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.