Integrating prediction in mean-variance portfolio optimization

TL;DR

This paper introduces a novel framework that integrates prediction models directly into mean-variance portfolio optimization, providing analytical solutions and demonstrating improved decision-making through simulations.

Contribution

It is the first rigorous study to incorporate prediction models into mean-variance portfolio optimization with analytical solutions and neural network-based optimization methods.

Findings

Improved portfolio decisions through integrated prediction and optimization.

Analytical solutions for unconstrained and equality constrained cases.

Enhanced optimization efficiency using neural-network architectures.

Abstract

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.