A non-index mixed-asset portfolio optimization approach

As the financial industry shifts from divided operations to mixed operations, mixed-asset portfolios have gradually gained ground in investment portfolios. Existing mixed-asset portfolio optimization approaches frequently introduce indices for representing asset classes to eliminate heterogeneity among asset classes. However, few introduced indices comprehensively and realistically represent asset classes, leading to a loss of feasible solutions and practical reliability. To address these limitations, this paper proposes a non-index mixed-asset portfolio optimization approach consisting of problem modeling and problem solving. For problem modeling, our approach models the mixed-asset portfolio optimization as a multi-objective bi-level optimization problem. In the inner-level optimization, optimal portfolios within each asset class are constructed to represent the corresponding asset class. These optimal portfolios contain more information and are constructed from realistically available products, thus representing the asset class more comprehensively and practically. In the outer-level optimization, the allocation among the asset classes is optimized to obtain an optimal mixed-asset portfolio. For problem solving, a multi-swarm dynamic cooperative optimization method is proposed to solve the modeled problem. Considering that obtaining the complete inner-level optimization of the modeled problem is challenging and time-consuming, a dynamic collaboration mechanism is designed to obtain the optimal subset of the inner-level optimization, thus solving the problem efficiently and effectively. To verify the effectiveness of our proposed non-index approach, an experiment is conducted to compare our proposed approach with four state-of-the-art approaches. Our proposed non-index approach problem outperforms competitors in 27 of 30 scenarios on both the Pareto optimality and the realistic performance.