Online Learning of Commission Avoidant Portfolio Ensembles
This work addresses portfolio optimization for investors by managing transaction costs, though it appears incremental as it builds on existing commission-oblivious algorithms.
The paper tackles the problem of portfolio selection with transaction costs by introducing an online ensemble learning strategy that includes a commission avoidance mechanism, achieving a logarithmic regret bound and demonstrating significant improvement over state-of-the-art methods in numerical examples.
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.