Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

International Journal of Computational Intelligence Systems

Volumn 6, Issue 1, 2013, Pages 96-114

  Xiao, Y ^a,c   Xiao, Jin ^b,c   Lu, Fengbin ^c   Wang, Shouyang ^c  

^a CENTRAL CHINA NORMAL UNIVERSITY   (China)

^b Department of Management Science and System Science   (China)

^c CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

artificial neural networks; ensemble forecasting; genetic operator; particle swarm optimization; stock e exchange prices

Indexed keywords

ELMAN NETWORK; ENSEMBLE FORECASTING; FINANCIAL PROBLEMS; FORECASTING MODELS; GENERALIZED REGRESSION NEURAL NETWORKS; GENETIC OPERATORS; INDIVIDUAL MODELS; LINEAR COMBINATION MODEL; META MODEL; NON-LINEAR RELATIONSHIPS; NONLINEAR ENSEMBLE MODEL; PREDICTION PERFORMANCE; PRICES FORECASTING; STOCK E-EXCHANGE PRICES; STOCK INDICES; TRAINING SETS; WAVELET NEURAL NETWORKS;

COSTS; FORECASTING; PARTICLE SWARM OPTIMIZATION (PSO); SUPPORT VECTOR MACHINES;

NEURAL NETWORKS;

EID: 84872390465     PISSN: 18756891     EISSN: 18756883     Source Type: Journal    
DOI: 10.1080/18756891.2013.756227     Document Type: Article

References (44)

