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.

[1]  S. M. Lo,et al.  Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong , 2003, Neurocomputing.

[2]  M. Hashem Pesaran,et al.  Forecasting stock returns an examination of stock market trading in the presence of transaction costs , 1994 .

[3]  Milton S. Boyd,et al.  Designing a neural network for forecasting financial and economic time series , 1996, Neurocomputing.

[4]  Chin Kuo,et al.  Neural Networks vs. Conventional Methods of Forecasting , 1996 .

[5]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[6]  Rimvydas Simutis,et al.  Stocks' Trading System Based on the Particle Swarm Optimization Algorithm , 2004, International Conference on Computational Science.

[7]  Thomas Hellström,et al.  Techniques and Software for Development and Evaluation of Trading Strategies , 2001 .

[8]  Dimitris K. Tasoulis,et al.  Financial forecasting through unsupervised clustering and evolutionary trained neural networks , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[9]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Andrew W. Lo,et al.  Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation , 2000 .

[11]  C. Tan,et al.  NEURAL NETWORKS FOR TECHNICAL ANALYSIS: A STUDY ON KLCI , 1999 .

[12]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[13]  Sam Mirmirani,et al.  Gold Price, Neural Networks and Genetic Algorithm , 2004 .