Evolutionary models in cash management policies with multiple assets

Abstract

This work aimed to apply genetic algorithms (GA) and particle swarm optimization (PSO) in cash balance management using multiple asset investments. This problem consists of a stochastic model that does not define a single ideal point for cash balance, but an oscillation range between a lower bound, an ideal balance and an upper bound. Thus, this paper proposes the application of GA and PSO to minimize the total cost of cash maintenance, by obtaining the parameters of a cash management policy with three assets (cash and two investments), and using the assumptions presented in literature. Computational experiments were applied in the development and validation of the models. The results indicated that both the GA and PSO are applicable in determining the cash management policy, but with better results for the PSO model.