Online Learning of Commission Avoidant Portfolio Ensembles

Autor:	Uziel, Guy, El-Yaniv, Ran
Rok vydání:	2016
Předmět:	Computer Science - Artificial Intelligence Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: text overlap with arXiv:1604.03266
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1605.00788 Zobrazit plný text záznamu View this record from Arxiv