ABSTRACT

This chapter proposes a novel online portfolio selection (OLPS) strategy named “passive-aggressive mean reversion” (PAMR) (Li et al. 2012). Unlike traditional trend-following approaches, the proposed approach relies upon the mean reversion relation of financial markets. We are the first to devise a loss function that reflects the mean reversion principle. Further equipped with passive-aggressive online learning (Crammer et al. 2006), the proposed strategy can effectively exploit mean reversion. By analyzing PAMR’s update scheme, we find that it nicely trades portfolio return with volatility risk and reflects the mean reversion principle. We conduct extensive numerical experiments in Part IV to evaluate the proposed algorithms on various real datasets. In most cases, the proposed PAMR strategy outperforms all benchmarks and almost all state-of-the-art strategies under various performance metrics. In addition to superior performance, the proposed PAMR runs extremely fast and thus is very suitable for real-life online trading applications.