An approach for stock buying with evolutionary optimization algorithms

Providing stock buying with more profit for buyer is challenging procedure in buying operation. In other words, buyers expect more profit with less cost. Many influence parameters cause this procedure being attractive. One of them is liquidity. There are some measurements for liquidity. With complete and detailed model and with detailed variables and in the static environment (with no changing in conditions), we can use common approach for optimization to buy proper stocks, but in a dynamic environment (which many of circumstances are changed) with incomplete models and with noisy variables (probably), common approaches cannot satisfy all requirements. In spite of common approaches for optimization, we can use Evolutionary Optimization Algorithms. In this paper, three evolutionary optimization algorithms (Particle Swarm Optimization, The Wale Optimization algorithm and the Worm Optimization algorithm), in multi-objective mode, are used to buy the stocks of three Iranian banks and then benefits and weakness of evolutionary algorithms are compared.