1. Xiao, Y., Xiao, J. and Wang, S. Y. 2012. A hybrid forecasting model for non-stationary time series: an application to container throughput prediction. International Journal of Knowledge and Systems Science., 3(2): 67-82.
2. Xiao, Y., Xiao, J. and Wang, S. Y. 2012. A hybrid model for time series forecasting. Human Systems Management., 31(2): 133-143.
3. Y. Xiao, J. Xiao, K. K. Lai and S. Y. Wang, A nonlinear neural networks ensemble model for nonstationary financial market trend mining, IEEE Transactions on Neural Networks & Learning Systems. 2012, Forthcoming.
4. Y. Xiao, M. Xiao and F. Z. Zhao, Improving financial returns using neural networks and adaptive particle swarm optimization, in: Proc.5th Int. Conf. Business Intelligence and Financial Engineering, Lanzhou, China, 2012, Forthcoming.
5. Y. Xiao, J. Xiao and S. Y. Wang, A multiscale modeling approach incorporating ARIMA and ANNs for financial market volatility forecasting, in: Proc. Int. Conf. Forecasting Economic and Financial Systems, Beijing, China, 2012, Forthcoming.
6. Brownstone, D. 1996. Using percentage accuracy to measure neural network predictions in Stock Market movements. Neurocomputing., 10: 237-250.
7. Quah, T. S. and Srinivasan, B. 1999. Improving returns on stock investment through neural network selection. Expert Systems with Applications., 17: 295-301.
8. Kuo, R. J., Chen, C. H. and Hwang, Y. C. 2001. An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets Systems., 118: 21-45.
9. Plikynas, D., Simanauskas, L. and Buda, S. 2002. Research of neural network methods for compound stock exchange indices analysis. Informatica-Lithuan., 13: 465-484.
10. Ao, S. I. 2003. Automating stock prediction with neural network and evolutionary computation. Lecture Notes in Computer Science., 2690: 203-210.
11. Slim, C. 2004. Forecasting the volatility of stock index returns: A Stochastic neural network approach. Lecture Notes in Computer Science., 3045: 935-944.
12. O'Connor, N. and Madden, M. G. 2006. A neural network approach to predicting stock exchange movements using external factors. Knowledge-Based Systems., 19: 371-378.
13. Thomaidis, N. S., Tzastoudis, V. S. and Dounias, G. D. 2007. A comparison of neural network model selection strategies for the pricing of S&P 500 stock index options. Int. J. on Artificial Intelligence Tools., 16: 1093-1113.
14. Liao, Z. and Wang, J. 2010. Forecasting model of global stock index by stochastic time effective neural network. Expert Systems with Applications., 37: 834-841.
15. Guresen, E., Kayakutlu, G. and Daim, T. U. 2011. Using artificial neural network models in stock market index prediction. Expert Systems with Applications., 38: 10389-10397.
16. Jasemi, M., Kimiagari, A. M. and Memariani, A. 2011. A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick. Expert Systems with Applications., 38: 3884-3890.
17. E. Grimaldi, A. F. Grimaccia, M. Mussetta and R.E. Zich, PSO as an effective learning algorithm for neural network applications, in: Proc.3rd Int. Conf. Computational Electromagnetics and Its Applications, Beijing, China, 2004, pp. 557-560.
18. Alfi, A. and Modares, H. 2011. System identification and control using adaptive particle swarm optimization. Applied Mathematical Modelling., 35: 1210-1221.
19. Wang, P. C. and Shoup, T. E. 2011. A poly-hybrid PSO optimization method with intelligent parameter adjustment. Advances in Engineering Software., 42: 555-565.
20. Abraham, A. and AuYeung, A. 2003. Integrating ensemble of intelligent systems for modeling stock indices. Lecture Notes in Computer Science., 2687: 774-781.
21. Kwon, Y. K. and Moon, B. R. 2004. Evolutionary ensemble for stock prediction. Lecture Notes in Computer Science., 3103: 1102-1113.
22. Chun, S. H. and Park, Y. J. 2005. Dynamic adaptive ensemble case-based reasoning: application to stock market prediction. Expert Systems with Applications., 28(3): 435-443.
23. Chen, Y. H., Yang, B. and Abraham, A. 2007. Flexible neural trees ensemble for stock index modeling. Neurocomputing., 70(4-6): 697-703.
24. Aladag, C. H., Egrioglu, E. and Yolcu, U. 2010. Forecast Combination by Using Artificial Neural Networks. Neural Processing Letters., 32(3): 269-276.
25. Hung, J. C. 2011. Applying a combined fuzzy systems and GARCH model to adaptively forecast stock market volatility. Applied Soft Computing., 11(5): 3938-3945.
26. Yao, J. T. and Tan, C. L. 2000. A case study on using neural networks to perform technical forecasting of forex. Neurocomputing., 34: 79-98.
27. Breiman, L. 1996. Bagging predictors. Machine Learning., 26: 123-140.
28. Raviv, Y. and Intrator, N. 1996. Bootstrapping with noise: an effective regularization technique. Connection Science., 8: 355-372.
29. McCulloch, W. S. and Pitts, W. 1943. A logical calculus of the ideas imminent in nervous activity. Bulletin and Mathematical Biophysics., 5: 115-133.
30. Elman, J. L. 1990. Finding Structure in Time. Cognitive Science., 14: 179-211.
31. Pham, D. T. and Liu, X. 1992. Dynamic system identification using partially recurrent neural networks. Journal of Systems Engineering., 2(2): 90-97.
32. Specht, D. F. 1991. A general regression neural network. IEEE Transactions on Neural Networks., 2: 568-576.
33. Parzen, E. 1962. On estimation of a probability density function and mode. Annals of Mathematical Statistics., 33: 1065-1076.
34. Daubechies, I. 1992. Ten Lectures on Wavelet, Philadelphia: SIAM.
35. Hornik, K., Stinchocombe, M. and White, H. 1989. Multilayer feedforward networks are universal approximators. Neural Networks., 2: 359-366.
36. Zhang, X., Qi, J.Zhang, R. 2001. Prediction of programmed-temperature retention values of naphthas by wavelet neural network. Journal of Computational Chemistry., 25: 125
37. Jiang, C. W. and Etorre, B. 2005. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization. Mathematics and Computers in Simulation., 68(1): 57-65.
38. Fan, S. F. and Zahara, E. 2007. Hybrid simplex search and particle swarm optimization for unconstratined optimization problems. European Journal of Operational Research., 181(2): 527-548.
39. Hamzacebi, C. and Kutay, F. 2007. Continuous functions minimization by dynamic random search technique. Applied Mathematical Modelling., 31(10): 189-198.
40. Ratnawecra, A. and Halgamuge, S. 2004. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. Evolutionary Computation., 8(3): 240-255.
41. Engelbrecht, AP. 2005. Fundamentals of computational swarm intelligence, Hoboken: Wiley.
42. Huang, X., Zhang, J. and Zhan, Z. H. 2009. Faster particle swarm optimization with random inertia weight. Computer Engineering and Design., 30(3): 647-650.
43. G. M. Chen, J. Y. Jia and Q. Han, Study on the strategy of decreasing inertia weight in particle swarm optimization algorithm, Journal of Xi'an Jiaotong University. 40 1 2006, 53-56.
44. Benediktsson, J. A., Sveinsson, J. R., Ersoy, O.K. and Swain, P.H. 1997. Parallel consensual neural networks. IEEE Transactions on Neural Networks., 8: 54-64.