Application of a Multi-Objective Optimization Algorithm Based on Differential Grouping to Financial Asset Allocation

In the era of big data and rapid information growth, investors encounter a complex financial environment characterized by extensive data, conflicting investment objectives, and markets that are unpredictable due to economic and policy fluctuations. Hence, asset selection is vital for both investors and researchers. Multi-objective optimization algorithms balance multiple objectives to find optimal solutions and are widely used in engineering, economics, etc. This paper proposes a multi-objective decomposition optimization algorithm integrated with differential grouping (DG-MOEA/D). Initially, the algorithm employs the recursive spectral clustering differential grouping (RDGSC) technique to identify dependencies among variables, grouping them to reduce interactions between the variables. It then uses MOEA/D-UTEA to optimize each group, with an external archive for storing and updating solutions. Experimental results on the DTLZ and LSMOP test functions show that the DG-MOEA/D algorithm greatly outperforms the other seven comparison algorithms. When used in real-world scenarios like stock and bond asset allocation, the algorithm continues to outperform other methods, demonstrating its significant advantages in practical applications.