Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Online portfolio selection: A survey
ACM Computing Surveys (CSUR)
Large scale online kernel classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Robust median reversion strategy for on-line portfolio selection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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聽http://www.cais.ntu.edu.sg/~chhoi/PAMR/.