Gaussian Weighting Reversion Strategy for Accurate On-Line Portfolio Selection

摘要

Considering the drawbacks of existing reversion based on-line portfolio selection (PS) strategies, we propose a new on-line learning and reversion based strategy that is called Gaussian Weighting Reversion (GWR in short). On the one hand, to exploit the "time validity" of historical market data, which means that the more recent market data are more valuable to market prediction than the less recent market data, we use the Gaussian function to weight data in a moving window. On the other hand, for each time point we average two predictions to alleviate the impact of noise and outliers. We conduct extensive evaluation on six real market datasets and compare our strategy with nine existing methods, including the state of the art ones. Experimental results show that our method outperforms the existing methods.