProceedings{pmlr-v15-li11b, title = {Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection}, author = {Li, Bin and Hoi, Steven C.H. and Zhao, Peilin and Gopalkrishnan, Vivekanand}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {434--442}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/li11b/li11b.pdf}, url = {https://proceedings.mlr.press/v15/li11b.html}, abstract = {This paper proposes a novel on-line portfolio selection strategy named “Confidence Weighted Mean Reversion” (CWMR). Inspired by the mean reversion principle and the confidence weighted online learning technique, CWMR models a portfolio vector as Gaussian distribution, and sequentially updates the distribution by following the mean reversion trading principle. The CWMR strategy is able to effectively exploit the power of mean reversion for on-line portfolio selection. Extensive experiments on various real markets demonstrate the effectiveness of our strategy in comparison to the state of the art.} }
Endnote
%0 Conference Paper %T Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection %A Bin Li %A Steven C.H. Hoi %A Peilin Zhao %A Vivekanand Gopalkrishnan %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-li11b %I PMLR %P 434--442 %U https://proceedings.mlr.press/v15/li11b.html %V 15 %X This paper proposes a novel on-line portfolio selection strategy named “Confidence Weighted Mean Reversion” (CWMR). Inspired by the mean reversion principle and the confidence weighted online learning technique, CWMR models a portfolio vector as Gaussian distribution, and sequentially updates the distribution by following the mean reversion trading principle. The CWMR strategy is able to effectively exploit the power of mean reversion for on-line portfolio selection. Extensive experiments on various real markets demonstrate the effectiveness of our strategy in comparison to the state of the art.
RIS
TY - CPAPER TI - Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection AU - Bin Li AU - Steven C.H. Hoi AU - Peilin Zhao AU - Vivekanand Gopalkrishnan BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-li11b PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 434 EP - 442 L1 - http://proceedings.mlr.press/v15/li11b/li11b.pdf UR - https://proceedings.mlr.press/v15/li11b.html AB - This paper proposes a novel on-line portfolio selection strategy named “Confidence Weighted Mean Reversion” (CWMR). Inspired by the mean reversion principle and the confidence weighted online learning technique, CWMR models a portfolio vector as Gaussian distribution, and sequentially updates the distribution by following the mean reversion trading principle. The CWMR strategy is able to effectively exploit the power of mean reversion for on-line portfolio selection. Extensive experiments on various real markets demonstrate the effectiveness of our strategy in comparison to the state of the art. ER -
APA
Li, B., Hoi, S.C., Zhao, P. & Gopalkrishnan, V.. (2011). Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:434-442 Available from https://proceedings.mlr.press/v15/li11b.html.

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