Neuro-evolutionary approach to stock market prediction

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn , Issue , 2007, Pages 2515-2520

  Mańdziuk, Jacek ^a   Jaruszewicz, Marcin ^b  

^a WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMMERCE; COMPUTER NETWORKS; DIESEL ENGINES; FORECASTING; KETONES;

INPUT VARIABLES;

NEURAL NETWORKS;

EID: 51749088785     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2007.4371354     Document Type: Conference Paper

References (19)

1. R. Gencay and M. Qi, "Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging," IEEE Transactions on Neural Networks, vol. 12, no. 4, pp. 726-734, 2001.
2. T. Chenoweth and Z. Obradović, "A multi-component nonlinear prediction system for the S&P 500 index," Neurocomputing, vol. 10, pp. 275-290, 1996.
3. J. Yao and C. Tan, "A case study on using neural networks to perform technical forecasting of FOREX," Neurocomputing, vol. 34, pp. 79-98, 2000.
4. M. Jaruszewicz and J. Mándziuk, "One day prediction of NIKKEI index considering information from other stock markets," L. Rutkowski et al. (Ed.), ICAISC, Lect. Notes in Art. Int., vol. 3070, pp. 1130-1135, 2004.
5. _, "Neuro-genetic system for DAX index prediction," A. Cared and L. Rutkowski and R. Tadeusiewicz and J. Zurada (Ed.), Artificial Intelligence and Soft Computing, pp. 42-49, 2006.
6. E. Saad, D. Prokhorov, and D. Wunsch, "Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks," IEEE Transactions on Neural Networks, vol. 9, no. 6, pp. 1456-1470, 1998.
7. R. Lee, "iJADE Stock Advisor: An intelligent agent based stock prediction system using hybrid RBF recurrent network," IEEE Transactions on Systems, vol. 34, no. 3, pp. 421-428, 2004.
8. P. Tino, C. Schittenkopf, and G. Dorffner, "Financial volatility trading using recurrent neural networks," IEEE Transactions on Neural Networks, vol. 12, no. 4, pp. 865-874, 2001.
9. L. Cao and F. Tay, "Support Vector Machine with adaptive parameters in financial time series forecasting," IEEE Transactions on Neural Networks, vol. 14, no. 6, pp. 1506-1518, 2003.
10. J. V. Hansen and R. D. Nelson, "Data mining of time series using stacked generalizers," Neurocomputing, vol. 43, pp. 173-184, 2002.
11. V. Kodogiannis and A. Lolis, "Forecasting financial time series using neural network and fuzzy system-based techniques," Neural Computing & Applications, vol. 11, pp. 90-102, 2002.
12. P. Lajbcygier, "Improving option pricing with the product constrained hybrid neural network," IEEE Transactions on Neural Networks, vol. 15, no. 2, pp. 465-476, 2004.
13. M. Dempster, T. Payne, Y. Romahi, and G. Thompson, "Computational learning techniques for intraday FX trading using popular technical indicators," IEEE Transactions on Neural Networks, vol. 12, no. 4, pp. 744-754, 2001.
14. B. LeBaron, "Empirical regularities from interacting long- and short-memory investors in an agent-based stock market," IEEE Transactions on Evolutionary Computation, vol. 5, no. 5, pp. 442-455, 2001.
15. K. Kim and W. Lee, "Stock market prediction using artificial neural networks with optimal feature transformation," Neural Computing & Applications, vol. 13, pp. 255-260, 2004.
16. F.-L. Chung, T.-C. Fu, V. Ng, and R. Luk, "An evolutionary approach to pattern-based time series segmentation," IEEE Transactions on Evolutionary Computation, vol. 8, no. 5, pp. 471-489, 2004.
17. S. Hayward, "The role of heterogeneous agents past and forward time horizons in formulating computational models," Computational Economics, vol. 25, pp. 25-40, 2005.
18. S. Thawornwong and D. Enke, "The adaptive selection of financial and economic variables for use with artificial neural networks," Neurocomputing, vol. 56, pp. 205-232, 2004.
19. J. Bródka, "Private communication," 2006.