Integrating prediction in mean-variance portfolio optimization

Abstract

In quantitative finance, prediction models are traditionally optimized independently from their use in the asset allocation decision-making process. We address this limitation and present a stochastic optimization framework for integrating regression prediction models in a mean-variance optimization (MVO) setting. Closed-form analytical solutions are provided for the unconstrained and equality constrained MVO case. For the general inequality constrained case, we make use of recent advances in neural-network architecture for efficient optimization of batch quadratic programs. To our knowledge, this is the first rigorous study of integrating prediction in a mean-variance portfolio optimization setting. We present several simulations, using both synthetic and global futures data, and demonstrate the benefits of the integrated approach in comparison to the decoupled alternative.