Optimal computing budget allocation to the differential evolution algorithm for large-scale portfolio optimization

Abstract

Differential evolution (DE) is one of the popular techniques in large-scale portfolio optimization, which is noticed for its applications in the problems that are non-convex, non-continuous, non-differentiable, and so on. This technique suffers specific short-comings, for example, unstable convergence in the final solution, trapped in local optimum, and demand for high number of replications. Optimal Computing Budget Allocation (OCBA) technique gives an efficient way to reach the global optimum by optimally assigning computing resource among designs. The integration of DE and OCBA gives better performance than DE alone in terms of convergence rate and the attained global optimum. The ordering of the integration also plays a vital role, that is, the strategy of first applying DE before OCBA outperforms the reversely ordered one. Both integration strategies are essentially the improved DE algorithms for large-scale portfolio optimization. In addition to numerical tests, empirical analysis of 100 stocks in S&P500 over a 10-year period confirms the conclusions.