ABSTRACT

Empirical evidence (Borodin et al. 2004) shows that stock price relatives may follow the mean reversion property, which has not been fully exploited by existing strategies. Moreover, all existing online portfolio selection (OLPS) strategies only focus on the first-order information of a portfolio vector, though second-order information may also benefit a strategy. This chapter proposes a novel strategy named “confidenceweighted mean reversion” (CWMR) (Li et al. 2011b, 2013). Inspired by the mean reversion principle in finance and confidence-weighted (CW) online machine learning technique (Crammer et al. 2008; Dredze et al. 2008), CWMR models the portfolio vector as a Gaussian distribution, and sequentially updates the distribution following the mean reversion principle. Analysis of CWMR’s closed form updates clearly reflects the mean reversion trading idea and the interaction of first-order and second-order information. Extensive experiments, in Part IV, on various real markets show that CWMR is able to effectively exploit the power of mean reversion and second-order information, and is superior to the state-of-the-art techniques.