Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30526
A New Hybrid Model with Passive Congregation for Stock Market Indices Prediction

Authors: Tarek Aboueldahab

Abstract:

In this paper, we propose a new hybrid learning model for stock market indices prediction by adding a passive congregation term to the standard hybrid model comprising Particle Swarm Optimization (PSO) with Genetic Algorithm (GA) operators in training Neural Networks (NN). This new passive congregation term is based on the cooperation between different particles in determining new positions rather than depending on the particles selfish thinking without considering other particles positions, thus it enables PSO to perform both the local and global search instead of only doing the local search. Experiment study carried out on the most famous European stock market indices in both long term and short term prediction shows significantly the influence of the passive congregation term in improving the prediction accuracy compared to standard hybrid model.

Keywords: Hybrid Model, stock market prediction, Global Search, Passive Congregation

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082543

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1102

References:


[1] 0. S.I. Wu, and H. Zheng, " Modeling Stock Market using Neural Networks," Proceedings of Software Engineering and Applications, ACTA Press, USA, 2003.
[2] K. Kim, and W. Lee, "Stock Market Prediction using ANN with Optimal Feature Transformation," Neural Computing & Applications, Vol. 13, No.3, pp.225-260, 2004.
[3] A.U. Khan, T. K. Bandopadhyaya, S. Sharma. "Genetic Algorithm Based Backpropagation Neural Network Performs better than Backpropagation Neural Network in Stock Rates Prediction." IJCSNS International Journal of Computer Science and Network Security, Vol.8, No. 7, July 2008.
[4] H. X. Chen, S. M. Yuan„ and K. Jiang, "Selective neural network ensemble based on clustering," Lecture Notes in Computer Science, Springer Verlag, Heidelberg, Vol. 3971, pp. 545-550, 2006.
[5] X. Zhang, Y. Chen, J. Y. Yang, "Stock Index Forecasting Using PSO Bases Selective Neural Network Ensemble." International Conference on Artificial Intelligence (ICAI07), pp. 260-264. 2007.
[6] R. Poli, C. D. Chio, and W. B. Langdon. "Exploring extended particle swarms: a genetic programming approach". Genetic And Evolutionary Computation Conference (GECCO'05), pp. 169-176, 2005.
[7] M. Settles and T. Soule, "Breeding Swarm: A GA/PSO hybrid" In Genetic and Evolutionary Computation Conference (GECCO). pp. 161¬168. Washington, USA 2005.
[8] Y. Chen, B. Yang and A. Abraham„ "Flexible neural trees ensemble for stock index modeling" Neurocomputing, 70, pp. 697-703,2007.
[9] J.A. Vrugt, B.A. Robinson, and J.M. Hyman, "Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Space"IEEE Transactions on Evolutionary Computation, Vol. 13, No. 2, pp. 243-259, April 2009.
[10] K. Premalatha and A. Natarajan, "Hybrid PSO and GA for Global Maximization" International Journal Open Problems Compt. Math., Vol. 2, No. 4,pp. 597 — 608, 2009.
[11] T. Aboueldahab, and M. Fakhreldin,"Identification and Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network" Intelligent Control and Automation, Vol. 2, No. 3, pp. 57-62, August 2011
[12] T. Aboueldahab, and M. Fakhreldin, "Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network." IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4341-4345, Istanbul. 2010.
[13] J. Kennedy, and R.C. Eberhart, "Particle swarm optimization." Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp.1942-1948, 1995.
[14] C.F. Juang, "A Hybrid of Genetic Algorithm and Particle Swarm Optimization for RecurrentNetwork Design." IEEETransactions on systems, Man, Cybernetics- Part B: Vol. 34, No. 2, 2004.
[15] A. A. E. Ahmed, L. T. Germano, and Z. C. Antonio, "A Hybrid Particle Swarm Optimization Applied to Loss Power Minimization." IEEE Transactions on Power Systems, Vol. 20, No. 2, pp. 859-866, May 2005.
[16] J. K. Parrish and W. M. Hamner, eds., Animal Groups in Three Dimensions, Cambridge University Press, Cambridge, UK, 1997
[17] http://in.finance.yahoo.com/