1 research outputs found
Online Learning of Portfolio Ensembles with Sector Exposure Regularization
We consider online learning of ensembles of portfolio selection algorithms
and aim to regularize risk by encouraging diversification with respect to a
predefined risk-driven grouping of stocks. Our procedure uses online convex
optimization to control capital allocation to underlying investment algorithms
while encouraging non-sparsity over the given grouping. We prove a logarithmic
regret for this procedure with respect to the best-in-hindsight ensemble. We
applied the procedure with known mean-reversion portfolio selection algorithms
using the standard GICS industry sector grouping. Empirical Experimental
results showed an impressive percentage increase of risk-adjusted return
(Sharpe ratio)