Robo-Advising in Motion: A Model Predictive Control Approach

TL;DR

This paper introduces a dynamic, multi-period robo-advising framework using Model Predictive Control, which improves portfolio strategies by incorporating realistic constraints and forecasting methods, outperforming static approaches.

Contribution

It presents a novel MPC-based asset allocation model combining Hidden Markov Models and Black-Litterman, addressing practical constraints and comparing two optimality criteria.

Findings

MPC strategies outperform myopic approaches in simulations.

MV criterion yields flexible, diversified portfolios.

MRB criterion produces smoother, less sensitive allocations.

Abstract

Robo-advisors (RAs) are automated portfolio management systems that complement traditional financial advisors by offering lower fees and smaller initial investment requirements. While most existing RAs rely on static, one-period allocation methods, we propose a dynamic, multi-period asset-allocation framework that leverages Model Predictive Control (MPC) to generate suboptimal but practically effective strategies. Our approach combines a Hidden Markov Model with Black-Litterman (BL) methodology to forecast asset returns and covariances, and incorporates practically important constraints, including turnover limits, transaction costs, and target portfolio allocations. We study two predominant optimality criteria in wealth management: dynamic mean-variance (MV) and dynamic risk-budgeting (MRB). Numerical experiments demonstrate that MPC-based strategies consistently outperform myopic approaches, with MV providing flexible and diversified portfolios, while MRB delivers smoother allocations less sensitive to key parameters. These findings highlight the trade-offs between adaptability and stability in practical robo-advising design.