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投资组合模型中期望收益等参数的估计误差对最优投资组合策略的稳定性产生重要影响.在提出考虑复杂约束和交易成本的鲁棒均值-CVa R投资组合模型的基础上,设计改进粒子群算法来求解该模型.应用实际交易数据对所提出的模型和算法进行数值实验和比较,结果表明改进粒子群算法能有效地求解该模型,产生更稳定的最优投资策略,从而能够更好地适合实际投资环境.
The estimation error of expectation return and other parameters in the portfolio model has an important influence on the stability of the optimal portfolio strategy.Based on the robust mean-CVa R portfolio model with complex constraints and transaction costs, an improved particle swarm optimization Algorithm to solve the model.Applying the actual transaction data to the proposed model and algorithm for numerical experiments and comparison, the results show that the improved particle swarm optimization algorithm can effectively solve the model to produce a more stable optimal investment strategy, which can better Suitable for the actual investment environment.