Abstract
One of the central problems in financial markets is to make the profitable stocks trading decisions using historical stocks’ market data. This paper presents the decision-making method which is based on the application of neural networks (NN) and swarm intelligence technologies and is used to generate one-step ahead investment decisions. In brief, the analysis of historical stocks prices variations is made using “single layer” NN, and subsequently the Particle Swarm Optimization (PSO) algorithm is applied in order to select ”global best” NN for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. The experimental investigations were made considering different number of NN, moving time intervals and commission fees. The experimental results presented in the paper show that the application of our proposed method lets to achieve better results than the average of the market.
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