  Many problems in quantitative finance involve both predictive forecasting and
decision-based optimization. Traditionally, predictive models are optimized
with unique prediction-based objectives and constraints, and are therefore
unaware of how those predictions will ultimately be used in the context of
their final decision-based optimization. We present a stochastic optimization
framework for integrating regression based predictive models in a mean-variance
portfolio optimization setting. Closed-form analytical solutions are provided
for the unconstrained and equality constrained 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
global futures data and demonstrate the benefits of the integrated approach in
comparison to the decoupled alternative.
