DECISION-MAKING MODEL FOR STOCK MARKETS BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM
The objective of this paper is to introduce the decision-making model for stock markets. The proposed model is based on the study of historic data and the application of Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) algorithm. In the proposed decision-making model the ANN are applied in order to make the analysis of historical daily stock returns and to calculate the recommendations concerning the purchase of the stocks. Subsequently, the application of PSO algorithm is made. The core idea of this algorithm application is to select the "global best" ANN for future investment decisions and to adapt the weights of other ANN towards the weights of the best network. The experimental investigation results presented in this paper show the potentiality of PSO algorithm applications for the decision-making in the stock markets.
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