Portfolio selection via constrained stochastic gradients

摘要

In this paper, we consider the online portfolio selection problem. We develop several algorithms for portfolio selection based on sequential regularized optimizations and constrained stochastic gradient based approximations to this. We relate these methods to related results in stochastic gradients and universal portfolios, and compare results of simulations using historical data. We also demonstrate that these results compare favorably with respect to so-called universal portfolios.