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Multiobjective dynamic optimization of investment portfolio based on model predictive control. (English) Zbl 1480.91265

Summary: In this paper, a multiobjective model predictive control (MO-MPC) for portfolio selection is proposed. The objective functions are defined using a multiperiod format through the receding horizon strategy, considering the expected wealth, the variance, and the conditional value at risk as the objective function to be optimized, including transaction costs, self-financing, and investment limits for each asset. A Pareto front is obtained in each step by a multiobjective genetic algorithm, and a Pareto optimal point is chosen as the control action applied to the system. This choice is made based on a selection criterion according to the investor profile. Finally, the performance of the MO-MPC facing extreme situations of the financial market is investigated through numerical experiments using data from the Brazilian stock exchange. Simulation results show that the MO-MPC can deal with the dynamic and unstable scenario, efficiently tracing the trade-off between risk and return, managing the transaction costs and bounds.

MSC:

91G10 Portfolio theory
93B45 Model predictive control
90C29 Multi-objective and goal programming
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