Weak aggregating specialist algorithm for online portfolio selection

This paper proposes a novel online learning algorithm, named weak aggregating specialist algorithm (WASA), and presents its theoretical bound. This algorithm has a flexible feature, which is to allow abandoning some expert advice according to pre-set rules. Based on this algorithm, a new online portfolio strategy named weak aggregating specialized CRP (WASC) is designed, which only aggregates awake specialized expert advice. Firstly, a pool of special constant rebalanced portfoliosCRP(b) strategies is employed to construct the index set of specialized experts. Secondly, a distance function is exploited to measure the distance between the current adjusted portfolio and each specialized expert advice, and the index set of awake specialized experts is constructed. Finally, the portfolio is updated by aggregating all awake specialized expert advice. Furthermore, theoretical and experimental analyses are established to illustrate the performance of the proposed strategy WASC. Theoretical results guarantee that WASC performs as well as the best specialized expert. Experimental results show that WASC outperforms some existing strategies in terms of the return and risk metrics, which illustrates its effectiveness in various real financial markets.