  We employ model predictive control for a multi-period portfolio optimization
problem. In addition to the mean-variance objective, we construct a portfolio
whose allocation is given by model predictive control with a risk-parity
objective, and provide a successive convex program algorithm that provides 30
times faster and robust solutions in the experiments. Computational results on
the multi-asset universe show that multi-period models perform better than
their single period counterparts in out-of-sample period, 2006-2020. The
out-of-sample risk-adjusted performance of both mean-variance and risk-parity
formulations beat the fix-mix benchmark, and achieve Sharpe ratio of 0.64 and
0.97, respectively.
