Gaussian Weighting Reversion Strategy for Accurate Online Portfolio Selection

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Gaussian Weighting Reversion Strategy for Accurate Online Portfolio Selection

Xia Cai; Zekun Ye

In this paper, we design and implement a new on-line portfolio selection strategy based on reversion mechanism and weighted on-line learning. Our strategy, called “Gaussian Weighting Reversion” (GWR), improves the reversion estimator to form optimal portfolios and effectively overcomes the shortcomings of existing on-line portfolio selection strategies. Firstly, GWR uses Gaussian function to weight data in a sliding window to exploit the “time validity” of historical market data. It means that the more recent data are more valuable for market prediction than the earlier. Secondly, the self-learning for various sliding windows is created to make our strategy adaptive to different markets. In addition, double estimations are first proposed to be made at each time point, and the average of double estimations is obtained to alleviate the influence of noise and outliers. Extensive evaluation on six public datasets shows the advantages of our strategy compared with other nine competing strategies, including the state-of-the-art ones. Finally, the complexity analysis of GWR shows its availability in large-scale real-life online trading.


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