2016/05/03 by Guy Uziel, Uziel, Guy, Ran El-Yaniv +1
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.1605.00788
arXiv admin note: text overlap with arXiv:1604.03266
arxiv created 2016/05/29 · arxiv updated 2016/05/31
We present a novel online ensemble learning strategy for portfolio selection. The new strategy controls and exploits any set of commission-oblivious portfolio selection algorithms. The strategy handles transaction costs using a novel commission avoidance mechanism. We prove a logarithmic regret bound for our strategy with respect to optimal mixtures of the base algorithms. Numerical examples validate the viability of our method and show significant improvement over the state-of-the-art.