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In this paper, we consider the problem of financial portfolio optimization. A hierarchical framework is used, and receding horizon control (RHC) ideas are exploited to pose and solve two relevant constrained optimization problems. We first present the classic problem of wealth maximization subject to risk constraints. We also formulate a new approach to portfolio optimization which attempts to minimize the peak risk over the prediction horizon, while trying to track a wealth objective. This approach is designed to assist investors that might be unable to precisely specify their risk tolerance. We compare this methodology with the classical approach. It is concluded that this approach may be particularly beneficial during downturns - appreciably limiting losses during downturns while providing most of the upturn benefits.