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

References

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