  Prediction models are traditionally optimized independently from their use in
the asset allocation decision-making process. We address this shortcoming and
present a 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 historical simulations using both synthetic and global futures
data to demonstrate the benefits of the integrated approach.
