Financial market has systemic complexity and *** investors,return and risk often *** to rationally allocate funds into different assets and achieve excess returns with effectively controlling risk are main problems to...
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Financial market has systemic complexity and *** investors,return and risk often *** to rationally allocate funds into different assets and achieve excess returns with effectively controlling risk are main problems to be solved in the field of portfolio optimization(PO).At present,due to the influence of modeling and algorithm solving,the PO models established by many researchers are still mainly focused on single-stage single-objective models or single-stage multiobjective *** is actually considered as a multi-stage multi-objective optimization problem in real investment *** is more difficult than the previous single-stage PO model for meeting the realistic *** this paper,the authors proposed a mean-improved stable tail adjusted return ratio-maximum drawdown rate(M-ISTARR-MD)PO model which effectively characterizes the real investment *** order to solve the multi-stage multi-objective PO model with complex multi-constraints,the authors designed a multi-stage constrainedmulti-objective evolutionaryalgorithm with orthogonal learning(MSCMOEA-OL).Comparing with four well-known intelligence algorithms,the MSCMOEA-OL algorithm has competitive advantages in solving the M-ISTARR-MD model on the proposed constructed carbon neutral stock *** paper provides a new way to construct and solve the complex PO model.
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