Adapting particle swarm optimization to stock markets

The paper is focused on the development of intelligent decision-making model which is based on the application of artificial neural networks (ANN) and swarm intelligence algorithm. The proposed model generates one-step forward investment decisions. The ANN are used to make the analysis of historical stock returns and to calculate one day forward possible profit, which could be get while following the model proposed decisions concerning the purchase of the stocks. Subsequently the particle swarm optimization (PSO) algorithm is applied for training of ANN. The training of ANN is made through the adjustment of all ANN towards the weights of "global best" ANN. The experimental investigations were made considering different forms of decision-making model: different structure ANN, input variables etc. The paper introduces experimental investigation for the evaluation of decision-making model. The experimental results show that the application of the proposed decision-making model lets to achieve better results than the average of the market.